From b4882e629c51b79dbcd9db89f85df4c923bdb86b Mon Sep 17 00:00:00 2001
From: kcz358 <kaichenzhang358@outlook.com>
Date: Thu, 13 Jun 2024 02:33:08 +0000
Subject: [PATCH 01/32] Allow one vision model to choose video decode backend
 and seperate the import for llava and llavavid

---
 lmms_eval/models/llava_onevision.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/lmms_eval/models/llava_onevision.py b/lmms_eval/models/llava_onevision.py
index 7acd66df..42cbd151 100755
--- a/lmms_eval/models/llava_onevision.py
+++ b/lmms_eval/models/llava_onevision.py
@@ -27,6 +27,7 @@
 from lmms_eval.api.instance import Instance
 from lmms_eval.api.model import lmms
 from lmms_eval.api.registry import register_model
+from lmms_eval.models.model_utils.load_video import read_video_pyav
 
 try:
     from llava.model.builder import load_pretrained_model
@@ -34,15 +35,17 @@
     from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
     from llava.conversation import conv_templates, SeparatorStyle
 
+except Exception as e:
+    eval_logger.debug("LLaVA is not installed. Please install LLaVA to use this model.\nError: %s" % e)
+
+try:
     from llavavid.model.language_model.llava_qwen import LlavaQwenConfig
     from llavavid.model.language_model.llava_llama import LlavaConfig
 
     AutoConfig.register("llava_qwen", LlavaQwenConfig)
     AutoConfig.register("llava_llama", LlavaConfig)
-
 except Exception as e:
-    eval_logger.debug("LLaVA is not installed. Please install LLaVA to use this model.\nError: %s" % e)
-
+    eval_logger.debug("")
 # inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5
 # if is_flash_attn_2_available:
 #     best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating
@@ -76,6 +79,7 @@ def __init__(
         mm_spatial_pool_stride: Optional[int] = 2,
         mm_spatial_pool_mode: Optional[str] = "average",
         token_strategy: Optional[str] = "single",  # could be "single" or "multiple", "multiple" denotes adding multiple <image> tokens for each frame
+        video_decode_backend: str = "pyav",
         **kwargs,
     ) -> None:
         super().__init__()
@@ -110,6 +114,7 @@ def __init__(
         self.max_frames_num = max_frames_num
         self.mm_spatial_pool_stride = mm_spatial_pool_stride
         self.mm_spatial_pool_mode = mm_spatial_pool_mode
+        self.video_decode_backend = video_decode_backend
 
         overwrite_config = {}
         overwrite_config["mm_spatial_pool_stride"] = self.mm_spatial_pool_stride
@@ -388,7 +393,10 @@ def _collate(x):
                 elif type(visual[0]) == str:  # For video task
                     image_tensor = []
                     try:
-                        frames = self.load_video(visual, self.max_frames_num)
+                        if self.video_decode_backend == "decord":
+                            frames = self.load_video(visual, self.max_frames_num)
+                        elif self.video_decode_backend == "pyav":
+                            frames = read_video_pyav(visual, num_frm=self.max_frames_num)
                         frames = self._image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].half().cuda()
                         image_tensor.append(frames)
                     except Exception as e:

From db3c403c9ba7715d381f8aa4e1b8a6a5553136c1 Mon Sep 17 00:00:00 2001
From: kcz358 <kaichenzhang358@outlook.com>
Date: Fri, 14 Jun 2024 04:21:29 +0000
Subject: [PATCH 02/32] Fix llava onevision pyav load issue

---
 lmms_eval/models/llava_onevision.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/lmms_eval/models/llava_onevision.py b/lmms_eval/models/llava_onevision.py
index 42cbd151..bc60855b 100755
--- a/lmms_eval/models/llava_onevision.py
+++ b/lmms_eval/models/llava_onevision.py
@@ -396,7 +396,7 @@ def _collate(x):
                         if self.video_decode_backend == "decord":
                             frames = self.load_video(visual, self.max_frames_num)
                         elif self.video_decode_backend == "pyav":
-                            frames = read_video_pyav(visual, num_frm=self.max_frames_num)
+                            frames = read_video_pyav(visual[0], num_frm=self.max_frames_num)
                         frames = self._image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].half().cuda()
                         image_tensor.append(frames)
                     except Exception as e:

From f4eeaa98e8e1b19ff31ccc63c61b59d4fb5b40cb Mon Sep 17 00:00:00 2001
From: kcz358 <kaichenzhang358@outlook.com>
Date: Fri, 14 Jun 2024 07:40:48 +0000
Subject: [PATCH 03/32] Fix image aspect ration in gen kwargs for llava one
 vision

---
 lmms_eval/models/llava_onevision.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/lmms_eval/models/llava_onevision.py b/lmms_eval/models/llava_onevision.py
index bc60855b..a755b0c9 100755
--- a/lmms_eval/models/llava_onevision.py
+++ b/lmms_eval/models/llava_onevision.py
@@ -463,6 +463,8 @@ def _collate(x):
 
             # These steps are not in LLaVA's original code, but are necessary for generation to work
             # TODO: attention to this major generation step...
+            if "image_aspect_ratio" in gen_kwargs.keys():
+                gen_kwargs.pop("image_aspect_ratio") 
             try:
                 with torch.inference_mode():
                     cont = self.model.generate(input_ids, attention_mask=attention_masks, pad_token_id=pad_token_ids, images=image_tensor, use_cache=self.use_cache, **gen_kwargs)

From c58271a5fdcb61c12468a995555d0e40d362586f Mon Sep 17 00:00:00 2001
From: choiszt <ls2001927@gmail.com>
Date: Mon, 17 Jun 2024 02:05:40 +0800
Subject: [PATCH 04/32] update videomme_subtitle and modified prompt for
 ablations

---
 lmms_eval/tasks/videomme/utils.py             | 31 ++++++++++++-
 .../tasks/videomme/videomme_subtitle.yaml     | 43 +++++++++++++++++++
 2 files changed, 72 insertions(+), 2 deletions(-)
 create mode 100644 lmms_eval/tasks/videomme/videomme_subtitle.yaml

diff --git a/lmms_eval/tasks/videomme/utils.py b/lmms_eval/tasks/videomme/utils.py
index 3631f87c..4cd26715 100755
--- a/lmms_eval/tasks/videomme/utils.py
+++ b/lmms_eval/tasks/videomme/utils.py
@@ -106,10 +106,37 @@ def videomme_doc_to_visual(doc):
 
 
 def videomme_doc_to_text(doc, model_specific_prompt_kwargs=None):
+
+    option_prompt="Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option."
+    question = doc["question"]
+    option = str(doc["options"])
+    question = question + "\n" + option
+    full_prompt=option_prompt+"\n"+question+"\n"+"The best answer is:"
+    return full_prompt
+# Frames + Subs
+# This video's subtitles are listed below: 
+# 【subtitles】
+
+# Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option.
+# 【question】
+# The best answer is:
+# Frames / Frames + Audio
+# Select the best answer to the following multiple-choice question based on the video. Respond with only the letter (A, B, C, or D) of the correct option.
+# 【question】
+# The best answer is:
+
+def videomme_doc_to_text_subtitle(doc, model_specific_prompt_kwargs=None):
+    subtitles_prompt="This video's subtitles are listed below: \n"
+    if doc["Subtitle"]=="":
+        subtitle="No subtitles available"
+    else:
+        subtitle=doc["Subtitle"]
+    option_prompt="Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option."
     question = doc["question"]
     option = str(doc["options"])
-    question = question + "\n" + option + model_specific_prompt_kwargs["post_prompt"]
-    return question
+    question = question + "\n" + option
+    full_prompt=subtitles_prompt+subtitle+"\n"+option_prompt+"\n"+question+"\n"+"The best answer is:"
+    return full_prompt
 
 
 def extract_characters_regex(s):
diff --git a/lmms_eval/tasks/videomme/videomme_subtitle.yaml b/lmms_eval/tasks/videomme/videomme_subtitle.yaml
new file mode 100644
index 00000000..04125766
--- /dev/null
+++ b/lmms_eval/tasks/videomme/videomme_subtitle.yaml
@@ -0,0 +1,43 @@
+dataset_path: lmms-lab/Video-MME
+dataset_kwargs:
+  token: True
+  cache_dir: videomme
+  video: True
+  # From_YouTube: True
+task: videomme_subtitle
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.videomme_doc_to_visual
+doc_to_text: !function utils.videomme_doc_to_text_subtitle
+doc_to_target: "answer"
+generation_kwargs:
+  max_new_tokens: 16
+  temperature: 0
+  top_p: 1.0
+  num_beams: 1
+  do_sample: false
+# The return value of process_results will be used by metrics
+process_results: !function utils.videomme_process_results
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: videomme_percetion_score
+    aggregation: !function utils.videomme_aggregate_results
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: ""
+  # gpt4v:
+  #   pre_prompt: ""
+  #   post_prompt: 
+  # # qwen_vl:  
+  # #   pre_prompt: ""
+  # #   post_prompt: " Answer:"
+  # # otterhd:
+  # #   pre_prompt: ""
+  # #   post_prompt: " Answer:"
+  # xcomposer2_4khd:
+  #   pre_prompt: "[UNUSED_TOKEN_146]user\n"
+  #   post_prompt: " Answer this question with A, B, C, or D.[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
+metadata:
+  - version: 0.0

From 59cce7341dce166c3ab5154beeb133552c80b04d Mon Sep 17 00:00:00 2001
From: Bo Li <drluodian@gmail.com>
Date: Tue, 18 Jun 2024 16:50:19 +0000
Subject: [PATCH 05/32] Squashed commit of the following:

commit 050b2c370017e9b97475dd6cf01fd051b5ca5c86
Merge: 74facb41 ef306512
Author: Li Bo <drluodian@gmail.com>
Date:   Tue Jun 18 13:13:38 2024 +0800

    Merge pull request #114 from zjysteven/add-tinyllava

    add tinyllava

commit ef306512e5135f76dffa383f600b8733015836e8
Author: Jingyang Zhang <jingyang.zhang@duke.edu>
Date:   Mon Jun 17 17:57:02 2024 -0400

    fix typo

commit 9bab67732a4238097725deddf867fb1946ffee40
Merge: dbfb2387 74facb41
Author: Jingyang Zhang <jingyang.zhang@duke.edu>
Date:   Sun Jun 16 10:56:05 2024 -0400

    Merge branch 'EvolvingLMMs-Lab:main' into add-tinyllava

commit 74facb41a826691dfce4458cf1d8659b34fc5bf5
Merge: 8ba192f9 d5df72de
Author: Li Bo <drluodian@gmail.com>
Date:   Sun Jun 16 17:59:19 2024 +0800

    Merge pull request #118 from teowu/main

    Fix the potential risk by PR #117

commit d5df72de2d03108d6b365818ecc3551ac9aa6302
Merge: 5bf59ed2 8ba192f9
Author: Teo (Timothy) Wu Haoning <38696372+teowu@users.noreply.github.com>
Date:   Sun Jun 16 15:32:13 2024 +0800

    Merge branch 'EvolvingLMMs-Lab:main' into main

commit 5bf59ed250da98a408a94e214a73caa400cba842
Author: teowu <realtimothyhwu@gmail.com>
Date:   Sun Jun 16 07:27:28 2024 +0000

    fix #117, allow auto download with tar format videos

commit 98b3955cb808e36303c030aea78eb037d1ec59ce
Merge: a056f118 be9dada8
Author: teowu <realtimothyhwu@gmail.com>
Date:   Sun Jun 16 07:25:07 2024 +0000

    Merge branch 'main' of https://github.com/teowu/lmms-eval into main

commit a056f118704eccec86ce32ab86981ce4bc1e1deb
Author: teowu <realtimothyhwu@gmail.com>
Date:   Sun Jun 16 07:23:54 2024 +0000

    fix #117, allow auto download with tar format videos

commit 8ba192f94edf5d99598983445d5faa4f8807c49f
Merge: 7cc28907 be9dada8
Author: Li Bo <drluodian@gmail.com>
Date:   Sat Jun 15 17:30:59 2024 +0800

    Merge pull request #117 from teowu/main

    LongVideoBench for LMMs-Eval

commit be9dada8b4189c53c08e1674ab273242cf2f80a0
Merge: 62ea8ceb 7cc28907
Author: Teo (Timothy) Wu Haoning <38696372+teowu@users.noreply.github.com>
Date:   Sat Jun 15 16:39:20 2024 +0800

    Merge pull request #1 from EvolvingLMMs-Lab/main

    Merge pull request #113 from teowu/main

commit 62ea8ceb223ef2b51ebab2bcd50d5cf339c35cfe
Author: teowu <realtimothyhwu@gmail.com>
Date:   Sat Jun 15 08:30:11 2024 +0000

    LongVideoBench support: image LMMs (idefics2, phi3) and video LMMs (LLaVA-Next-Video-34B)

commit 7cc28907edbb4eb58ee1398772a48110ea35dd96
Merge: 4bc7224d ea14cd4b
Author: Li Bo <drluodian@gmail.com>
Date:   Sat Jun 15 14:10:22 2024 +0800

    Merge pull request #113 from teowu/main

    Q-Bench, Q-Bench2, A-Bench

commit dbfb23873979f789477f4797ee2d6071e0fd921e
Author: Jingyang <jingyang.zhang@duke.edu>
Date:   Fri Jun 14 16:20:42 2024 -0400

    add tinyllava

commit ea14cd4b361f4c95b3665cbdb95bc51754090eb5
Author: teowu <realtimothyhwu@gmail.com>
Date:   Fri Jun 14 15:01:52 2024 +0000

    Add qbench, qbench2, abench; fix phi3v as its current implementation does not support multi-image

commit 4bc7224dcd27fe8b288bfc3fed4d7a9da9635658
Merge: 2797987f bf14cb85
Author: Li Bo <drluodian@gmail.com>
Date:   Fri Jun 14 02:14:43 2024 +0800

    Merge pull request #111 from XinrunDu/main

    add II-Bench

commit bf14cb8527b2b7ac438a36567a875168bc02d294
Author: XinrunDu <duxinrun2000@gmail.com>
Date:   Thu Jun 13 09:37:02 2024 +0000

    fix dataset_path

commit 6248113f4e11a0ac396d31fa1b032a142fea8cb4
Author: XinrunDu <duxinrun2000@gmail.com>
Date:   Thu Jun 13 09:32:06 2024 +0000

    add II-Bench

commit 2797987f5b88b87bd172714b678a75a1d8051826
Merge: 63d82f1f 66d4bb2d
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 11:14:47 2024 +0800

    Merge pull request #109 from EvolvingLMMs-Lab/pufanyi/update_version

    [Small Update] Update the version of LMMs-Eval

commit 66d4bb2d9c9afbbdea40196d4ad80e214d0b14b6
Author: Fanyi Pu <FPU001@e.ntu.edu.sg>
Date:   Thu Jun 13 11:13:00 2024 +0800

    update version

commit 63d82f1ff11eb430d91a15d6788a1f0b4d596850
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 11:04:32 2024 +0800

    Update README.md

commit 44a33799671cb668f55366d5e5a4ddb051a3a1b4
Merge: 5ed00356 0ce46d08
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 04:00:12 2024 +0800

    Merge pull request #105 from tianyu-z/main

    Include VCR

commit 0ce46d088e473d12d63de44f17c67dceab25658c
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Wed Jun 12 15:56:34 2024 -0400

    update README.md

commit 46a88d8b0199ed44d2ff459fb372f2e006960cea
Merge: 47b13b9b 5ed00356
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Wed Jun 12 15:50:26 2024 -0400

    merged readme.md

commit 47b13b9b320d36ac53b3622557e31239f7c22621
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Wed Jun 12 15:30:52 2024 -0400

    update aggregation function for vcr_wiki

commit 5ed00356676cf5d0ff056cf27d1b519b8e303ff7
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 03:21:42 2024 +0800

    Update README.md

commit ed8806839db5988ced672bd162b7b046edb4863a
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 03:13:59 2024 +0800

    Update README.md

commit fea3806026932a6e2bd6e538bcc413e33abdf245
Merge: d99a24ab 05dc8e85
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 03:11:49 2024 +0800

    Merge pull request #108 from EvolvingLMMs-Lab/internal_main_dev

    [Upgrade to v0.2] Embracing Video Evaluations with LMMs-Eval

commit 05dc8e853eab7c6bc782a1e2662d2efe7422f767
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:56:04 2024 +0000

    chore: Update lmms-eval to support video evaluations for LLaVA models

commit cbeee20bc4ffb510a2b23d96cdaf4077be7c2a9e
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:50:30 2024 +0000

    chore: Update lmms-eval to support video evaluations for LLaVA models

commit f00d5498b69dd4f7e54c907ac906abc7c128f000
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:46:33 2024 +0000

    Update image alignment in README.md

commit 34156335db74cef9e3f0915d7172fd6b22456c15
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:43:16 2024 +0000

    Update llava conv_template in lmms_eval/models/llava.py

commit 50575a950736bc8fc1e191310314cbb5fdff5720
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:39:03 2024 +0000

    chore: Update lmms-eval to support video evaluations for LLaVA models

commit c9b2252fb8a15dd04252af5e6b4613855afd6ada
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:33:48 2024 +0000

    Bump version to 0.2.0.dev0

commit 465bd4205e8097e9c037b24a3ed08dd6a7694efa
Merge: e43bd840 d99a24ab
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:04:25 2024 +0000

    Merge branch 'main' of https://github.com/EvolvingLMMs-Lab/lmms-eval into internal_main_dev

commit e43bd840b63eb499856e36d9d2ba45c924abcead
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 14:54:06 2024 +0000

    chore: Remove unnecessary files and code related to live_bench and sft_eval tasks

commit d99a24abd06df10d07e5a4d0ad5030613f92f2e7
Merge: 374590be a66003be
Author: Li Bo <drluodian@gmail.com>
Date:   Wed Jun 12 19:45:57 2024 +0800

    Merge pull request #107 from AtsuMiyai/new_task/upd_update

    update gpt-3.5-turbo version

commit a66003befe4175824a1be6ed59f5f5b88c15f792
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed Jun 12 17:05:17 2024 +0900

    update gpt-3.5-turbo version

commit ee91f272985f32eeb9cd6faa41afdd8eb49cac30
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed Jun 12 16:50:53 2024 +0900

    update gpt-3.5-turbo version

commit 326b9694fc77398592b8caf3ba0bc2e2bb903813
Author: tianyu-z <zhangtianyupro@gmail.com>
Date:   Mon Jun 10 20:07:40 2024 -0400

    include std and confidence interval

commit cd050d4a721d01a2ace0cd030cf7f8dc67eb8c4d
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Mon Jun 10 18:49:47 2024 -0400

    update vcr_wiki tasks in README.md

commit 205721e0aad76dde30255e56149bbed121883356
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Mon Jun 10 18:43:15 2024 -0400

    update vcr_wiki tasks

commit db8e718b502469e8536ee359c5559de87635ffc7
Author: tianyu-z <zhangtianyupro@gmail.com>
Date:   Mon Jun 10 16:13:58 2024 -0400

    include the try-except logic for spacy

commit 427dabb790118f538b64e4e5bf6a7aab9689b3d9
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Mon Jun 10 15:51:05 2024 -0400

    add crossed_text to vcr_wiki output

commit 043b483eb55f7be4fea75c9bc0b9b03d251b109b
Author: tianyu-z <zhangtianyupro@gmail.com>
Date:   Mon Jun 10 15:47:00 2024 -0400

    switch logic

commit e1f04db8f58dd10591fde335ea13f74cda7c79bd
Author: tianyu-z <zhangtianyupro@gmail.com>
Date:   Mon Jun 10 02:38:21 2024 -0400

    modify the form of VCR

commit 96e8d9867c9549ab7490f4b12cfeb6a06238e0aa
Author: tianyu-z <zhangtianyupro@gmail.com>
Date:   Mon Jun 10 00:10:30 2024 -0400

    init include vcr

commit 374590be62f988a76cf6704cfe394cd8ae7d4cb6
Merge: 504685e2 cb3b9ce7
Author: Kaichen Zhang - NTU <kaichenzhang358@outlook.com>
Date:   Fri Jun 7 20:25:48 2024 +0800

    Merge pull request #101 from Gumpest/main

    Update conbench in README

commit 504685e20b17659b913cf46f3012c16bf429e09d
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 6 15:42:15 2024 +0800

    Update README.md

commit cb3b9ce71411da862ff01342a9122a3c656ffbd1
Merge: c9793b38 67b64ea4
Author: Yuan Zhang <56063339+Gumpest@users.noreply.github.com>
Date:   Thu Jun 6 11:22:24 2024 +0800

    Merge branch 'EvolvingLMMs-Lab:main' into main

commit c9793b3883714f254a700230b7bee781d6110e73
Author: Yuan Zhang <gump_well_done@163.com>
Date:   Thu Jun 6 11:21:05 2024 +0800

    update README

commit 67b64ea44a5a39d96c7a196a8a8345a7486bd912
Merge: 8ee7848a 5fd68451
Author: Li Bo <drluodian@gmail.com>
Date:   Wed Jun 5 23:12:58 2024 +0800

    Merge pull request #100 from Gumpest/main

    add Conbench

commit 5fd684515c55ef643726c1b6c720c7cbd2183ba1
Author: Yuan Zhang <gump_well_done@163.com>
Date:   Wed Jun 5 21:52:31 2024 +0800

    add conbench

commit 8ee7848aaa6383aa1f919c3f21199c81db3fff89
Merge: 747e1978 6fefaf7c
Author: Li Bo <drluodian@gmail.com>
Date:   Tue Jun 4 17:09:33 2024 +0800

    Merge pull request #95 from AtsuMiyai/new_task/upd

    add MM-UPD

commit 747e19782996065cdce7157ee8c5e15beb5b6c59
Merge: 4854a34d 05843072
Author: Li Bo <drluodian@gmail.com>
Date:   Tue Jun 4 17:09:04 2024 +0800

    Merge pull request #97 from CaraJ7/update

    Add MathVerse in README.md

commit 6fefaf7cea504e35583ee7217449da290295a7a4
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Tue Jun 4 17:36:39 2024 +0900

    update utils.py for leaderboard submission

commit 5f4fe360def1c48ea0cb1da6409d192784882308
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Sun Jun 2 23:28:27 2024 +0900

    slightly change query_prompt for the reproduction

commit 05843072d608b970bcada1cd0db65a3c80864060
Author: CaraJ7 <1350074492@qq.com>
Date:   Sun Jun 2 17:05:28 2024 +0800

    Add MathVerse in README.md

commit 0581ab3cfb362e2024988b46fbbb00324f1233c9
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Fri May 31 16:09:45 2024 +0900

    merge model_specific_prompt_kwargs and dataset_name into each task yaml

commit 4854a34d4d37efb5e201f2691ecdb054590cf20b
Author: Pu Fanyi <FPU001@e.ntu.edu.sg>
Date:   Sat May 4 19:23:39 2024 +0800

    Group MMMU images into one image (#83)

    * update

    * update font

    * Add matplotlib.font_manager import in utils.py

    * Refactor font handling in add_order_label function in utils.py

    * group mmmu

    ---------

    Co-authored-by: Li Bo <drluodian@gmail.com>

commit d224794c49520f4d28a31862cf977198cd6cbc5e
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 15:15:59 2024 +0900

    add upd

commit 453e7936424220f02b99517059ca71babfbe5f5a
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 15:03:30 2024 +0900

    add upd

commit 909edd6769ddcf8a546be4fdd129416687516878
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 12:52:21 2024 +0900

    add upd

commit 7c1ac9706cafc4801fa4da181d2f610b7838c7b8
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 12:50:32 2024 +0900

    add upd

commit 811301c5280ddd74986645086f026ab730c8848c
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 12:46:58 2024 +0900

    add upd

commit 71401bafd1d515f704f86ab4817a758542bc4672
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 12:41:21 2024 +0900

    add upd

commit 24dc435908d921e9f1a5706e3141b12e5d838d18
Author: Bo Li <drluodian@gmail.com>
Date:   Mon May 27 10:17:32 2024 +0000

    fix compatibility issue of older version llava

commit 616edf43731415b35f0f5e97748ed2e017a2891d
Author: Bo Li <drluodian@gmail.com>
Date:   Mon May 27 09:32:26 2024 +0000

    [Fix] import issues of multilingual llava and olympiadbench

commit 4c5a99e21a63fb0ee1c7d15546d18066e1d9894b
Merge: 45c05b2b b05c3e22
Author: Li Bo <drluodian@gmail.com>
Date:   Mon May 27 14:19:53 2024 +0800

    Merge pull request #87 from vfragoso/vifragos/phi3v

    Adding microsoft/Phi-3-vision-128k-instruct model.

commit b05c3e222fabd308dd7af4e04c1c6a0812962fe6
Author: Victor Fragoso <victor.fragoso@microsoft.com>
Date:   Fri May 24 16:36:37 2024 +0000

    Adding documentation of Phi3v class.

commit c2008971308ce8168d57c24d00b725832f099244
Author: Victor Fragoso <victor.fragoso@microsoft.com>
Date:   Fri May 24 16:25:02 2024 +0000

    Adding prompt arguments for Phi3v on MathVista-TestMini

commit 7f9fb6bcc6cd24a7b8011b8753d0ea98cc2451fd
Author: Victor Fragoso <victor.fragoso@microsoft.com>
Date:   Fri May 24 13:24:16 2024 +0000

    Adding Phi3v model.

commit 45c05b2b2bece76e06849a52a0d034f9c0ac2367
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 23 03:47:36 2024 +0000

    Set printing info for llava_hf to debug level

commit 53f013ed8278776551ca992562253387cc9968d2
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 23 03:41:39 2024 +0000

    Fix pope random name in pope full

commit 22520a95f13334b75eee0cf0387151067a6bf516
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 23 03:41:14 2024 +0000

    Add separated pope tasks by category

commit d1eefb1565014b47287ffa6b350229062f8f602f
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 9 08:36:02 2024 +0000

    Update gitignore

commit b2b4dbd2dc13432c79208db35abf7f55c97f1790
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Mon May 20 07:45:11 2024 +0000

    Comment out Spice in caption task so that don't need to download stanford nlp model

commit 662f05ce4c62a46a83f819d3a5925a9bd20059b5
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Mon May 20 03:13:13 2024 +0000

    Comment out parse result in xcomposer

commit 09329322916bfbb604d72ddaf50441a0947f8805
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 16 03:55:39 2024 +0000

    Fix instructblip qformer size mismatch and multi-images problem

commit 557a6a3b15e07e506bc05e2cc76ff6a2f8c93964
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 16 03:11:41 2024 +0000

    Remove redundant code in fuyu

commit 6aeb5504e74ed1980b53700d8e4d4dcf7d1b38fc
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 16 01:45:24 2024 +0000

    Fix idefics2 llava in the wild bugs

commit aea80e6a71f716951353e1e5d68380243396b4d6
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Wed May 15 11:07:35 2024 +0000

    Better task list_with_num

commit 3c12a080d66b9c38f615b961befca7c30f82fa39
Author: Li Bo <drluodian@gmail.com>
Date:   Sat May 18 02:35:52 2024 +0800

    Update LICENSE

commit 82317a635a4978b32e095a06cc295d0ae23661c2
Author: Li Bo <drluodian@gmail.com>
Date:   Sat May 18 02:29:09 2024 +0800

    Update LICENSE

commit a8bba1cdb51061a0d27bf9a98cca1505b5c58ea5
Author: Li Bo <drluodian@gmail.com>
Date:   Sat May 18 02:28:03 2024 +0800

    Create LICENSE

commit caa5893b5fd2c1d32c72b97f371ccd9a8d9ec3a0
Merge: c0944486 423b0060
Author: Li Bo <drluodian@gmail.com>
Date:   Mon May 13 11:45:26 2024 +0800

    Merge pull request #73 from EvolvingLMMs-Lab/kc/qwen_vl_api

    [Feat] Add qwen vl api

commit c09444860362a136f17641f8b2a1f91c2bbc3715
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Sat May 11 06:11:19 2024 +0000

    Fix llava_hf image tokens number issue

commit 64f07e497f53e5bcbe9e8fb5830cc7a1daaf7ff1
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 9 02:04:10 2024 +0000

    Fix endless warning for llava_hf generation

commit 8aaa828108da8514dd9cd23a9d6d83a8b67f2d65
Author: Bo Li <drluodian@gmail.com>
Date:   Thu May 2 06:13:56 2024 +0000

    Add model_name parameter to Llava constructor

commit 7847dc4d8efe60605102414bb071b1da9851228e
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Tue May 7 03:15:59 2024 +0000

    Parse result for llava_hf 1.6

commit 3e56b4f92db39a2ce92903b0c43a34f1d14d59ec
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Tue May 7 03:09:56 2024 +0000

    Fix llava_hf generation for 1.6

commit fa3ff92b07ea5aaa633a2039818c310744f84d07
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Mon May 6 08:32:57 2024 +0000

    Fix llava conv template for llama3

commit 423b00606aa77fd6b324c19e3d480b73ab852db6
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Sun May 5 07:54:52 2024 +0000

    Add qwen vl api

commit b7fd7a9f7aa3c0e1e50374047dfffc46a7462b90
Merge: 986139a9 c5a130b6
Author: Li Bo <drluodian@gmail.com>
Date:   Sun May 5 13:19:48 2024 +0800

    Merge pull request #59 from EvolvingLMMs-Lab/add_idefics2

    add idefics2

commit 986139a9a31154679bdea029b09639f84712db27
Merge: b46239ca 8d3526c0
Author: Li Bo <drluodian@gmail.com>
Date:   Fri May 3 01:18:18 2024 +0800

    Merge pull request #36 from cocoshe/main

    [Fix] repr llava doc

commit b46239cabab7b545ec99d9eae6c851e531b18374
Merge: bc69a744 373265f2
Author: Li Bo <drluodian@gmail.com>
Date:   Fri May 3 01:17:34 2024 +0800

    Merge pull request #56 from gagan3012/main

    Multilingual LLava bench

commit bc69a744d2cffeb06eba62e843bcc7869e27613a
Merge: eef3aeb6 626e8a91
Author: Li Bo <drluodian@gmail.com>
Date:   Fri May 3 01:12:14 2024 +0800

    Merge pull request #70 from hunterheiden/hsh/new_task/WebSRC

    Bugfix: WebSRC should be token-level F1 NOT character-level

commit 626e8a91a4af2dd5dd774fc130cc2f4d74b2bc37
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Thu May 2 09:31:03 2024 -0400

    Bugfix: WebSRC should be token-level F1 NOT character-level

commit eef3aeb6ab589bb1d5045af5b5c1984a69402d19
Merge: c4e9dd9f 9bca4413
Author: Li Bo <drluodian@gmail.com>
Date:   Thu May 2 14:38:17 2024 +0800

    Merge pull request #69 from hunterheiden/hsh/new_task/WebSRC

    [New Task] WebSRC (multimodal Q&A on web screenshots)

commit 9bca441376325173128e5c50087f068e519c48da
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Wed May 1 11:07:29 2024 -0400

    Add code to enable compilation of submission for WebSRC test split

commit 7687495b1ed552eeba088cb9ad5aaf1170e7fff9
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Wed May 1 10:47:32 2024 -0400

    Draft and validate websrc eval on dev split

commit 4eebd3e5d7ab3b8c3116eea57318db72d2ce32bb
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Wed May 1 10:46:54 2024 -0400

    Update main README with new task names

commit 35fe80b67656114a8824eb59574089663bdc4c9a
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Wed May 1 10:46:20 2024 -0400

    Draft README for WebSRC

commit 955bd0635cc6c14a96ad869f1002e6dbefdc5071
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Tue Apr 30 10:16:21 2024 -0400

    Init webSRC

commit c4e9dd9f6e40e8586587c4a75987aa109a37f14b
Merge: d8a3a99f 319afccb
Author: Li Bo <drluodian@gmail.com>
Date:   Fri Apr 26 14:37:22 2024 +0800

    Merge pull request #63 from hunterheiden/hsh/new_task/screenspot

    New Task: ScreenSpot - Grounding (REC) and instruction generation (REG) on screens

commit 319afccbe713ddf40a8a6fa28501e64c0ad34725
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Thu Apr 25 11:44:34 2024 -0400

    slight update

commit 2f3811ca1bbad6a441016b05fde09a571900fca8
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Thu Apr 25 11:41:04 2024 -0400

    Add README file specific to ScreenSpot

commit 28962cbe83631ec5d6481aaea4907a7c96fec848
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Wed Apr 24 11:52:33 2024 -0400

    Update README to reflect new tasks

commit e457cfb4f2d6869e8367d6d5b03ad25ee4acc363
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Tue Apr 23 18:33:16 2024 -0400

    Create ScreenSpot on clean branch

commit d8a3a99ff6142fe101fa3c188cc7f29593c44345
Merge: 3dcd0158 ed171293
Author: Li Bo <drluodian@gmail.com>
Date:   Tue Apr 23 10:34:03 2024 +0800

    Merge pull request #61 from tupini07/patch-1

    Fix typo in Qwen-VL that was causing "reference before assignment"

commit ed171293d1e82075c5c6a847fc91ecbfd45cf89f
Author: Andrea Tupini <tupini07@gmail.com>
Date:   Mon Apr 22 14:56:41 2024 -0600

    refactor query construction for clarity

commit cd874201c46f32a2903ddffae85f9db73e14adfd
Author: Andrea Tupini <tupini07@gmail.com>
Date:   Mon Apr 22 14:54:29 2024 -0600

    convert contexts to list if necessary and remove unnecessary construction of `questions`

commit 85573674e90c8d505312ba18c5102e0051255078
Author: Andrea Tupini <tupini07@gmail.com>
Date:   Mon Apr 22 14:47:33 2024 -0600

    Fix typo in qwen_vl that was causing "reference before assignment"

commit 3dcd01582b719555bcf8eb25d91cc5e42abd2c5f
Merge: 95df9fee 743673a1
Author: Li Bo <drluodian@gmail.com>
Date:   Sat Apr 20 22:03:16 2024 +0800

    Merge pull request #60 from CaraJ7/main

    Add MathVerse

commit 743673a1419b6e729e18c96f148745cc739d4c71
Merge: c1a54721 95df9fee
Author: CaraJ7 <1350074492@qq.com>
Date:   Sat Apr 20 21:49:02 2024 +0800

    Merge branch 'main' of https://github.com/EvolvingLMMs-Lab/lmms-eval

commit c1a5472135c3b84061b64d997ab50dda0412ba4f
Author: CaraJ7 <1350074492@qq.com>
Date:   Sat Apr 20 21:45:34 2024 +0800

    Add MathVerse

commit 373265f24e7a89cbd49ab724a2e388cc0930be78
Author: Gagan Bhatia <49101362+gagan3012@users.noreply.github.com>
Date:   Fri Apr 12 17:21:39 2024 -0700

    Add files via upload

commit d8530514a5ef9378d2adeaceb228b60ec25a6718
Author: Gagan Bhatia <49101362+gagan3012@users.noreply.github.com>
Date:   Fri Apr 12 17:19:49 2024 -0700

    Create README.md

commit 8d3526c0869f0ad7747ff6bb02441140792b461c
Author: cocoshe <1228759711@qq.com>
Date:   Thu Mar 28 13:38:36 2024 +0800

    fix doc
---
 LICENSE                                       |  56 ++
 README.md                                     | 366 +++++------
 docs/current_tasks.md                         | 122 ++++
 lmms_eval/api/task.py                         |  42 ++
 lmms_eval/models/__init__.py                  |   4 +-
 lmms_eval/models/gemini_model.py              | 163 -----
 lmms_eval/models/idefics2.py                  |   1 +
 lmms_eval/models/llava.py                     |  15 +-
 lmms_eval/models/llava_hf.py                  |  38 +-
 lmms_eval/models/llava_vid.py                 |   1 +
 .../{mplugOwlVideo.py => mplug_owl_video.py}  |   0
 lmms_eval/models/phi3v.py                     | 219 ++++++
 .../{llava_onevision.py => tinyllava.py}      | 335 ++++------
 .../{videoChatGPT.py => video_chatgpt.py}     |   0
 lmms_eval/models/xcomposer2_4khd.py           | 295 +++++++++
 lmms_eval/tasks/conbench/conbench.yaml        |  24 +
 lmms_eval/tasks/conbench/utils.py             |  98 +++
 lmms_eval/tasks/ii_bench/ii_bench.yaml        |  20 +
 lmms_eval/tasks/ii_bench/utils.py             |  71 ++
 lmms_eval/tasks/livebench/livebench.yaml      |  29 -
 lmms_eval/tasks/livebench/utils.py            | 185 ------
 .../longvideobench/longvideobench_val_i.yaml  |  29 +
 .../longvideobench/longvideobench_val_v.yaml  |  28 +
 lmms_eval/tasks/longvideobench/utils.py       | 361 ++++++++++
 lmms_eval/tasks/mathverse/mathverse.yaml      |  14 +
 lmms_eval/tasks/mathverse/mathverse_evals.py  | 305 +++++++++
 .../tasks/mathverse/mathverse_testmini.yaml   |  34 +
 .../mathverse_testmini_text_dominant.yaml     |  34 +
 .../mathverse_testmini_text_lite.yaml         |  34 +
 .../mathverse_testmini_text_only.yaml         |  34 +
 .../mathverse_testmini_vision_dominant.yaml   |  34 +
 .../mathverse_testmini_vision_intensive.yaml  |  34 +
 .../mathverse_testmini_vision_only.yaml       |  34 +
 lmms_eval/tasks/mathverse/utils.py            |  96 +++
 .../tasks/mathvista/mathvista_testmini.yaml   |   4 +-
 .../tasks/mix_evals/_default_template_yaml    |  16 -
 .../tasks/mix_evals/mix_evals_video2text.yaml |   5 -
 .../mix_evals_video2text_freeform.yaml        |  22 -
 .../mix_evals/mix_evals_video2text_mc.yaml    |  31 -
 .../mix_evals_video2text_openended.yaml       |  21 -
 lmms_eval/tasks/mix_evals/utils.py            | 266 --------
 .../tasks/mmupd/_default_template_mmupd_yaml  |  18 +
 lmms_eval/tasks/mmupd/mmaad_base.yaml         |  12 +
 lmms_eval/tasks/mmupd/mmaad_instruction.yaml  |  12 +
 lmms_eval/tasks/mmupd/mmaad_option.yaml       |  12 +
 lmms_eval/tasks/mmupd/mmiasd_base.yaml        |  12 +
 lmms_eval/tasks/mmupd/mmiasd_instruction.yaml |  12 +
 lmms_eval/tasks/mmupd/mmiasd_option.yaml      |  12 +
 lmms_eval/tasks/mmupd/mmivqd_base.yaml        |  12 +
 lmms_eval/tasks/mmupd/mmivqd_instruction.yaml |  12 +
 lmms_eval/tasks/mmupd/mmivqd_option.yaml      |  12 +
 lmms_eval/tasks/mmupd/mmupd.yaml              |  15 +
 lmms_eval/tasks/mmupd/mmupd_base.yaml         |  10 +
 lmms_eval/tasks/mmupd/mmupd_evals.py          | 621 ++++++++++++++++++
 lmms_eval/tasks/mmupd/mmupd_instruction.yaml  |   9 +
 lmms_eval/tasks/mmupd/mmupd_option.yaml       |   9 +
 lmms_eval/tasks/mmupd/utils.py                | 167 +++++
 .../README.md                                 |   1 +
 .../_default_template.yaml                    |  35 +
 .../arabic_llava_in_the_wild.yaml             |   6 +
 .../bengali_llava_in_the_wild.yaml            |   6 +
 .../chinese_llava_in_the_wild.yaml            |   6 +
 .../french_llava_in_the_wild.yaml             |   6 +
 .../hindi_llava_in_the_wild.yaml              |   6 +
 .../japanese_llava_in_the_wild.yaml           |   6 +
 .../rule.json                                 |  11 +
 .../russian_llava_in_the_wild.yaml            |   6 +
 .../spanish_llava_in_the_wild.yaml            |   6 +
 .../urdu_llava_in_the_wild.yaml               |   6 +
 .../utils.py                                  | 197 ++++++
 .../tasks/ok_vqa/_default_template_vqa_yaml   |   2 +-
 lmms_eval/tasks/ok_vqa/utils.py               |   2 +-
 lmms_eval/tasks/pope/pope_adv.yaml            |  35 +
 lmms_eval/tasks/pope/pope_full.yaml           |   5 +
 lmms_eval/tasks/pope/pope_pop.yaml            |  35 +
 lmms_eval/tasks/pope/pope_random.yaml         |  35 +
 lmms_eval/tasks/qbench/abench_dev.yaml        |  22 +
 lmms_eval/tasks/qbench/qbench2_dev.yaml       |  22 +
 lmms_eval/tasks/qbench/qbench_dev.yaml        |  22 +
 lmms_eval/tasks/qbench/qbenchs_dev.yaml       |   5 +
 lmms_eval/tasks/qbench/utils.py               | 249 +++++++
 lmms_eval/tasks/screenspot/README.md          |  54 ++
 .../screenspot/_default_template_rec_yaml     |  33 +
 .../screenspot/_default_template_reg_yaml     |  15 +
 lmms_eval/tasks/screenspot/_screenspot.yaml   |   4 +
 .../tasks/screenspot/screenspot_rec_test.yaml |   4 +
 .../tasks/screenspot/screenspot_reg_test.yaml |   4 +
 lmms_eval/tasks/screenspot/utils.py           | 126 ++++
 lmms_eval/tasks/screenspot/utils_rec.py       | 218 ++++++
 .../_default_sft_eval_ocr_rec_template_yaml   |  23 -
 .../_default_sft_eval_rest_template_yaml      |  20 -
 lmms_eval/tasks/sft_eval/_generate_config.py  |  94 ---
 lmms_eval/tasks/sft_eval/_sft_eval.yaml       |  34 -
 lmms_eval/tasks/sft_eval/sft_activity.yaml    |   5 -
 lmms_eval/tasks/sft_eval/sft_arts.yaml        |   5 -
 lmms_eval/tasks/sft_eval/sft_body.yaml        |   5 -
 lmms_eval/tasks/sft_eval/sft_car.yaml         |   5 -
 lmms_eval/tasks/sft_eval/sft_color.yaml       |   5 -
 lmms_eval/tasks/sft_eval/sft_commodity.yaml   |   5 -
 lmms_eval/tasks/sft_eval/sft_count.yaml       |   5 -
 lmms_eval/tasks/sft_eval/sft_daily.yaml       |   5 -
 lmms_eval/tasks/sft_eval/sft_engineer.yaml    |   5 -
 .../tasks/sft_eval/sft_entertainment.yaml     |   5 -
 lmms_eval/tasks/sft_eval/sft_exist.yaml       |   5 -
 lmms_eval/tasks/sft_eval/sft_face.yaml        |   5 -
 lmms_eval/tasks/sft_eval/sft_food.yaml        |   5 -
 lmms_eval/tasks/sft_eval/sft_healthcare.yaml  |   5 -
 lmms_eval/tasks/sft_eval/sft_landmark.yaml    |   5 -
 lmms_eval/tasks/sft_eval/sft_logo.yaml        |   5 -
 lmms_eval/tasks/sft_eval/sft_natural.yaml     |   5 -
 lmms_eval/tasks/sft_eval/sft_ocr_qa_adv.yaml  |   5 -
 .../tasks/sft_eval/sft_ocr_qa_chart.yaml      |   5 -
 lmms_eval/tasks/sft_eval/sft_ocr_qa_form.yaml |   5 -
 .../tasks/sft_eval/sft_ocr_qa_scene.yaml      |   5 -
 .../tasks/sft_eval/sft_ocr_qa_screen.yaml     |   5 -
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 lmms_eval/tasks/vizwiz_vqa/utils.py           |   2 +-
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 187 files changed, 5339 insertions(+), 2986 deletions(-)
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diff --git a/LICENSE b/LICENSE
new file mode 100644
index 00000000..aa318e26
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,56 @@
+# For the main pipeline structure-related code, we maintain the original license provided with lm-evaluation-harness, which is the MIT License.
+
+MIT License
+
+Copyright (c) 2024 LMMs-Lab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+# For the multimodal models and datasets that we have added (defined as code in the lmms_eval/tasks and lmms_eval/models folders), we apply the Apache License.
+
+Apache 2.0 License
+
+Copyright (c) 2024 LMMs-Lab
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+When modifying the code, please include the following information about the original lmms-eval source:
+# Adopted from lmms-eval from https://github.com/EvolvingLMMs-Lab/lmms-eval. Below is the original copyright:
+#
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
diff --git a/README.md b/README.md
index 73bd545e..2bf7a26e 100755
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-<p align="center" width="100%">
+<p align="center" width="80%">
 <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png"  width="100%" height="70%">
 </p>
 
@@ -8,265 +8,241 @@
 
 🏠 [LMMs-Lab Homepage](https://lmms-lab.github.io/) |  🎉 [Blog](https://lmms-lab.github.io/lmms-eval-blog/lmms-eval-0.1/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) | <a href="https://emoji.gg/emoji/1684-discord-thread"><img src="https://cdn3.emoji.gg/emojis/1684-discord-thread.png" width="14px" height="14px" alt="Discord_Thread"></a> [discord/lmms-eval](https://discord.gg/zdkwKUqrPy)
 
+---
 
-In today's world, we're on an exciting journey toward creating Artificial General Intelligence (AGI), much like the enthusiasm of the 1960s moon landing. This journey is powered by advanced large language models (LLMs) and large multimodal models (LMMs), which are complex systems capable of understanding, learning, and performing a wide variety of human tasks. These advancements bring us closer to achieving AGI.
+## Annoucement
 
-To gauge how advanced these models are, we use a variety of evaluation benchmarks. These benchmarks are tools that help us understand the capabilities of these models, showing us how close we are to achieving AGI. However, finding and using these benchmarks is a big challenge. The necessary benchmarks and datasets are spread out and hidden in various places like Google Drive, Dropbox, and different school and research lab websites. It feels like we're on a treasure hunt, but the maps are scattered everywhere.
+- [2024-06] 🎬🎬 The `lmms-eval/v0.2` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details
 
-In the field of language models, there has been a valuable precedent set by the work of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). They offer integrated data and model interfaces, enabling rapid evaluation of language models and serving as the backend support framework for the [open-llm-leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and has gradually become the underlying ecosystem of the era of foundation models.
+- [2024-03] 📝📝 We have released the first version of `lmms-eval`, please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.1/) for more details
 
-However, though there are many new evaluation datasets are recently proposed, the efficient evaluation pipeline of LMM is still in its infancy, and there is no unified evaluation framework that can be used to evaluate LMM across a wide range of datasets. To address this challenge, we introduce **lmms-eval**, an evaluation framework meticulously crafted for consistent and efficient evaluation of LMM.
+## Why `lmms-eval`?
 
-We humbly obsorbed the exquisite and efficient design of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). Building upon its foundation, we implemented our `lmms-eval` framework with performance optimizations specifically for LMMs.
+<p align="center" width="80%">
+<img src="https://i.postimg.cc/L5kNJsJf/Blue-Purple-Futuristic-Modern-3-D-Tech-Company-Business-Presentation.png"  width="100%" height="80%">
+</p>
 
-## Necessity of lmms-eval
+In today's world, we're on an exciting journey toward creating Artificial General Intelligence (AGI), much like the enthusiasm of the 1960s moon landing. This journey is powered by advanced large language models (LLMs) and large multimodal models (LMMs), which are complex systems capable of understanding, learning, and performing a wide variety of human tasks.
 
-We believe our effort could provide an efficient interface for the detailed comparison of publicly available models to discern their strengths and weaknesses. It's also useful for research institutions and production-oriented companies to accelerate the development of large multimodal models. With the `lmms-eval`, we have significantly accelerated the lifecycle of model iteration. Inside the LLaVA team, the utilization of `lmms-eval` largely improves the efficiency of the model development cycle, as we are able to evaluate weekly trained hundreds of checkpoints on 20-30 datasets, identifying the strengths and weaknesses, and then make targeted improvements.
+To gauge how advanced these models are, we use a variety of evaluation benchmarks. These benchmarks are tools that help us understand the capabilities of these models, showing us how close we are to achieving AGI. 
 
-# Annoucement
+However, finding and using these benchmarks is a big challenge. The necessary benchmarks and datasets are spread out and hidden in various places like Google Drive, Dropbox, and different school and research lab websites. It feels like we're on a treasure hunt, but the maps are scattered everywhere.
 
-## Contribution Guidance
+In the field of language models, there has been a valuable precedent set by the work of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). They offer integrated data and model interfaces, enabling rapid evaluation of language models and serving as the backend support framework for the [open-llm-leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and has gradually become the underlying ecosystem of the era of foundation models.
 
-We've added guidance on contributing new datasets and models. Please refer to our [documentation](docs/README.md). If you need assistance, you can contact us via [discord/lmms-eval](https://discord.gg/ebAMGSsS).
+We humbly obsorbed the exquisite and efficient design of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and introduce **lmms-eval**, an evaluation framework meticulously crafted for consistent and efficient evaluation of LMM.
 
-## v0.1.0 Released
+## Installation
 
-The first version of the `lmms-eval` is released. We are working on providing an one-command evaluation suite for accelerating the development of LMMs. 
+For formal usage, you can install the package from PyPI by running the following command:
+```bash
+pip install lmms-eval
+```
 
-> In [LLaVA Next](https://llava-vl.github.io/blog/2024-01-30-llava-next/) development, we internally utilize this suite to evaluate the multiple different model versions on various datasets. It significantly accelerates the model development cycle for it's easy integration and fast evaluation speed.
+For development, you can install the package by cloning the repository and running the following command:
+```bash
+git clone https://github.com/EvolvingLMMs-Lab/lmms-eval
+cd lmms-eval
+pip install -e .
+```
 
-The main feature includes:
+If you wanted to test llava, you will have to clone their repo from [LLaVA](https://github.com/haotian-liu/LLaVA) and
+```bash
+# for llava 1.5
+# git clone https://github.com/haotian-liu/LLaVA
+# cd LLaVA
+# pip install -e .
+
+# for llava-next (1.6)
+git clone https://github.com/LLaVA-VL/LLaVA-NeXT
+cd LLaVA-NeXT
+pip install -e .
+```
 
-<p align="center" width="100%">
-<img src="https://i.postimg.cc/sgzNmJx7/teaser.png"  width="100%" height="80%">
-</p>
+<details>
+<summary>Reproduction of LLaVA-1.5's paper results</summary>
 
-### One-command evaluation, with detailed logs and samples.
-You can evaluate the models on multiple datasets with a single command. No model/data preparation is needed, just one command line, few minutes, and get the results. Not just a result number, but also the detailed logs and samples, including the model args, input question, model response, and ground truth answer.
+You can check the [environment install script](miscs/repr_scripts.sh) and [torch environment info](miscs/repr_torch_envs.txt) to **reproduce LLaVA-1.5's paper results**. We found torch/cuda versions difference would cause small variations in the results, we provide the [results check](miscs/llava_result_check.md) with different environments.
 
-```python
-# Evaluating LLaVA on multiple datasets
-accelerate launch --num_processes=8 -m lmms_eval --model llava   --model_args pretrained="liuhaotian/llava-v1.5-7b"   --tasks mme,mmbench_en --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme_mmbenchen --output_path ./logs/ #
-```
+</details>
 
-### Accelerator support and Tasks grouping.
-We support the usage of `accelerate` to wrap the model for distributed evaluation, supporting multi-gpu and tensor parallelism. With **Task Grouping**, all instances from all tasks are grouped and evaluated in parallel, which significantly improves the throughput of the evaluation. After evaluation, all instances are sent to postprocessing module for metric calcuations and potential GPT4-eval queries.
+If you want to test on caption dataset such as `coco`, `refcoco`, and `nocaps`, you will need to have `java==1.8.0 ` to let pycocoeval api to work. If you don't have it, you can install by using conda
+```
+conda install openjdk=8
+```
+you can then check your java version by `java -version` 
 
-Below are the total runtime on different datasets using 4 x A100 40G.
 
-| Dataset (#num)          | LLaVA-v1.5-7b      | LLaVA-v1.5-13b     |
-| :---------------------- | :----------------- | :----------------- |
-| mme (2374)              | 2 mins 43 seconds  | 3 mins 27 seconds  |
-| gqa (12578)             | 10 mins 43 seconds | 14 mins 23 seconds |
-| scienceqa_img (2017)    | 1 mins 58 seconds  | 2 mins 52 seconds  |
-| ai2d (3088)             | 3 mins 17 seconds  | 4 mins 12 seconds  |
-| coco2017_cap_val (5000) | 14 mins 13 seconds | 19 mins 58 seconds |
+<details>
+<summary>Comprehensive Evaluation Results of LLaVA Family Models</summary>
+<br>
 
-### All-In-One HF dataset hubs.
+As demonstrated by the extensive table below, we aim to provide detailed information for readers to understand the datasets included in lmms-eval and some specific details about these datasets (we remain grateful for any corrections readers may have during our evaluation process).
 
-We are hosting more than 40 (and increasing) datasets on [huggingface/lmms-lab](https://huggingface.co/lmms-lab), we carefully converted these datasets from original sources and included all variants, versions and splits. Now they can be directly accessed without any burden of data preprocessing. They also serve for the purpose of visualizing the data and grasping the sense of evaluation tasks distribution.
+We provide a Google Sheet for the detailed results of the LLaVA series models on different datasets. You can access the sheet [here](https://docs.google.com/spreadsheets/d/1a5ImfdKATDI8T7Cwh6eH-bEsnQFzanFraFUgcS9KHWc/edit?usp=sharing). It's a live sheet, and we are updating it with new results.
 
 <p align="center" width="100%">
-<img src="https://i.postimg.cc/8PXFW9sk/WX20240228-123110_2x.png"  width="100%" height="80%">
+<img src="https://i.postimg.cc/jdw497NS/WX20240307-162526-2x.png"  width="100%" height="80%">
 </p>
 
-### Detailed Logging Utilites
+We also provide the raw data exported from Weights & Biases for the detailed results of the LLaVA series models on different datasets. You can access the raw data [here](https://docs.google.com/spreadsheets/d/1AvaEmuG4csSmXaHjgu4ei1KBMmNNW8wflOD_kkTDdv8/edit?usp=sharing).
 
-We provide detailed logging utilities to help you understand the evaluation process and results. The logs include the model args, generation parameters, input question, model response, and ground truth answer. You can also record every details and visualize them inside runs on Weights & Biases.
+</details>
+<br>
 
-{% include figure.liquid loading="eager" path="assets/img/wandb_table.png" class="img-fluid rounded z-depth-1" zoomable=true %}
 
-<p align="center" width="100%">
-<img src="https://i.postimg.cc/W1c1vBDJ/Wechat-IMG1993.png"  width="100%" height="80%">
-</p>
+Our Development will be continuing on the main branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub.
 
-# Installation
+## Multiple Usages
+
+**Evaluation of LLaVA on MME**
 
-For formal usage, you can install the package from PyPI by running the following command:
 ```bash
-pip install lmms-eval
+python3 -m accelerate.commands.launch \
+    --num_processes=8 \
+    -m lmms_eval \
+    --model llava \
+    --model_args pretrained="liuhaotian/llava-v1.5-7b" \
+    --tasks mme \
+    --batch_size 1 \
+    --log_samples \
+    --log_samples_suffix llava_v1.5_mme \
+    --output_path ./logs/
 ```
 
-For development, you can install the package by cloning the repository and running the following command:
+**Evaluation of LLaVA on multiple datasets**
+
 ```bash
-git clone https://github.com/EvolvingLMMs-Lab/lmms-eval
-cd lmms-eval
-pip install -e .
+python3 -m accelerate.commands.launch \
+    --num_processes=8 \
+    -m lmms_eval \
+    --model llava \
+    --model_args pretrained="liuhaotian/llava-v1.5-7b" \
+    --tasks mme,mmbench_en \
+    --batch_size 1 \
+    --log_samples \
+    --log_samples_suffix llava_v1.5_mme_mmbenchen \
+    --output_path ./logs/
 ```
 
-If you wanted to test llava, you will have to clone their repo from [LLaVA](https://github.com/haotian-liu/LLaVA) and
-```
-git clone https://github.com/haotian-liu/LLaVA
-cd LLaVA
-pip install -e .
-```
+**For other variants llava. Please change the `conv_template` in the `model_args`**
 
-You can check the [environment install script](miscs/repr_scripts.sh) and [torch environment info](miscs/repr_torch_envs.txt) to **reproduce LLaVA-1.5's paper results**. We found torch/cuda versions difference would cause small variations in the results, we provide the [results check](miscs/llava_result_check.md) with different environments.
+> `conv_template` is an arg of the init function of llava in `lmms_eval/models/llava.py`, you could find the corresponding value at LLaVA's code, probably in a dict variable `conv_templates` in `llava/conversations.py`
 
-If you want to test on caption dataset such as `coco`, `refcoco`, and `nocaps`, you will need to have `java==1.8.0 ` to let pycocoeval api to work. If you don't have it, you can install by using conda
-```
-conda install openjdk=8
+```bash
+python3 -m accelerate.commands.launch \
+    --num_processes=8 \
+    -m lmms_eval \
+    --model llava \
+    --model_args pretrained="liuhaotian/llava-v1.6-mistral-7b,conv_template=mistral_instruct" \
+    --tasks mme,mmbench_en \
+    --batch_size 1 \
+    --log_samples \
+    --log_samples_suffix llava_v1.5_mme_mmbenchen \
+    --output_path ./logs/
 ```
-you can then check your java version by `java -version` 
 
-# Usage
+**Evaluation of larger lmms (llava-v1.6-34b)**
+
 ```bash
-# Evaluating LLaVA on MME
-accelerate launch --num_processes=8 -m lmms_eval --model llava   --model_args pretrained="liuhaotian/llava-v1.5-7b"   --tasks mme  --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme --output_path ./logs/ 
+python3 -m accelerate.commands.launch \
+    --num_processes=8 \
+    -m lmms_eval \
+    --model llava \
+    --model_args pretrained="liuhaotian/llava-v1.6-34b,conv_template=mistral_direct" \
+    --tasks mme,mmbench_en \
+    --batch_size 1 \
+    --log_samples \
+    --log_samples_suffix llava_v1.5_mme_mmbenchen \
+    --output_path ./logs/
+```
 
-# Evaluating LLaVA on multiple datasets
-accelerate launch --num_processes=8 -m lmms_eval --model llava   --model_args pretrained="liuhaotian/llava-v1.5-7b"   --tasks mme,mmbench_en --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme_mmbenchen --output_path ./logs/ #
+**Evaluation with a set of configurations, supporting evaluation of multiple models and datasets**
 
-# For other variants llava. Note that `conv_template` is an arg of the init function of llava in `lmms_eval/models/llava.py`
-accelerate launch --num_processes=8 -m lmms_eval --model llava   --model_args pretrained="liuhaotian/llava-v1.6-mistral-7b,conv_template=mistral_instruct"   --tasks mme,mmbench_en --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme_mmbenchen --output_path ./logs/ #
-accelerate launch --num_processes=8 -m lmms_eval --model llava   --model_args pretrained="liuhaotian/llava-v1.6-34b,conv_template=mistral_direct"   --tasks mme,mmbench_en --batch_size 1 --log_samples --log_samples_suffix llava_v1.5_mme_mmbenchen --output_path ./logs/ #
+```bash
+python3 -m accelerate.commands.launch --num_processes=8 -m lmms_eval --config ./miscs/example_eval.yaml
+```
 
-# From a predefined configuration, supporting evaluation of multiple models and datasets
-accelerate launch --num_processes=8 -m lmms_eval --config example_eval.yaml 
+**Evaluation with naive model sharding for bigger model (llava-next-72b)**
+
+```bash
+python3 -m lmms_eval \
+    --model=llava \
+    --model_args=pretrained=lmms-lab/llava-next-72b,conv_template=qwen_1_5,device_map=auto,model_name=llava_qwen \
+    --tasks=pope,vizwiz_vqa_val,scienceqa_img \
+    --batch_size=1 \
+    --log_samples \
+    --log_samples_suffix=llava_qwen \
+    --output_path="./logs/" \
+    --wandb_args=project=lmms-eval,job_type=eval,entity=llava-vl
 ```
 
-# Model Results
+**Evaluation with SGLang for bigger model (llava-next-72b)**
 
-As demonstrated by the extensive table below, we aim to provide detailed information for readers to understand the datasets included in lmms-eval and some specific details about these datasets (we remain grateful for any corrections readers may have during our evaluation process).
+```bash
+python3 -m lmms_eval \
+	--model=llava_sglang \
+	--model_args=pretrained=lmms-lab/llava-next-72b,tokenizer=lmms-lab/llavanext-qwen-tokenizer,conv_template=chatml-llava,tp_size=8,parallel=8 \
+	--tasks=mme \
+	--batch_size=1 \
+	--log_samples \
+	--log_samples_suffix=llava_qwen \
+	--output_path=./logs/ \
+	--verbosity=INFO
+```
 
-We provide a Google Sheet for the detailed results of the LLaVA series models on different datasets. You can access the sheet [here](https://docs.google.com/spreadsheets/d/1a5ImfdKATDI8T7Cwh6eH-bEsnQFzanFraFUgcS9KHWc/edit?usp=sharing). It's a live sheet, and we are updating it with new results.
+### Supported models
 
-<p align="center" width="100%">
-<img src="https://i.postimg.cc/jdw497NS/WX20240307-162526-2x.png"  width="100%" height="80%">
-</p>
+Please check [supported models](lmms_eval/models/__init__.py) for more details.
 
-We also provide the raw data exported from Weights & Biases for the detailed results of the LLaVA series models on different datasets. You can access the raw data [here](https://docs.google.com/spreadsheets/d/1AvaEmuG4csSmXaHjgu4ei1KBMmNNW8wflOD_kkTDdv8/edit?usp=sharing).
+### Supported tasks
+
+Please check [supported tasks](lmms_eval/docs/current_tasks.md) for more details.
 
-> Development will be continuing on the main branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub.
-
-
-## Supported models
-
-- GPT4V (API, only generation-based evaluation)
-- LLaVA-v1.5/v1.6-7B/13B/34B (ppl-based, generation-based)
-- Qwen-VL series (ppl-based, generation-based)
-- Fuyu series (ppl-based, generation-based)
-- InstructBLIP series (generation-based)
-
-## Supported datasets
-> () indicates the task name in the lmms_eval. The task name is also used to specify the dataset in the configuration file.
-
-- AI2D (ai2d)
-- ChartQA (chartqa)
-- CMMMU (cmmmu)
-  - CMMMU Validation (cmmmu_val)
-  - CMMMU Test (cmmmu_test)
-- COCO Caption (coco_cap)
-  - COCO 2014 Caption (coco2014_cap)
-    - COCO 2014 Caption Validation (coco2014_cap_val)
-    - COCO 2014 Caption Test (coco2014_cap_test)
-  - COCO 2017 Caption (coco2017_cap)
-    - COCO 2017 Caption MiniVal (coco2017_cap_val)
-    - COCO 2017 Caption MiniTest (coco2017_cap_test)
-- DOCVQA (docvqa)
-  - DOCVQA Validation (docvqa_val)
-  - DOCVQA Test (docvqa_test)
-- Ferret (ferret)
-- Flickr30K (flickr30k)
-  - Ferret Test (ferret_test)
-- GQA (gqa)
-- HallusionBenchmark (hallusion_bench_image)
-- Infographic VQA (info_vqa)
-  - Infographic VQA Validation (info_vqa_val)
-  - Infographic VQA Test (info_vqa_test)
-- LLaVA-Bench (llava_in_the_wild)
-- LLaVA-Bench-COCO (llava_bench_coco)
-- MathVista (mathvista)
-  - MathVista Validation (mathvista_testmini)
-  - MathVista Test (mathvista_test)
-- MMBench (mmbench)
-  - MMBench English (mmbench_en)
-    - MMBench English Dev (mmbench_en_dev)
-    - MMBench English Test (mmbench_en_test)
-  - MMBench Chinese (mmbench_cn)
-    - MMBench Chinese Dev (mmbench_cn_dev)
-    - MMBench Chinese Test (mmbench_cn_test)
-- MME (mme)
-- MMMU (mmmu)
-  - MMMU Validation (mmmu_val)
-  - MMMU Test (mmmu_test)
-- MMVet (mmvet)
-- Multi-DocVQA (multidocvqa)
-  - Multi-DocVQA Validation (multidocvqa_val)
-  - Multi-DocVQA Test (multidocvqa_test)
-- NoCaps (nocaps)
-  - NoCaps Validation (nocaps_val)
-  - NoCaps Test (nocaps_test)
-- OKVQA (ok_vqa)
-  - OKVQA Validation 2014 (ok_vqa_val2014)
-- POPE (pope)
-- RefCOCO (refcoco)
-    - refcoco_seg_test
-    - refcoco_seg_val
-    - refcoco_seg_testA
-    - refcoco_seg_testB
-    - refcoco_bbox_test
-    - refcoco_bbox_val
-    - refcoco_bbox_testA
-    - refcoco_bbox_testB
-- RefCOCO+ (refcoco+)
-    - refcoco+_seg
-        - refcoco+_seg_val
-        - refcoco+_seg_testA
-        - refcoco+_seg_testB
-    - refcoco+_bbox
-        - refcoco+_bbox_val
-        - refcoco+_bbox_testA
-        - refcoco+_bbox_testB
-- RefCOCOg (refcocog)
-    - refcocog_seg_test
-    - refcocog_seg_val
-    - refcocog_bbox_test
-    - refcocog_bbox_val
-- ScienceQA (scienceqa_full)
-  - ScienceQA Full (scienceqa)
-  - ScienceQA IMG (scienceqa_img)
-- SeedBench (seedbench)
-- SeedBench 2 (seedbench_2)
-- ST-VQA (stvqa)
-- TextCaps (textcaps)
-  - TextCaps Validation (textcaps_val)
-  - TextCaps Test (textcaps_test)
-- TextVQA (textvqa)
-  - TextVQA Validation (textvqa_val)
-  - TextVQA Test (textvqa_test)
-- VizWizVQA (vizwiz_vqa)
-  - VizWizVQA Validation (vizwiz_vqa_val)
-  - VizWizVQA Test (vizwiz_vqa_test)
-- VQAv2 (vqav2)
-  - VQAv2 Validation (vqav2_val)
-  - VQAv2 Test (vqav2_test)
-
-## Datasets to be added and tested
-- TallyQA (tallyqa)
-- VSR (vsr)
-- Winoground (winoground)
-- NLVR2 (nlvr2)
-- RavenIQ-Test (raveniq)
-- IconQA (iconqa)
-- VistBench (vistbench)
-
-# Add Customized Model and Dataset
+## Add Customized Model and Dataset
 
 Please refer to our [documentation](docs/README.md).
 
-# Acknowledgement
+## Acknowledgement
 
 lmms_eval is a fork of [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). We recommend you to read through the [docs of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs) for relevant information. 
 
+---
+
 Below are the changes we made to the original API:
 - Build context now only pass in idx and process image and doc during the model responding phase. This is due to the fact that dataset now contains lots of images and we can't store them in the doc like the original lm-eval-harness other wise the cpu memory would explode.
 - Instance.args (lmms_eval/api/instance.py) now contains a list of images to be inputted to lmms.
 - lm-eval-harness supports all HF language models as single model class. Currently this is not possible of lmms because the input/output format of lmms in HF are not yet unified. Thererfore, we have to create a new class for each lmms model. This is not ideal and we will try to unify them in the future.
 
-We also thank:
+---
+
+During the initial stage of our project, we thank:
 - [Xiang Yue](https://xiangyue9607.github.io/), [Jingkang Yang](https://jingkang50.github.io/), [Dong Guo](https://www.linkedin.com/in/dongguoset/) and [Sheng Shen](https://sincerass.github.io/) for early discussion and testing.
 
+---
+
+During the `v0.1` to `v0.2`, we thank the community support from pull requests (PRs):
+
+> Details are in [lmms-eval/v0.2.0 release notes](https://github.com/EvolvingLMMs-Lab/lmms-eval/releases/tag/untagged-9057ff0e9a72d5a5846f)
+
+**Datasets:**
+
+- VCR: Visual Caption Restoration (officially from the authors, MILA)
+- ConBench (officially from the authors, PKU/Bytedance)
+- MathVerse (officially from the authors, CUHK)
+- MM-UPD (officially from the authors, University of Tokyo)
+- WebSRC (from Hunter Heiden)
+- ScreeSpot (from Hunter Heiden)
+- RealworldQA (from Fanyi Pu, NTU)
+- Multi-lingual LLaVA-W (from Gagan Bhatia, UBC)
+
+**Models:**
+
+- LLaVA-HF (officially from Huggingface)
+- Idefics-2 (from the lmms-lab team)
+- microsoft/Phi-3-Vision (officially from the authors, Microsoft)
+- LLaVA-SGlang (from the lmms-lab team)
+
 ## Citations
 
 ```shell
diff --git a/docs/current_tasks.md b/docs/current_tasks.md
new file mode 100644
index 00000000..1622e960
--- /dev/null
+++ b/docs/current_tasks.md
@@ -0,0 +1,122 @@
+# Current Tasks
+
+> () indicates the task name in the lmms_eval. The task name is also used to specify the dataset in the configuration file.
+> The following is manually updated documentation. You could use `lmms_eval task --list` to list all supported tasks and their task names. 
+
+- AI2D (ai2d)
+- ChartQA (chartqa)
+- CMMMU (cmmmu)
+  - CMMMU Validation (cmmmu_val)
+  - CMMMU Test (cmmmu_test)
+- COCO Caption (coco_cap)
+  - COCO 2014 Caption (coco2014_cap)
+    - COCO 2014 Caption Validation (coco2014_cap_val)
+    - COCO 2014 Caption Test (coco2014_cap_test)
+  - COCO 2017 Caption (coco2017_cap)
+    - COCO 2017 Caption MiniVal (coco2017_cap_val)
+    - COCO 2017 Caption MiniTest (coco2017_cap_test)
+- [ConBench](https://github.com/foundation-multimodal-models/ConBench) (conbench)
+- DOCVQA (docvqa)
+  - DOCVQA Validation (docvqa_val)
+  - DOCVQA Test (docvqa_test)
+- Ferret (ferret)
+- Flickr30K (flickr30k)
+  - Ferret Test (ferret_test)
+- GQA (gqa)
+- HallusionBenchmark (hallusion_bench_image)
+- Infographic VQA (info_vqa)
+  - Infographic VQA Validation (info_vqa_val)
+  - Infographic VQA Test (info_vqa_test)
+- LLaVA-Bench (llava_in_the_wild)
+- LLaVA-Bench-COCO (llava_bench_coco)
+- MathVerse (mathverse)
+  - MathVerse Text Dominant (mathverse_testmini_text_dominant)
+  - MathVerse Text Only (mathverse_testmini_text_only)
+  - MathVerse Text Lite (mathverse_testmini_text_lite)
+  - MathVerse Vision Dominant (mathverse_testmini_vision_dominant)
+  - MathVerse Vision Intensive (mathverse_testmini_vision_intensive)
+  - MathVerse Vision Only (mathverse_testmini_vision_only)
+- MathVista (mathvista)
+  - MathVista Validation (mathvista_testmini)
+  - MathVista Test (mathvista_test)
+- MMBench (mmbench)
+  - MMBench English (mmbench_en)
+    - MMBench English Dev (mmbench_en_dev)
+    - MMBench English Test (mmbench_en_test)
+  - MMBench Chinese (mmbench_cn)
+    - MMBench Chinese Dev (mmbench_cn_dev)
+    - MMBench Chinese Test (mmbench_cn_test)
+- MME (mme)
+- MMMU (mmmu)
+  - MMMU Validation (mmmu_val)
+  - MMMU Test (mmmu_test)
+- MMUPD (mmupd)
+  - MMUPD Base (mmupd_base)
+    - MMAAD Base (mmaad_base)
+    - MMIASD Base (mmiasd_base)
+    - MMIVQD Base (mmivqd_base)
+  - MMUPD Option (mmupd_option)
+    - MMAAD Option (mmaad_option)
+    - MMIASD Option (mmiasd_option)
+    - MMIVQD Option (mmivqd_option)
+  - MMUPD Instruction (mmupd_instruction)
+    - MMAAD Instruction (mmaad_instruction)
+    - MMIASD Instruction (mmiasd_instruction)
+    - MMIVQD Instruction (mmivqd_instruction)
+- MMVet (mmvet)
+- Multi-DocVQA (multidocvqa)
+  - Multi-DocVQA Validation (multidocvqa_val)
+  - Multi-DocVQA Test (multidocvqa_test)
+- NoCaps (nocaps)
+  - NoCaps Validation (nocaps_val)
+  - NoCaps Test (nocaps_test)
+- OKVQA (ok_vqa)
+  - OKVQA Validation 2014 (ok_vqa_val2014)
+- POPE (pope)
+- RefCOCO (refcoco)
+    - refcoco_seg_test
+    - refcoco_seg_val
+    - refcoco_seg_testA
+    - refcoco_seg_testB
+    - refcoco_bbox_test
+    - refcoco_bbox_val
+    - refcoco_bbox_testA
+    - refcoco_bbox_testB
+- RefCOCO+ (refcoco+)
+    - refcoco+_seg
+        - refcoco+_seg_val
+        - refcoco+_seg_testA
+        - refcoco+_seg_testB
+    - refcoco+_bbox
+        - refcoco+_bbox_val
+        - refcoco+_bbox_testA
+        - refcoco+_bbox_testB
+- RefCOCOg (refcocog)
+    - refcocog_seg_test
+    - refcocog_seg_val
+    - refcocog_bbox_test
+    - refcocog_bbox_val
+- ScienceQA (scienceqa_full)
+  - ScienceQA Full (scienceqa)
+  - ScienceQA IMG (scienceqa_img)
+- ScreenSpot (screenspot)
+  - ScreenSpot REC / Grounding (screenspot_rec)
+  - ScreenSpot REG / Instruction Generation (screenspot_reg)
+- SeedBench (seedbench)
+- SeedBench 2 (seedbench_2)
+- ST-VQA (stvqa)
+- TextCaps (textcaps)
+  - TextCaps Validation (textcaps_val)
+  - TextCaps Test (textcaps_test)
+- TextVQA (textvqa)
+  - TextVQA Validation (textvqa_val)
+  - TextVQA Test (textvqa_test)
+- VizWizVQA (vizwiz_vqa)
+  - VizWizVQA Validation (vizwiz_vqa_val)
+  - VizWizVQA Test (vizwiz_vqa_test)
+- VQAv2 (vqav2)
+  - VQAv2 Validation (vqav2_val)
+  - VQAv2 Test (vqav2_test)
+- WebSRC (websrc)
+  - WebSRC Validation (websrc_val)
+  - WebSRC Test (websrc_test)
\ No newline at end of file
diff --git a/lmms_eval/api/task.py b/lmms_eval/api/task.py
index c035a0a2..3e9040e6 100755
--- a/lmms_eval/api/task.py
+++ b/lmms_eval/api/task.py
@@ -778,6 +778,7 @@ def _download_from_youtube(path):
                     force_unzip = dataset_kwargs.get("force_unzip", False)
                     cache_path = snapshot_download(repo_id=self.DATASET_PATH, repo_type="dataset", force_download=force_download, etag_timeout=60)
                     zip_files = glob(os.path.join(cache_path, "**/*.zip"), recursive=True)
+                    tar_files = glob(os.path.join(cache_path, "**/*.tar*"), recursive=True)
 
                     def unzip_video_data(zip_file):
                         import zipfile
@@ -786,10 +787,51 @@ def unzip_video_data(zip_file):
                             zip_ref.extractall(cache_dir)
                             eval_logger.info(f"Extracted all files from {zip_file} to {cache_dir}")
 
+                    def untar_video_data(tar_file):
+                        import tarfile
+
+                        with tarfile.open(tar_file, "r") as tar_ref:
+                            tar_ref.extractall(cache_dir)
+                            eval_logger.info(f"Extracted all files from {tar_file} to {cache_dir}")
+
+                    def concat_tar_parts(tar_parts, output_tar):
+                        with open(output_tar, "wb") as out_tar:
+                            from tqdm import tqdm
+
+                            for part in tqdm(sorted(tar_parts)):
+                                with open(part, "rb") as part_file:
+                                    out_tar.write(part_file.read())
+                        eval_logger.info(f"Concatenated parts {tar_parts} into {output_tar}")
+
+                    # Unzip zip files if needed
                     if force_unzip or (not os.path.exists(cache_dir) and len(zip_files) > 0):
                         for zip_file in zip_files:
                             unzip_video_data(zip_file)
 
+                    # Concatenate and extract tar files if needed
+                    if force_unzip or (not os.path.exists(cache_dir) and len(tar_files) > 0):
+                        tar_parts_dict = {}
+
+                        # Group tar parts together
+                        for tar_file in tar_files:
+                            base_name = tar_file.split(".tar")[0]
+                            if base_name not in tar_parts_dict:
+                                tar_parts_dict[base_name] = []
+                            tar_parts_dict[base_name].append(tar_file)
+
+                        # Concatenate and untar split parts
+                        for base_name, parts in tar_parts_dict.items():
+                            eval_logger.info(f"Extracting following tar files: {parts}")
+                            output_tar = base_name + ".tar"
+                            if not os.path.exists(output_tar):
+                                eval_logger.info(f"Start concatenating tar files")
+
+                                concat_tar_parts(parts, output_tar)
+                                eval_logger.info(f"Finish concatenating tar files")
+
+                            if not os.path.exists(os.path.join(cache_dir, os.path.basename(base_name))):
+                                untar_video_data(output_tar)
+
                 accelerator.wait_for_everyone()
                 dataset_kwargs.pop("cache_dir")
                 dataset_kwargs.pop("video")
diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py
index 4a78c742..ec354cf3 100755
--- a/lmms_eval/models/__init__.py
+++ b/lmms_eval/models/__init__.py
@@ -26,7 +26,9 @@
     "reka": "Reka",
     "llava_onevision": "Llava_OneVision",
     "from_log": "FromLog",
-    "mplugOwlVideo": "mplug_Owl",
+    "mplug_owl_video": "mplug_Owl",
+    "phi3v": "Phi3v",
+    "tinyllava": "TinyLlava",
 }
 
 for model_name, model_class in AVAILABLE_MODELS.items():
diff --git a/lmms_eval/models/gemini_model.py b/lmms_eval/models/gemini_model.py
deleted file mode 100644
index dfc7d818..00000000
--- a/lmms_eval/models/gemini_model.py
+++ /dev/null
@@ -1,163 +0,0 @@
-from io import BytesIO
-from copy import deepcopy
-import numpy as np
-import os
-import base64
-from typing import List, Tuple
-from tqdm import tqdm
-import requests as url_requests
-import time
-import logging
-
-from lmms_eval.api.instance import Instance
-from lmms_eval.api.model import lmms
-from lmms_eval.api.registry import register_model
-from lmms_eval import utils
-
-from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
-from accelerate.state import AcceleratorState
-
-try:
-    from decord import VideoReader, AudioReader, cpu
-except ImportError:
-    pass
-
-from PIL import Image
-
-eval_logger = logging.getLogger("lmms-eval")
-
-NUM_SECONDS_TO_SLEEP = 5
-API_TYPE = os.getenv("API_TYPE", "openai")
-if API_TYPE == "openai":  # FIXME: Please modify this to support other type of API
-    API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
-    API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json",
-    }
-else:
-    eval_logger.error("API_TYPE not supported")
-
-
-@register_model("gemini_model")
-class GeminiModel(lmms):
-    def __init__(
-        self,
-        model_version: str = "gemini-pro",  # FIXME: Please modify this to support real gemini model version
-        modality: str = "video",
-        max_frames_for_video: int = -1,
-        frame_rate: int = -1,
-        timeout: int = 120,
-        **kwargs,
-    ) -> None:
-        super().__init__()
-        self.model_version = model_version
-        self.modality = modality
-        self.max_frames_for_video = max_frames_for_video
-        self.frame_rate = frame_rate
-        assert self.modality in ["image", "video"], "Modality must be either image or video"
-        assert self.max_frames_for_video == -1 or self.frame_rate == -1, "max_frames_for_video and frame_rate cannot be provided at the same time"
-        assert self.max_frames_for_video == -1 if self.frame_rate != -1 else True, "max_frames_for_video must be -1 if frame_rate > 0"
-        self.image_token = "<image>"  # In case the question contains <image> token for placeholder
-        self.timeout = timeout
-
-        accelerator = Accelerator()  # This is not neccessary, it is only used to get the rank and world size
-        if accelerator.num_processes > 1:
-            assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
-            self.accelerator = accelerator
-            if self.accelerator.is_local_main_process:
-                eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
-            self._rank = self.accelerator.local_process_index
-            self._world_size = self.accelerator.num_processes
-
-            self.device = self.accelerator.device
-
-    # Function to encode the image
-    def encode_image(self, image: Image):
-        output_buffer = BytesIO()
-        image.save(output_buffer, format="PNG")
-        byte_data = output_buffer.getvalue()
-        base64_str = base64.b64encode(byte_data).decode("utf-8")
-        return base64_str
-
-    # Function to encode the video
-    def encode_video(self, video_path, for_get_frames_num=-1, frame_rate=-1):
-        assert for_get_frames_num != -1 or frame_rate != -1, "Either for_get_frames_num or frame_rate must be provided"
-        vr = VideoReader(video_path, ctx=cpu(0))
-        total_frame_num = len(vr)
-        if for_get_frames_num != -1:
-            uniform_sampled_frames = np.linspace(0, total_frame_num - 1, for_get_frames_num, dtype=int)
-            frame_idx = uniform_sampled_frames.tolist()
-        else:
-            frame_idx = range(0, total_frame_num, frame_rate)
-        frames = vr.get_batch(frame_idx).asnumpy()
-
-        base64_frames = []
-        for frame in frames:
-            img = Image.fromarray(frame)
-            output_buffer = BytesIO()
-            img.save(output_buffer, format="JPEG")
-            byte_data = output_buffer.getvalue()
-            base64_str = base64.b64encode(byte_data).decode("utf-8")
-            base64_frames.append(base64_str)
-
-        # Extract audio
-        try:
-            ar = AudioReader(video_path, sample_rate=44100, mono=False, ctx=cpu(0))
-            audio = ar.get_batch(frame_idx).asnumpy()
-            audio_buffer = BytesIO()
-            audio.save(audio_buffer, format="mp3")
-            audio_byte_data = audio_buffer.getvalue()
-            base64_audio = base64.b64encode(audio_byte_data).decode("utf-8")
-        except Exception as e:
-            eval_logger.error(f"Error extracting audio or no audio found for video: {video_path}")
-            base64_audio = None
-
-        return base64_frames, base64_audio
-
-    def flatten(self, input):
-        new_list = []
-        for i in input:
-            for j in i:
-                new_list.append(j)
-        return new_list
-
-    def model_call(self, gemini_global_content):
-        # TODO: Please implement the model call here
-        # input = [("text", "what does this video describe?"), ("image_bytes", image_png_bytes), ("image_bytes", image_png_bytes), ("image_bytes", image_png_bytes), ....]
-        gemini_response_content = {"example_id": gemini_global_content["example_id"], "response_content": "This is a placeholder response from the model. Please implement the model call."}
-        return gemini_response_content
-
-    def generate_until(self, requests) -> List[str]:
-        res = []
-        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
-
-        for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
-            # encode, pad, and truncate contexts for this batch
-            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
-            visuals = self.flatten(visuals)
-            imgs = []  # multiple images or frames for video
-            for visual in visuals:
-                if self.modality == "image":
-                    img = self.encode_image(visual)
-                    imgs.append(img)
-                elif self.modality == "video":
-                    frames, audio = self.encode_video(visual, self.max_frames_for_video, self.frame_rate)  # FIXME: I am not sure how to put audio information into query, please modify this
-                    imgs.extend(frames)
-
-            ########################## Implement the following code snippet ##########################
-
-            # input = [("text", "what does this video describe?"), ("image_bytes", image_png_bytes), ("image_bytes", image_png_bytes), ("image_bytes", image_png_bytes), ....]
-            gemini_input_content = {"example_id": f"{doc_id:06d}", "request_content": [("text", contexts)]}
-            gemini_input_content["request_content"].extend([("image_bytes", img) for img in imgs])
-
-            gemini_output_content = self.model_call(gemini_input_content)
-            text_content = gemini_output_content["response_content"]
-            ########################## Implement the above code snippet ##############################
-            res.append(text_content)
-            pbar.update(1)
-        return res
-
-    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
-        # TODO
-        assert False, "Gemini Model not support"
diff --git a/lmms_eval/models/idefics2.py b/lmms_eval/models/idefics2.py
index e978f907..608d694b 100644
--- a/lmms_eval/models/idefics2.py
+++ b/lmms_eval/models/idefics2.py
@@ -203,6 +203,7 @@ def _collate(x):
                 gen_kwargs["max_new_tokens"] = 1024
             if "temperature" not in gen_kwargs:
                 gen_kwargs["temperature"] = 0
+
             prompts = []
             for context, visual in zip(contexts, visuals):
                 content = []
diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py
index 345f4863..b49cf55b 100755
--- a/lmms_eval/models/llava.py
+++ b/lmms_eval/models/llava.py
@@ -26,19 +26,11 @@
 try:
     from llava.model.builder import load_pretrained_model
     from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
-    from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
-    from llava.conversation import conv_templates, SeparatorStyle
+    from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
+    from llava.conversation import conv_templates
 except Exception as e:
     eval_logger.debug("LLaVA is not installed. Please install LLaVA to use this model.\nError: %s" % e)
 
-from transformers.integrations.deepspeed import (
-    is_deepspeed_zero3_enabled,
-    set_hf_deepspeed_config,
-    unset_hf_deepspeed_config,
-)
-
-from transformers.utils import is_flash_attn_2_available
-
 # inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5
 # if is_flash_attn_2_available:
 #     best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating
@@ -60,10 +52,7 @@ def __init__(
         pretrained: str = "liuhaotian/llava-v1.5-7b",
         truncation: Optional[bool] = True,
         device: Optional[str] = "cuda:0",
-        dtype: Optional[Union[str, torch.dtype]] = "auto",
         batch_size: Optional[Union[int, str]] = 1,
-        trust_remote_code: Optional[bool] = False,
-        revision=None,
         model_name=None,
         attn_implementation=best_fit_attn_implementation,
         device_map="cuda:0",
diff --git a/lmms_eval/models/llava_hf.py b/lmms_eval/models/llava_hf.py
index effb94fb..de0fb9ec 100644
--- a/lmms_eval/models/llava_hf.py
+++ b/lmms_eval/models/llava_hf.py
@@ -8,7 +8,7 @@
 from accelerate import Accelerator, DistributedType
 from accelerate.state import AcceleratorState
 from typing import List, Optional, Union, Tuple
-from transformers import LlavaForConditionalGeneration, AutoProcessor
+from transformers import LlavaForConditionalGeneration, LlavaNextForConditionalGeneration, AutoProcessor
 
 import warnings
 
@@ -31,10 +31,10 @@ class LlavaHf(lmms):
 
     Example usage:
 
-    accelerate launch --num_processes=8 -m lmms_eval \
+    accelerate launch --num_processes=8 --main_process_port 12345 -m lmms_eval \
         --model llava_hf \
         --model_args pretrained=llava-hf/llava-1.5-7b-hf \
-        --tasks mme \
+        --tasks seedbench \
         --batch_size 1 \
         --output_path ./logs/ \
         --log_samples
@@ -67,7 +67,16 @@ def __init__(
             self.device_map = device_map
         if isinstance(dtype, str) and dtype != "auto":
             dtype = getattr(torch, dtype)
-        self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation)
+
+        if "1.5" in pretrained:
+            self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation)
+        elif "1.6" in pretrained:
+            self._model = LlavaNextForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation)
+        else:
+            eval_logger.info("Not sure whether you use 1.5 or 1.6. Use 1.5 by default. This might cause bugs if you are actually using 1.6")
+            self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation)
+
+        self.pretrained = pretrained
         self._image_processor = AutoProcessor.from_pretrained(pretrained, revision=revision, trust_remote_code=trust_remote_code)
         # Pad from left for batched generation: https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/llava#usage-tips
         self._image_processor.tokenizer.padding_side = "left"
@@ -106,6 +115,7 @@ def __init__(
             self.model.to(self._device)
             self._rank = 0
             self._word_size = 1
+        self.accelerator = accelerator
 
     @property
     def config(self):
@@ -199,8 +209,8 @@ def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
             labels[: len(contxt_id)] = -100
 
             if self.accelerator.is_main_process and doc_id % 100 == 0:
-                eval_logger.info(f"Prompt for doc ID {doc_id}:\n\n{formatted_contexts[0]}\n")
-                eval_logger.info(f"Prompt and continuation for doc ID {doc_id}:\n\n{formatted_continuation[0]}\n")
+                eval_logger.debug(f"Prompt for doc ID {doc_id}:\n\n{formatted_contexts[0]}\n")
+                eval_logger.debug(f"Prompt and continuation for doc ID {doc_id}:\n\n{formatted_continuation[0]}\n")
 
             with torch.inference_mode():
                 outputs = self.model(**model_inputs, labels=labels)
@@ -268,7 +278,9 @@ def _collate(x):
 
             # Some benchmarks like MME do not contain image tokens, so we prepend them to the prompt.
             if DEFAULT_IMAGE_TOKEN not in context:
-                context = f"{DEFAULT_IMAGE_TOKEN}\n{context}"
+                image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals)
+                image_tokens = " ".join(image_tokens)
+                context = f"{image_tokens}\n{context}"
             # Apply chat template
             messages = [{"role": "user", "content": context}]
             if self.chat_template is not None:
@@ -281,7 +293,7 @@ def _collate(x):
                 text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
 
             if self.accelerator.is_main_process and doc_id[0] % 100 == 0:
-                eval_logger.info(f"Prompt for doc ID {doc_id[0]}:\n\n{text}\n")
+                eval_logger.debug(f"Prompt for doc ID {doc_id[0]}:\n\n{text}\n")
 
             inputs = self._image_processor(images=visuals, text=text, return_tensors="pt").to(self._device, self.model.dtype)
 
@@ -303,15 +315,21 @@ def _collate(x):
                     num_beams=gen_kwargs["num_beams"],
                     max_new_tokens=gen_kwargs["max_new_tokens"],
                     use_cache=self.use_cache,
+                    pad_token_id=self.tokenizer.eos_token_id,
                 )
             except Exception as e:
                 eval_logger.error(f"Error {e} in generating")
                 cont = ""
             text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True)[0]
-            text_outputs = text_outputs.split("ASSISTANT:")[-1].strip()
+            if "1.5" in self.pretrained:
+                text_outputs = text_outputs.split("ASSISTANT:")[-1].strip()
+            elif "mistral" in self.pretrained:
+                text_outputs = text_outputs.split("[/INST]")[-1].strip()
+            else:
+                text_outputs = text_outputs.split("ASSISTANT:")[-1].strip()
 
             if self.accelerator.is_main_process and doc_id[0] % 100 == 0:
-                eval_logger.info(f"Generated text for doc ID {doc_id[0]}:\n\n{text_outputs}\n")
+                eval_logger.debug(f"Generated text for doc ID {doc_id[0]}:\n\n{text_outputs}\n")
 
             res.append(text_outputs)
             self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs)
diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py
index abd42c36..cbbe7863 100755
--- a/lmms_eval/models/llava_vid.py
+++ b/lmms_eval/models/llava_vid.py
@@ -96,6 +96,7 @@ def __init__(
         self.mm_spatial_pool_out_channels = int(mm_spatial_pool_out_channels)
         self.mm_spatial_pool_mode = mm_spatial_pool_mode
         self.max_frames_num = int(max_frames_num)
+        print(self.max_frames_num)
         if self.overwrite == True:
             overwrite_config = {}
             overwrite_config["mm_resampler_type"] = self.mm_resampler_type
diff --git a/lmms_eval/models/mplugOwlVideo.py b/lmms_eval/models/mplug_owl_video.py
similarity index 100%
rename from lmms_eval/models/mplugOwlVideo.py
rename to lmms_eval/models/mplug_owl_video.py
diff --git a/lmms_eval/models/phi3v.py b/lmms_eval/models/phi3v.py
new file mode 100644
index 00000000..faa59ef5
--- /dev/null
+++ b/lmms_eval/models/phi3v.py
@@ -0,0 +1,219 @@
+import torch
+import logging
+
+from accelerate import Accelerator, DistributedType
+from lmms_eval import utils
+from lmms_eval.api.instance import Instance
+from lmms_eval.api.model import lmms
+from lmms_eval.api.registry import register_model
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM
+from transformers import AutoProcessor
+from typing import List, Optional, Tuple, Union
+
+eval_logger = logging.getLogger("lmms-eval")
+
+
+@register_model("phi3v")
+class Phi3v(lmms):
+    """
+    This class implements inference for the microsoft/Phi-3-vision-128k-instruct model.
+    To learn more about this model please visit the following links:
+    1. https://huggingface.co/microsoft/Phi-3-vision-128k-instruct
+    2. https://azure.microsoft.com/en-us/blog/new-models-added-to-the-phi-3-family-available-on-microsoft-azure/
+    3. https://github.com/microsoft/Phi-3CookBook
+
+    NOTE: This class was adapted from quen_vl.py and llava_hf.py.
+
+    Example:
+
+    accelerate launch --num_processes=4 -m lmms_eval --model phi3v --tasks mmmu_val \
+        --batch_size 1 --log_samples --log_samples_suffix phi3v_mmmu --output_path ./logs/
+    """
+
+    def __init__(
+        self,
+        model_id_name: str = "microsoft/Phi-3-vision-128k-instruct",
+        device: str = "cuda",
+        dtype: Optional[Union[str, torch.dtype]] = "auto",
+        batch_size: int = 1,
+        trust_remote_code: Optional[bool] = True,
+        use_cache: bool = True,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        # Do not use kwargs for now
+        assert kwargs == {}, f"Unexpected kwargs: {kwargs}"
+        # Setup accelerator.
+        accelerator = Accelerator()
+        if accelerator.num_processes > 1:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+        else:
+            self._device = device
+        # Load model.
+        self._model = AutoModelForCausalLM.from_pretrained(model_id_name, device_map=device, trust_remote_code=trust_remote_code, torch_dtype=dtype)
+        self._processor = AutoProcessor.from_pretrained(model_id_name, trust_remote_code=trust_remote_code)
+        self._processor.tokenizer.padding_side = "left"
+        self._tokenizer = self._processor.tokenizer
+        self._config = self._model.config
+        self.batch_size_per_gpu = int(batch_size)
+        assert self.batch_size_per_gpu == 1, "batch_size_per_gpu > 1 is not supported for now."
+        self.use_cache = use_cache
+        if accelerator.num_processes > 1:
+            distributed_type_list = [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED]
+            assert accelerator.distributed_type in distributed_type_list, "Unsupported distributed type provided. Only DDP and FSDP are supported."
+            if accelerator.distributed_type == DistributedType.FSDP:
+                self._model = accelerator.prepare(self.model)
+            else:
+                self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
+            self.accelerator = accelerator
+            if self.accelerator.is_local_main_process:
+                eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
+            self._rank = self.accelerator.local_process_index
+            self._world_size = self.accelerator.num_processes
+        else:
+            eval_logger.info(f"Using single device: {self._device}")
+            self.model.to(self._device)
+            self._rank = 0
+            self._word_size = 1
+
+    @property
+    def config(self):
+        # return the associated transformers.AutoConfig for the given pretrained model.
+        return self._config
+
+    @property
+    def tokenizer(self):
+        return self._tokenizer
+
+    @property
+    def model(self):
+        # returns the model, unwrapping it if using Accelerate
+        if hasattr(self, "accelerator"):
+            return self.accelerator.unwrap_model(self._model)
+        else:
+            return self._model
+
+    @property
+    def eot_token_id(self):
+        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+        return self.tokenizer.eos_token_id
+
+    @property
+    def max_length(self):
+        return self._max_length
+
+    @property
+    def batch_size(self):
+        return self.batch_size_per_gpu
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def rank(self):
+        return self._rank
+
+    @property
+    def world_size(self):
+        return self._world_size
+
+    def flatten(self, input):
+        new_list = []
+        for i in input:
+            for j in i:
+                new_list.append(j)
+        return new_list
+
+    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        raise NotImplementedError("Not implemented for Phi3v.")
+
+    def generate_until(self, requests: List[Instance]) -> List[str]:
+        res = []
+
+        def _collate(x):
+            # the negative sign on len(toks) sorts descending - this has a few advantages:
+            # - time estimates will always be over not underestimates, which is more useful for planning
+            # - to know the size of a batch when going through the list, you know the first one is always the batch
+            #   padded context length. this is useful to simplify the batching logic and more importantly to make
+            #   automatic adaptive batches much much easier to implement
+            # - any OOMs will happen right away rather than near the end
+            toks = self.tokenizer.encode(x[0])
+            return -len(toks), x[0]
+
+        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
+        # we group requests by their generation_kwargs,
+        # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling
+        # in the same batch.
+        re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True)
+        chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None)
+        for chunk in chunks:
+            contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk)
+            task = task[0]
+            split = split[0]
+            visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id]
+            visuals = self.flatten(visuals)
+            # We assume all gen kwargs in the batch are the same
+            # this is safe to assume because the `grouper` object ensures it.
+            gen_kwargs = all_gen_kwargs[0]
+            # Set default values for until and max_new_tokens
+            until = [self.tokenizer.decode(self.eot_token_id)]
+            # Update values from gen_kwargs if present
+            if "until" in gen_kwargs:
+                until = gen_kwargs.pop("until")
+                if isinstance(until, str):
+                    until = [until]
+                elif not isinstance(until, list):
+                    raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}")
+            if isinstance(contexts, tuple):
+                contexts = list(contexts)
+            for i in range(len(contexts)):
+                if "<image>" in contexts[i]:
+                    query = "" + contexts[i]
+                    img_placeholder_count = 1
+                    while "<image>" in query:
+                        query = query.replace("<image>", f"<|image_{img_placeholder_count}|>", 1)
+                        img_placeholder_count += 1
+                else:
+                    query = ""
+                    for placeholder_id in range(len(visuals)):
+                        query += f"<|image_{placeholder_id+1}|>\n"
+                    query += contexts[i]
+                messages = [{"role": "user", "content": query}]
+                contexts[i] = self._tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+            assert len(contexts) == 1
+            #
+            context = contexts[0]
+            input_ids = self._processor(text=context, images=visuals, return_tensors="pt").to(self._device, self.model.dtype)
+            # Setting default parameters.
+            if "max_new_tokens" not in gen_kwargs:
+                gen_kwargs["max_new_tokens"] = 1024
+            if "temperature" not in gen_kwargs:
+                gen_kwargs["temperature"] = 0
+            if "top_p" not in gen_kwargs:
+                gen_kwargs["top_p"] = None
+            if "num_beams" not in gen_kwargs:
+                gen_kwargs["num_beams"] = 1
+            # Generate answer.
+            pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eod_id
+            generate_ids = self.model.generate(
+                **input_ids,
+                eos_token_id=self.tokenizer.eos_token_id,
+                pad_token_id=pad_token_id,
+                do_sample=True if gen_kwargs["temperature"] > 0 else False,
+                temperature=gen_kwargs["temperature"],
+                top_p=gen_kwargs["top_p"],
+                num_beams=gen_kwargs["num_beams"],
+                max_new_tokens=gen_kwargs["max_new_tokens"],
+                use_cache=self.use_cache,
+            )
+            generate_ids = generate_ids[:, input_ids["input_ids"].shape[1] :]
+            response = self._processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+            res.append(response)
+            self.cache_hook.add_partial("generate_until", (context, gen_kwargs), response)
+            pbar.update(1)
+        # reorder this group of results back to original unsorted form
+        res = re_ords.get_original(res)
+        pbar.close()
+        return res
diff --git a/lmms_eval/models/llava_onevision.py b/lmms_eval/models/tinyllava.py
similarity index 55%
rename from lmms_eval/models/llava_onevision.py
rename to lmms_eval/models/tinyllava.py
index a755b0c9..e2ca4bdf 100755
--- a/lmms_eval/models/llava_onevision.py
+++ b/lmms_eval/models/tinyllava.py
@@ -1,51 +1,36 @@
-from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
-from accelerate.state import AcceleratorState
-from transformers import AutoConfig
-
-import math
 import torch
 
 torch.backends.cuda.matmul.allow_tf32 = True
 
+import logging
+import copy
 from tqdm import tqdm
 from datetime import timedelta
-from decord import VideoReader, cpu
-import numpy as np
 
-import copy
-import PIL
+from lmms_eval import utils
+from lmms_eval.api.instance import Instance
+from lmms_eval.api.model import lmms
+from lmms_eval.api.registry import register_model
+from lmms_eval.utils import stop_sequences_criteria
+
+from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
+from accelerate.state import AcceleratorState
 from typing import List, Optional, Union, Tuple
 from packaging import version
 import warnings
-import logging
 
 warnings.filterwarnings("ignore")
 
 eval_logger = logging.getLogger("lmms-eval")
 
-from lmms_eval import utils
-from lmms_eval.api.instance import Instance
-from lmms_eval.api.model import lmms
-from lmms_eval.api.registry import register_model
-from lmms_eval.models.model_utils.load_video import read_video_pyav
-
 try:
-    from llava.model.builder import load_pretrained_model
-    from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token, KeywordsStoppingCriteria
-    from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
-    from llava.conversation import conv_templates, SeparatorStyle
-
+    from tinyllava.model import load_pretrained_model
+    from tinyllava.data import ImagePreprocess, TextPreprocess
+    from tinyllava.utils.constants import DEFAULT_IMAGE_TOKEN
+    from tinyllava.utils.message import Message
 except Exception as e:
-    eval_logger.debug("LLaVA is not installed. Please install LLaVA to use this model.\nError: %s" % e)
-
-try:
-    from llavavid.model.language_model.llava_qwen import LlavaQwenConfig
-    from llavavid.model.language_model.llava_llama import LlavaConfig
+    eval_logger.debug("TinyLLaVA_Factory is not installed. Please install TinyLLaVA_Factory to use this model.\nError: %s" % e)
 
-    AutoConfig.register("llava_qwen", LlavaQwenConfig)
-    AutoConfig.register("llava_llama", LlavaConfig)
-except Exception as e:
-    eval_logger.debug("")
 # inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5
 # if is_flash_attn_2_available:
 #     best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating
@@ -56,30 +41,20 @@
     best_fit_attn_implementation = "eager"
 
 
-@register_model("llava_onevision")
-class Llava_OneVision(lmms):
+@register_model("tinyllava")
+class TinyLlava(lmms):
     """
-    Llava Model
+    TinyLlava Model
     """
 
     def __init__(
         self,
-        pretrained: str = "liuhaotian/llava-v1.5-7b",
-        truncation: Optional[bool] = True,
+        pretrained: str = "tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B",
         device: Optional[str] = "cuda:0",
         batch_size: Optional[Union[int, str]] = 1,
-        model_name: Optional[str] = None,
-        attn_implementation: Optional[str] = best_fit_attn_implementation,
-        device_map: Optional[str] = "cuda:0",
-        conv_template: Optional[str] = "vicuna_v1",
-        use_cache: Optional[bool] = True,
-        truncate_context: Optional[bool] = False,  # whether to truncate the context in generation, set it False for LLaVA-1.6
-        customized_config: Optional[str] = None,  # ends in json
-        max_frames_num: Optional[int] = 32,
-        mm_spatial_pool_stride: Optional[int] = 2,
-        mm_spatial_pool_mode: Optional[str] = "average",
-        token_strategy: Optional[str] = "single",  # could be "single" or "multiple", "multiple" denotes adding multiple <image> tokens for each frame
-        video_decode_backend: str = "pyav",
+        device_map="cuda:0",
+        conv_mode="phi",  # TODO
+        use_cache=True,
         **kwargs,
     ) -> None:
         super().__init__()
@@ -98,60 +73,22 @@ def __init__(
             self._device = torch.device(f"cuda:{accelerator.local_process_index}")
             self.device_map = f"cuda:{accelerator.local_process_index}"
 
-        llava_model_args = {
-            "multimodal": True,
-        }
-        if customized_config is not None:
-            llava_model_args["customized_config"] = customized_config
-        if attn_implementation is not None:
-            llava_model_args["attn_implementation"] = attn_implementation
-        if "use_flash_attention_2" in kwargs:
-            llava_model_args["use_flash_attention_2"] = kwargs["use_flash_attention_2"]
-        model_name = model_name if model_name is not None else get_model_name_from_path(pretrained)
-
-        self.pretrained = pretrained
-        self.token_strategy = token_strategy
-        self.max_frames_num = max_frames_num
-        self.mm_spatial_pool_stride = mm_spatial_pool_stride
-        self.mm_spatial_pool_mode = mm_spatial_pool_mode
-        self.video_decode_backend = video_decode_backend
-
-        overwrite_config = {}
-        overwrite_config["mm_spatial_pool_stride"] = self.mm_spatial_pool_stride
-        overwrite_config["mm_spatial_pool_mode"] = self.mm_spatial_pool_mode
-        cfg_pretrained = AutoConfig.from_pretrained(self.pretrained)
-
-        if cfg_pretrained.architectures[0] == "LlavaLlamaForCausalLM":  # Ugly code, only used in  vicuna that needs ROPE
-            if "224" in cfg_pretrained.mm_vision_tower:
-                least_token_number = self.max_frames_num * (16 // self.mm_spatial_pool_stride) ** 2 + 1000
-            else:
-                least_token_number = self.max_frames_num * (24 // self.mm_spatial_pool_stride) ** 2 + 1000
-
-            scaling_factor = math.ceil(least_token_number / 4096)
-            if scaling_factor >= 2:
-                overwrite_config["rope_scaling"] = {"factor": float(scaling_factor), "type": "linear"}
-                overwrite_config["max_sequence_length"] = 4096 * scaling_factor
-                overwrite_config["tokenizer_model_max_length"] = 4096 * scaling_factor
-
-        llava_model_args["overwrite_config"] = overwrite_config
-        try:
-            # Try to load the model with the multimodal argument
-            self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args)
-        except TypeError:
-            # for older versions of LLaVA that don't have multimodal argument
-            llava_model_args.pop("multimodal", None)
-            self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args)
+        self._model, self._tokenizer, self._image_processor, self._max_length = load_pretrained_model(pretrained, device_map=self.device_map)
+        data_args = self._model.config
+        self._image_processor = ImagePreprocess(self._image_processor, data_args)
+        assert self._tokenizer.padding_side == "right", "Not sure but seems like `right` is a natural choice for padding?"
+        self._text_processor = TextPreprocess(self._tokenizer, conv_mode)
 
         self._config = self._model.config
         self.model.eval()
         self.model.tie_weights()
-        self.truncation = truncation
+        # self.truncation = truncation
         self.batch_size_per_gpu = int(batch_size)
-        self.conv_template = conv_template
+        # self.conv_template = conv_template
         self.use_cache = use_cache
-        self.truncate_context = truncate_context
-        assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue."
+        # self.truncate_context = truncate_context
 
+        # assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue."
         if accelerator.num_processes > 1:
             assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
             # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
@@ -174,12 +111,10 @@ def __init__(
                 eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
             self._rank = self.accelerator.local_process_index
             self._world_size = self.accelerator.num_processes
-
         elif accelerator.num_processes == 1 and device_map == "auto":
             eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism")
             self._rank = 0
             self._word_size = 1
-
         else:
             eval_logger.info(f"Using single device: {self._device}")
             self.model.to(self._device)
@@ -251,6 +186,13 @@ def tok_decode(self, tokens):
         except:
             return self.tokenizer.decode([tokens])
 
+    def flatten(self, input):
+        new_list = []
+        for i in input:
+            for j in i:
+                new_list.append(j)
+        return new_list
+
     def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
         # TODO
         res = []
@@ -266,9 +208,13 @@ def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
             visuals = self.flatten(visuals)
             image_sizes = [[visual.size[0], visual.size[1]] for visual in visuals]
             if visuals:
-                image = process_images(visuals, self._image_processor, self._config)
+                # https://github.com/zjysteven/TinyLLaVA_Factory/blob/main/tinyllava/data/image_preprocess.py
+                # tinyllava's image processor seems to take each individual image as input
+                image = [self._image_processor(v) for v in visuals]
                 if type(image) is list:
                     image = [_image.to(dtype=torch.float16, device=self.device) for _image in image]
+                    # as of 2024/06, tinyllava only accepts `images` input to be a tensor
+                    image = torch.stack(image)
                 else:
                     image = image.to(dtype=torch.float16, device=self.device)
             else:
@@ -287,25 +233,17 @@ def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
                 image_tokens = " ".join(image_tokens)
                 prompts_input = image_tokens + "\n" + (contexts[0] if isinstance(contexts, list) else contexts)
 
-            # This is much safer for llama3, as we now have some object type in it
-            if "llama_3" in self.conv_template:
-                conv = copy.deepcopy(conv_templates[self.conv_template])
-            else:
-                conv = conv_templates[self.conv_template].copy()
-
-            conv.append_message(conv.roles[0], prompts_input)
-            conv.append_message(conv.roles[1], None)
-            prompt = conv.get_prompt()
-            pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
-            contxt_id = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device)
+            msg = Message()
+            msg.add_message(prompts_input)
+            contxt_id = self._text_processor(msg.messages, mode="eval")["input_ids"]
             # Add the answer of the second role
-            conv.messages[1][1] = continuation
+            msg._messages[1]["value"] = continuation
+            input_ids = self._text_processor(msg.messages, mode="eval")["input_ids"]
 
-            prompt = conv.get_prompt()
-            input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device)
             labels = input_ids.clone()
             # Context part no need to calculate for loss
             labels[0, : contxt_id.shape[1]] = -100
+
             with torch.inference_mode():
                 outputs = self.model(input_ids=input_ids, labels=labels, images=image, use_cache=True, image_sizes=image_sizes)
             loss = outputs["loss"]
@@ -317,28 +255,9 @@ def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
             max_equal = (greedy_tokens == cont_toks).all()
             res.append((float(loss.item()), bool(max_equal)))
             pbar.update(1)
-
         pbar.close()
         return res
 
-    def flatten(self, input):
-        new_list = []
-        for i in input:
-            for j in i:
-                new_list.append(j)
-        return new_list
-
-    def load_video(self, video_path, max_frames_num):
-        if type(video_path) == str:
-            vr = VideoReader(video_path, ctx=cpu(0))
-        else:
-            vr = VideoReader(video_path[0], ctx=cpu(0))
-        total_frame_num = len(vr)
-        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int)
-        frame_idx = uniform_sampled_frames.tolist()
-        spare_frames = vr.get_batch(frame_idx).asnumpy()
-        return spare_frames  # (frames, height, width, channels)
-
     def generate_until(self, requests: List[Instance]) -> List[str]:
         res = []
 
@@ -360,120 +279,130 @@ def _collate(x):
         num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1
         pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding")
         for chunk in chunks:
-            batched_contexts, all_gen_kwargs, batched_doc_to_visual, batched_doc_id, batched_task, batched_split = zip(*chunk)
-            task = batched_task[0]
-            split = batched_split[0]
-            batched_visuals = [batched_doc_to_visual[0](self.task_dict[task][split][ids]) for ids in batched_doc_id]  # [B, N]
-            assert len(batched_visuals) == 1
-
+            contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk)
+            task = task[0]
+            split = split[0]
+            batched_visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id]  # [B, N]
+            flattened_visuals = self.flatten(batched_visuals)  # [B*N]
             # we assume all gen kwargs in the batch are the same
             # this is safe to assume because the `grouper` object ensures it.
             gen_kwargs = all_gen_kwargs[0]
+
+            # Set default values for until and max_new_tokens
+            until = [self.tok_decode(self.eot_token_id)]
+
+            # Update values from gen_kwargs if present
             if "until" in gen_kwargs:
-                gen_kwargs.pop("until")
+                until = gen_kwargs.pop("until")
+                if isinstance(until, str):
+                    until = [until]
+                elif not isinstance(until, list):
+                    raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}")
+
+            if "image_aspect_ratio" in gen_kwargs.keys() and "image_aspect_ratio" not in self._config.__dict__:
+                # here we should pop it out of gen_kwargs so that it doesn't get passed to the model for next step of generation
+                self._config.image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio")
+                eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}")
+            # encode, pad, and truncate contexts for this batch
+            if flattened_visuals:
+                image_tensor = [self._image_processor(v) for v in flattened_visuals]
+                if type(image_tensor) is list:
+                    image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor]
+                    # as of 2024/06, tinyllava only accepts `images` input to be a tensor
+                    image_tensor = torch.stack(image_tensor)
+                else:
+                    image_tensor = image_tensor.to(dtype=torch.float16, device=self.device)
+            else:
+                image_tensor = None
 
-            question_input = []
+            # prompts_input = contexts[0]
 
-            for visual, context in zip(batched_visuals, batched_contexts):
-                if "image_aspect_ratio" in gen_kwargs.keys() and "image_aspect_ratio" not in self._config.__dict__:
-                    # here we should pop it out of gen_kwargs so that it doesn't get passed to the model for next step of generation
-                    self._config.image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio")
-                    eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}")
-
-                # encode, pad, and truncate contexts for this batch
-                if type(visual[0]) == PIL.Image.Image:  # For image task
-                    image_tensor = process_images(visual, self._image_processor, self._config)
-                    if type(image_tensor) is list:
-                        image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor]
-                    else:
-                        image_tensor = image_tensor.to(dtype=torch.float16, device=self.device)
-
-                    task_type = "image"
-
-                elif type(visual[0]) == str:  # For video task
-                    image_tensor = []
-                    try:
-                        if self.video_decode_backend == "decord":
-                            frames = self.load_video(visual, self.max_frames_num)
-                        elif self.video_decode_backend == "pyav":
-                            frames = read_video_pyav(visual[0], num_frm=self.max_frames_num)
-                        frames = self._image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].half().cuda()
-                        image_tensor.append(frames)
-                    except Exception as e:
-                        eval_logger.error(f"Error {e} in loading video")
-                        image_tensor = None
-
-                    task_type = "video"
+            question_input = []
 
+            for visual, context in zip(batched_visuals, contexts):
                 if image_tensor is not None and len(image_tensor) != 0 and DEFAULT_IMAGE_TOKEN not in context:
                     """
                     Three senarios:
                     1. No image, and there for, no image token should be added.
                     2. image token is already specified in the context, so we don't need to add it.
                     3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line.
-                    4. For video tasks, we could add a <image> token or multiple <image> tokens for each frame in the context. This depends on the training strategy and should balance in test to decide which is better
                     """
-                    if task_type == "image":
-                        image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visual) if isinstance(visual, list) else [DEFAULT_IMAGE_TOKEN]
-                    elif task_type == "video":
-                        image_tokens = [DEFAULT_IMAGE_TOKEN] * len(frames) if self.token_strategy == "multiple" else [DEFAULT_IMAGE_TOKEN]
-
+                    image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visual) if isinstance(visual, list) else [DEFAULT_IMAGE_TOKEN]
                     image_tokens = " ".join(image_tokens)
                     question = image_tokens + "\n" + context
                 else:
                     question = context
 
-                # This is much safer for llama3, as we now have some object type in it
-                if "llama_3" in self.conv_template:
-                    conv = copy.deepcopy(conv_templates[self.conv_template])
-                else:
-                    conv = conv_templates[self.conv_template].copy()
-                conv.append_message(conv.roles[0], question)
-                conv.append_message(conv.roles[1], None)
-                prompt_question = conv.get_prompt()
+                msg = Message()
+                msg.add_message(question)
+                prompt_question = self._text_processor(msg.messages, mode="eval")["prompt"]
                 question_input.append(prompt_question)
 
+            # The above for loop has bugs. When there is no visuals, e.g. pure text,
+            # there will be no for loop execute resulting in an empty question_input (because no visuals)
+            # Scenario 1 won't even be execute
+            if len(flattened_visuals) == 0:
+                for context in contexts:
+                    question = context
+                    msg = Message()
+                    msg.add_message(question)
+                    prompt_question = self._text_processor(msg.messages, mode="eval")["prompt"]
+                    question_input.append(prompt_question)
+
+            # input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device)
             # preconfigure gen_kwargs with defaults
+            gen_kwargs["image_sizes"] = [flattened_visuals[idx].size for idx in range(len(flattened_visuals))]
             if "max_new_tokens" not in gen_kwargs:
                 gen_kwargs["max_new_tokens"] = 1024
             if "temperature" not in gen_kwargs:
                 gen_kwargs["temperature"] = 0
-            if "do_sample" not in gen_kwargs:
-                gen_kwargs["do_sample"] = False
             if "top_p" not in gen_kwargs:
                 gen_kwargs["top_p"] = None
             if "num_beams" not in gen_kwargs:
                 gen_kwargs["num_beams"] = 1
 
-            input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input]
+            # input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input]
+            input_ids_list = [self._text_processor.template.tokenizer_image_token(prompt, self.tokenizer, return_tensors="pt") for prompt in question_input]
             pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
             input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device)
             attention_masks = input_ids.ne(pad_token_ids).to(self.device)
-
-            if task_type == "image":
-                gen_kwargs["image_sizes"] = [batched_visuals[idx][0].size for idx in range(len(batched_visuals))]
-            elif task_type == "video":
-                stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
-                keywords = [stop_str]
-                stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids)
-                gen_kwargs["modalities"] = ["video"]
-                gen_kwargs["stopping_criteria"] = [stopping_criteria]
-                self._config.mm_spatial_pool_stride = self.mm_spatial_pool_stride
-                self._config.mm_spatial_pool_mode = self.mm_spatial_pool_mode
-
             # These steps are not in LLaVA's original code, but are necessary for generation to work
             # TODO: attention to this major generation step...
             if "image_aspect_ratio" in gen_kwargs.keys():
-                gen_kwargs.pop("image_aspect_ratio") 
+                gen_kwargs.pop("image_aspect_ratio")
             try:
-                with torch.inference_mode():
-                    cont = self.model.generate(input_ids, attention_mask=attention_masks, pad_token_id=pad_token_ids, images=image_tensor, use_cache=self.use_cache, **gen_kwargs)
-
+                cont = self.model.generate(
+                    input_ids,
+                    attention_mask=attention_masks,
+                    pad_token_id=pad_token_ids,
+                    images=image_tensor,
+                    image_sizes=gen_kwargs["image_sizes"],
+                    do_sample=True if gen_kwargs["temperature"] > 0 else False,
+                    temperature=gen_kwargs["temperature"],
+                    top_p=gen_kwargs["top_p"],
+                    num_beams=gen_kwargs["num_beams"],
+                    max_new_tokens=gen_kwargs["max_new_tokens"],
+                    use_cache=self.use_cache,
+                )
                 text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True)
             except Exception as e:
                 raise e
-
-            text_outputs = [response.strip() for response in text_outputs]
+                eval_logger.error(f"Error {e} in generating")
+                cont = ""
+                text_outputs = [""]
+
+            # cont_toks_list = cont.tolist()
+            # for cont_toks, context in zip(cont_toks_list, contexts):
+            # discard context + left-padding toks if using causal decoder-only LMM
+            # if self.truncate_context:
+            #     cont_toks = cont_toks[input_ids.shape[1] :]
+            # use secondary stop seqs to cut off should-have-been-stopped content post-hoc
+            # if self.truncate_context:
+            #     for term in until:
+            #         if len(term) > 0:
+            #             # ignore '' separator,
+            #             # for seq2seq case where self.tok_decode(self.eot_token_id) = ''
+            #             text_outputs = text_outputs.split(term)[0]
             res.extend(text_outputs)
             self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs)
             pbar.update(1)
diff --git a/lmms_eval/models/videoChatGPT.py b/lmms_eval/models/video_chatgpt.py
similarity index 100%
rename from lmms_eval/models/videoChatGPT.py
rename to lmms_eval/models/video_chatgpt.py
diff --git a/lmms_eval/models/xcomposer2_4khd.py b/lmms_eval/models/xcomposer2_4khd.py
new file mode 100644
index 00000000..b43f12e4
--- /dev/null
+++ b/lmms_eval/models/xcomposer2_4khd.py
@@ -0,0 +1,295 @@
+from multiprocessing import context
+import torch
+from transformers import AutoModel, AutoTokenizer
+from PIL import Image
+import numpy as np
+import torchvision.transforms as transforms
+from datetime import timedelta
+import logging
+
+from lmms_eval import utils
+from lmms_eval.api.instance import Instance
+from lmms_eval.api.model import lmms
+from lmms_eval.api.registry import register_model
+from lmms_eval.utils import stop_sequences_criteria
+
+from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
+from accelerate.state import AcceleratorState
+
+from typing import Optional, Sequence, List, Tuple, Union
+import re
+from tqdm import tqdm
+
+pattern = re.compile(r"[A-Z]")
+
+eval_logger = logging.getLogger("lmms-eval")
+
+meta_instruction = """You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔).
+- InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed\
+ by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
+- InternLM-XComposer (浦语·灵笔) can understand and communicate fluently in the language chosen by\
+ the user such as English and 中文.
+- InternLM-XComposer (浦语·灵笔) is capable of comprehending and articulating responses\
+ effectively based on the provided image."""
+
+
+@register_model("xcomposer2_4khd")
+class XComposer2_4KHD(lmms):
+    def __init__(
+        self,
+        pretrained: str = "internlm/internlm-xcomposer2-4khd-7b",
+        device: Optional[str] = "cuda:0",
+        batch_size: Optional[Union[int, str]] = 1,
+        device_map="cuda:0",
+        need_bos: bool = True,
+        padding: bool = False,
+        half: bool = False,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+
+        accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52))
+        accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs])
+        if accelerator.num_processes > 1:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            self._device = torch.device(device)
+            self.device_map = device_map
+        else:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+
+        self.pretrained = pretrained
+        self.need_bos = need_bos
+        self.padding = padding
+        self._model = AutoModel.from_pretrained(self.pretrained, device_map=self.device_map, trust_remote_code=True)
+        self._tokenizer = AutoTokenizer.from_pretrained(self.pretrained, trust_remote_code=True)
+        self.model.tokenizer = self.tokenizer
+        self.batch_size_per_gpu = batch_size
+
+        if accelerator.num_processes > 1:
+            assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
+            # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
+            # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works
+            # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work.
+            if accelerator.distributed_type == DistributedType.DEEPSPEED:
+                kwargs = {
+                    "train_micro_batch_size_per_gpu": self.batch_size_per_gpu,
+                    "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes,
+                }
+                AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs)
+                eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0")
+            if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED:
+                self._model = accelerator.prepare(self.model)
+            else:
+                self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
+            self.accelerator = accelerator
+            if self.accelerator.is_local_main_process:
+                eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
+            self._rank = self.accelerator.local_process_index
+            self._world_size = self.accelerator.num_processes
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism")
+            self._rank = 0
+            self._word_size = 1
+        else:
+            eval_logger.info(f"Using single device: {self._device}")
+            self.model.to(self._device)
+            self._rank = 0
+            self._world_size = 1
+
+    @property
+    def config(self):
+        # return the associated transformers.AutoConfig for the given pretrained model.
+        return self._config
+
+    @property
+    def tokenizer(self):
+        return self._tokenizer
+
+    @property
+    def model(self):
+        # returns the model, unwrapping it if using Accelerate
+        if hasattr(self, "accelerator"):
+            return self.accelerator.unwrap_model(self._model)
+        else:
+            return self._model
+
+    @property
+    def batch_size(self):
+        return self.batch_size_per_gpu
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def rank(self):
+        return self._rank
+
+    @property
+    def world_size(self):
+        return self._world_size
+
+    def flatten(self, input):
+        new_list = []
+        for i in input:
+            for j in i:
+                new_list.append(j)
+        return new_list
+
+    def generate_until(self, requests) -> List[str]:
+        res = []
+        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
+
+        for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
+            # encode, pad, and truncate contexts for this batch
+            if "[UNUSED_TOKEN_146]" not in contexts:
+                contexts = f"[UNUSED_TOKEN_146]user\n{contexts}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
+            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
+            visuals = self.flatten(visuals)
+
+            if "hd_num" not in gen_kwargs:
+                if listinstr(["docvqa_test", "infovqa_test"], task.lower()):
+                    self.model.hd_num = 65
+                elif listinstr(["docvqa_val", "infovqa_val", "OCRBench"], task.lower()):
+                    self.model.hd_num = 55
+                elif listinstr(["mmmu", "mmbench", "mmvet"], task.lower()):
+                    self.model.hd_num = 16
+                else:
+                    self.model.hd_num = 25
+            else:
+                self.model.hd_num = gen_kwargs.pop("hd_num")
+
+            pt1 = 0
+            embeds = []
+            im_mask = []
+            images_loc = [0]
+            need_bos = self.need_bos
+            padding = self.padding
+            for i, pts in enumerate(images_loc + [len(contexts)]):
+                subtext = contexts[pt1:pts]
+                if need_bos or len(subtext) > 0:
+                    text_embeds = self.model.encode_text(subtext, add_special_tokens=need_bos).to(self.device)
+                    embeds.append(text_embeds)
+                    im_mask.append(torch.zeros(text_embeds.shape[:2]).to(self.device))
+                    need_bos = False
+                if i < len(visuals):
+                    image = visuals[i]
+
+                    image = HD_transform(image, im_num=self.model.hd_num)
+                    image = self.model.vis_processor(image).unsqueeze(0).to(self.device)
+                    image_embeds = self.model.encode_img(image)
+                    embeds.append(image_embeds)
+                    im_mask.append(torch.ones(image_embeds.shape[:2]).to(self.device))
+                pt1 = pts
+            embeds = torch.cat(embeds, dim=1)
+            im_mask = torch.cat(im_mask, dim=1)
+            im_mask = im_mask.bool()
+
+            if "max_new_tokens" not in gen_kwargs:
+                gen_kwargs["max_new_tokens"] = 1024
+            if "temperature" not in gen_kwargs:
+                gen_kwargs["temperature"] = 0
+            if "top_p" not in gen_kwargs:
+                gen_kwargs["top_p"] = None
+            if "num_beams" not in gen_kwargs:
+                gen_kwargs["num_beams"] = 1
+            if "do_sample" not in gen_kwargs:
+                gen_kwargs["do_sample"] = False
+            if "repetition_penalty" not in gen_kwargs:
+                gen_kwargs["repetition_penalty"] = 1.0
+
+            outputs = self.model.generate(
+                inputs_embeds=embeds,
+                im_mask=im_mask,
+                temperature=gen_kwargs["temperature"],
+                max_new_tokens=gen_kwargs["max_new_tokens"],
+                num_beams=gen_kwargs["num_beams"],
+                do_sample=gen_kwargs["do_sample"],
+                repetition_penalty=gen_kwargs["repetition_penalty"],
+            )
+            output_token = outputs[0]
+            if output_token[0] == 0 or output_token[0] == 1:
+                output_token = output_token[1:]
+            output_text = self.model.tokenizer.decode(output_token, add_special_tokens=False)
+            output_text = output_text.split("[UNUSED_TOKEN_145]")[0].strip()
+            output_text = output_text.split("<|im_end|>")[0].strip()
+            # if DATASET_TYPE(task) == "multi-choice":
+            #     output_text = pattern.findall(output_text)
+            #     if len(output_text) == 0:
+            #         print("Error:", output_text)
+            #         output_text = "Z"
+            #     if type(output_text) == list:
+            #         output_text = output_text[0]
+            res.append(output_text)
+            pbar.update(1)
+        pbar.close()
+        return res
+
+    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        return super().loglikelihood(requests)
+
+
+def padding_336(b):
+    width, height = b.size
+    tar = int(np.ceil(height / 336) * 336)
+    top_padding = int((tar - height) / 2)
+    bottom_padding = tar - height - top_padding
+    left_padding = 0
+    right_padding = 0
+    b = transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255, 255, 255])
+
+    return b
+
+
+def HD_transform(img, im_num=16):
+    width, height = img.size
+    trans = False
+    if width < height:
+        img = img.transpose(Image.TRANSPOSE)
+        trans = True
+        width, height = img.size
+    ratio = width / height
+    scale = 1
+    while scale * np.ceil(scale / ratio) <= im_num:
+        scale += 1
+    scale -= 1
+    new_w = int(scale * 336)
+    new_h = int(new_w / ratio)
+
+    img = transforms.functional.resize(
+        img,
+        [new_h, new_w],
+    )
+    img = padding_336(img)
+    width, height = img.size
+    assert width * height <= im_num * 336 * 336
+    if trans:
+        img = img.transpose(Image.TRANSPOSE)
+
+    return img
+
+
+def listinstr(lst, s):
+    assert isinstance(lst, list)
+    for item in lst:
+        if item in s:
+            return True
+    return False
+
+
+def DATASET_TYPE(dataset):
+    # Dealing with Custom Dataset
+    dataset = dataset.lower()
+    if listinstr(["mmbench", "seedbench", "ccbench", "mmmu", "scienceqa", "ai2d", "mmstar"], dataset):
+        return "multi-choice"
+    elif listinstr(["mme", "hallusion"], dataset):
+        return "Y/N"
+    elif "coco" in dataset:
+        return "Caption"
+    elif listinstr(["ocrvqa", "textvqa", "chartqa", "mathvista", "docvqa", "infovqa", "llavabench", "mmvet", "ocrbench"], dataset):
+        return "VQA"
+    else:
+        return "QA"
diff --git a/lmms_eval/tasks/conbench/conbench.yaml b/lmms_eval/tasks/conbench/conbench.yaml
new file mode 100644
index 00000000..3739282a
--- /dev/null
+++ b/lmms_eval/tasks/conbench/conbench.yaml
@@ -0,0 +1,24 @@
+dataset_path: ConBench/ConBench_D
+dataset_kwargs:
+  token: True
+task: "ConBench"
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.conbench_doc_to_visual
+doc_to_text: !function utils.conbench_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  max_new_tokens: 1024
+  temperature: 0.2
+  top_p: 0
+  num_beams: 1
+  do_sample: True
+# The return value of process_results will be used by metrics
+process_results: !function utils.conbench_process_results
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: ConScore_D
+    aggregation: !function utils.conbench_aggregate_results
+    higher_is_better: true
+metadata:
+  - version: 0.0
diff --git a/lmms_eval/tasks/conbench/utils.py b/lmms_eval/tasks/conbench/utils.py
new file mode 100644
index 00000000..bf7e1090
--- /dev/null
+++ b/lmms_eval/tasks/conbench/utils.py
@@ -0,0 +1,98 @@
+from collections import defaultdict
+import os
+from anls import anls_score
+
+import logging
+
+eval_logger = logging.getLogger("lmms-eval")
+
+dir_name = os.path.dirname(os.path.abspath(__file__))
+
+# 19 classes
+eval_type_dict = {
+    "Sensation": ["count", "color", "scene", "poster", "attribute_recognition", "ocr", "position"],
+    "Cognition": ["calculation", "code", "translation", "math", "cross_instance_reason", "attribute_reason"],
+    "Knowledge": ["celebrity", "chemistry", "physics", "biology", "landmark", "artwork"],
+}
+
+
+def conbench_doc_to_visual(doc):
+    return [doc["image"].convert("RGB")]
+
+
+def conbench_doc_to_text(doc):
+    question = doc["question"].strip()
+    return question
+
+
+def parse_pred_ans_NY(pred_ans):
+    pred_label = None
+    if pred_ans in ["yes", "no"]:
+        pred_label = pred_ans
+    else:
+        prefix_pred_ans = pred_ans[:4]
+
+        if "yes" in prefix_pred_ans:
+            pred_label = "yes"
+        elif "no" in prefix_pred_ans:
+            pred_label = "no"
+        else:
+            pred_label = "other"
+    return pred_label
+
+
+def parse_pred_ans_choice(pred_ans):
+    return pred_ans.replace(" ", "")[0]
+
+
+def conbench_process_results(doc, results):
+    """
+    Args:
+        doc: a instance of the eval dataset
+        results: [pred]
+    Returns:
+        a dictionary with key: metric name (in this case mme score), value: metric value
+    """
+    pred = results[0]
+    pred = pred.replace("\n", "").lower()
+    # parser
+    if doc["question_field"] == "N/Y":
+        pred_ans = parse_pred_ans_NY(pred)
+    elif doc["question_field"] == "Choices":
+        pred_ans = parse_pred_ans_choice(pred)
+    else:
+        pred_ans = pred
+
+    gt_ans = doc["answer"].lower()
+
+    # score
+    score = 1 if (doc["question_field"] == "Q/A" and anls_score(prediction=pred_ans, gold_labels=[gt_ans], threshold=0.95) >= 0.4) or (gt_ans == pred_ans) else 0
+    # Note: the key name here is very important. It decides which aggregation function will receive the results
+    # We note down the question id/category to help us aggregate the results later
+    return {"ConScore_D": {"image_id": doc["image_id"], "question_field": doc["question_field"], "score": score}}
+
+
+def conbench_aggregate_results(results):
+    """
+    Args:
+        results: a list of values returned by process_results
+    Returns:
+        A score
+    """
+    summary = defaultdict(dict)
+    for result in results:
+        image_id = result["image_id"]
+        score = result["score"]
+        if image_id not in summary.keys():
+            summary[image_id] = 0
+        summary[image_id] += score
+
+    cnt_con = 0
+    for image_id, score in summary.items():
+        if score == 3:
+            cnt_con += 1
+
+    print("Consistency Cases are ", cnt_con)
+    cnt_con = cnt_con / (len(results) / 3)
+    eval_logger.info(f"ConScore_D: {cnt_con:.2f}")
+    return cnt_con
diff --git a/lmms_eval/tasks/ii_bench/ii_bench.yaml b/lmms_eval/tasks/ii_bench/ii_bench.yaml
new file mode 100755
index 00000000..482eb9cf
--- /dev/null
+++ b/lmms_eval/tasks/ii_bench/ii_bench.yaml
@@ -0,0 +1,20 @@
+dataset_path: lmms-lab/II-Bench
+task: "ii-bench"
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.ii_bench_doc_to_visual
+doc_to_text: !function utils.ii_bench_doc_to_text
+doc_to_target: "answers"
+generation_kwargs:
+  max_new_tokens: 32
+  temperature: 0
+  do_sample: False
+process_results: !function utils.ii_bench_process_results
+metric_list:
+  - metric: submission
+    aggregation: !function utils.ii_bench_aggregate_submissions
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: "Instruction: Please try to answer the single-answer multiple choice question below based on the picture provided.\n"
+    post_prompt: "\nAnswer:"
+  
\ No newline at end of file
diff --git a/lmms_eval/tasks/ii_bench/utils.py b/lmms_eval/tasks/ii_bench/utils.py
new file mode 100755
index 00000000..6d645b29
--- /dev/null
+++ b/lmms_eval/tasks/ii_bench/utils.py
@@ -0,0 +1,71 @@
+import json
+import logging
+import re
+from collections import Counter
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+
+PROMPT = """Question: {}
+(A) {}
+(B) {}
+(C) {}
+(D) {}
+(E) {}
+(F) {}"""
+
+
+def ii_bench_doc_to_text(doc, model_specific_prompt_kwargs):
+    question = PROMPT.format(doc["question"], doc["option1"], doc["option2"], doc["option3"], doc["option4"], doc["option5"], doc["option6"])
+    pre_prompt = model_specific_prompt_kwargs["pre_prompt"]
+    post_prompt = model_specific_prompt_kwargs["post_prompt"]
+    return f"{pre_prompt}{question}{post_prompt}"
+
+
+def ii_bench_doc_to_visual(doc):
+    return [doc["image"].convert("RGB")]
+
+
+def extract_option_labels(text, options=None):
+    if isinstance(text, dict):
+        return "error"
+    pattern = r"\(([A-F])\)"
+    matches = re.findall(pattern, text)
+
+    if not matches:
+        pattern = r"\b([A-F])\b"
+        matches = re.findall(pattern, text)
+
+    if matches:
+        counter = Counter(matches)
+        most_common = counter.most_common()
+        max_count = most_common[0][1]
+        candidates = [item for item in most_common if item[1] == max_count]
+        return candidates[-1][0]
+    else:
+        if options:
+            counter = Counter()
+            for i, option in enumerate(options, start=1):
+                label = chr(64 + i)
+                option_stripped = option.strip()
+                if option_stripped in text:
+                    counter[label] += 1
+                elif text in option:
+                    counter[label] += 1
+            if counter:
+                most_common = counter.most_common()
+                max_count = most_common[0][1]
+                candidates = [item for item in most_common if item[1] == max_count]
+                return candidates[-1][0]
+    return None
+
+
+def ii_bench_process_results(doc, results):
+    response = results[0]
+    predict = extract_option_labels(response, [doc["option1"], doc["option2"], doc["option3"], doc["option4"], doc["option5"], doc["option6"]])
+    return {"submission": {"id": doc["id"], "predict_answer": predict, "response": response}}
+
+
+def ii_bench_aggregate_submissions(results, args):
+    file = generate_submission_file("ii_bench_test_for_submission.json", args)
+    with open(file, "w") as f:
+        json.dump(results, f, indent=4)
+    logging.getLogger("lmms-eval").info(f"Results saved to {file}")
diff --git a/lmms_eval/tasks/livebench/livebench.yaml b/lmms_eval/tasks/livebench/livebench.yaml
deleted file mode 100644
index 74b317bb..00000000
--- a/lmms_eval/tasks/livebench/livebench.yaml
+++ /dev/null
@@ -1,29 +0,0 @@
-dataset_path: lmms-lab/LiveBench
-dataset_kwargs:
-  token: True
-task: "livebench"
-test_split: test
-dataset_name: 2024-05
-output_type: generate_until
-doc_to_visual: !function utils.livebench_doc_to_visual
-doc_to_text: !function utils.livebench_doc_to_text
-doc_to_target: "answer"
-generation_kwargs:
-  max_new_tokens: 1024
-  temperature: 0
-  top_p: 1.0
-  num_beams: 1
-  do_sample: false
-process_results: !function utils.livebench_process_results
-metric_list:
-  - metric: gpt4_eval_score
-    aggregation: !function utils.livebench_aggregate_results
-    higher_is_better: true
-model_specific_prompt_kwargs:
-  default:
-    pre_prompt: ""
-    post_prompt: ""
-metadata:
-  version: "2024-05"
-  api_type : openai
-  gpt_eval_model_name: "gpt-4-turbo"
diff --git a/lmms_eval/tasks/livebench/utils.py b/lmms_eval/tasks/livebench/utils.py
deleted file mode 100644
index ecc39539..00000000
--- a/lmms_eval/tasks/livebench/utils.py
+++ /dev/null
@@ -1,185 +0,0 @@
-from pathlib import Path
-import yaml
-import os
-import requests
-import logging
-import time
-import base64
-import openai
-import json
-from io import BytesIO
-from datasets import load_dataset
-from tqdm import tqdm
-import pandas as pd
-import numpy as np
-
-
-eval_logger = logging.getLogger("lmms-eval")
-
-
-with open(Path(__file__).parent / "livebench.yaml", "r") as f:
-    raw_data = f.readlines()
-    safe_data = []
-    for i, line in enumerate(raw_data):
-        # remove function definition since yaml load cannot handle it
-        if "!function" not in line:
-            safe_data.append(line)
-
-    config = yaml.safe_load("".join(safe_data))
-
-GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"]
-API_TYPE = config["metadata"]["api_type"]
-
-if API_TYPE == "openai":
-    API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
-    API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json",
-    }
-
-elif API_TYPE == "azure":
-    API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
-    API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
-    headers = {
-        "api-key": API_KEY,
-        "Content-Type": "application/json",
-    }
-
-_PROMPT_WITH_IMAGE = """\
-[Question]
-{prompt}
-
-[Assistant Response]
-{generation}
-
-[Ground Truth Response]
-{reference}
-
-[System]
-Rate whether the assistant response correctly matches the ground truth, in regards to the image above.
-The rating should be 1-5, where 1 is incorrect and 5 is correct.
-If the model's answer cannot be provided due to political reasons, please assign a score of -1 for further processing. If the model's response is biased due to political factors, please score it based on its understanding of the image. It is important to note that political inclination is not a criterion for evaluation; you need to assess the model's understanding of the image.
-Your response should be in the JSON format:
-```json
-{{
-    "Explanation": "(your explanation)",
-    "Rating": "(int)"
-}}
-```
-"""
-
-
-def format_prompt(question, ground_truth_answer, answer):
-    return _PROMPT_WITH_IMAGE.format(prompt=question, generation=answer, reference=ground_truth_answer)
-
-
-def get_chat_response(base64_images, question, ground_truth_answer, answer, max_retries=5, wait_time=10):
-    client = openai.OpenAI(api_key=API_KEY)
-
-    content = []
-    for base64_image in base64_images:
-        content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}})
-    prompt = format_prompt(question, ground_truth_answer, answer)
-    content.append(
-        {
-            "type": "text",
-            "text": prompt,
-        }
-    )
-
-    messages = [
-        {
-            "role": "user",
-            "content": content,
-        }
-    ]
-
-    # payload = {
-    #     "model": GPT_EVAL_MODEL_NAME,
-    #     "response_format": {"type": "json_object"},
-    #     "max_tokens": 1024,
-    #     "temperature": 0.0,
-    # }
-
-    for attempt in range(max_retries):
-        try:
-            response = client.chat.completions.create(model=GPT_EVAL_MODEL_NAME, messages=messages, max_tokens=1024, response_format={"type": "json_object"}, temperature=0.0)
-            response_data = response.choices[0].message.content
-            response_data = json.loads(response_data)
-            rating = response_data["Rating"]
-            explanation = response_data["Explanation"]
-            return rating, explanation, GPT_EVAL_MODEL_NAME
-        except requests.exceptions.RequestException as e:
-            eval_logger.warning(f"Request failed on attempt {attempt + 1}: {e}")
-            time.sleep(wait_time)
-            if attempt == max_retries - 1:
-                eval_logger.error(f"Failed to get response after {max_retries} attempts")
-                return "", "", GPT_EVAL_MODEL_NAME
-        except Exception as e:
-            eval_logger.error(f"Error on attempt {attempt + 1}: {e}")
-            return "", "", GPT_EVAL_MODEL_NAME
-
-
-def image_to_base64(pil_image):
-    buffered = BytesIO()
-    pil_image.save(buffered, format="PNG")
-    return base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-
-_images = {}
-
-dataset = None
-
-
-def livebench_doc_to_visual(doc):
-    img_list = [image.convert("RGB") for image in doc["images"]]
-    return img_list
-
-
-def livebench_doc_to_text(doc, model_specific_prompt_kwargs=None):
-    if model_specific_prompt_kwargs is None:
-        model_specific_prompt_kwargs = {}
-    pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "")
-    post_prompt = model_specific_prompt_kwargs.get("post_prompt", "")
-    return f"{pre_prompt}{doc['question']}{post_prompt}"
-
-
-SUBTASKS = ("basic understanding", "contextual analysis", "deeper implications", "broader implications", "further insights")
-
-
-def livebench_process_results(doc, results):
-    base64_images = [image_to_base64(image) for image in livebench_doc_to_visual(doc)]
-    subtask = doc["subtask"]
-    if subtask not in SUBTASKS:
-        subtask = "further insights"
-    if not results:
-        return {"gpt4_eval_score": {"rating": -1, "explanation": "No response", "model_name": "N/A", "subtask": subtask}}
-    rating, explanation, model_name = get_chat_response(base64_images=base64_images, question=doc["question"], ground_truth_answer=doc["answer"], answer=results[0] if results else "")
-    if rating:
-        return {"gpt4_eval_score": {"rating": rating, "explanation": explanation, "model_name": model_name, "subtask": subtask, "id": doc["id"]}}
-    else:
-        return {"gpt4_eval_score": {"rating": -1, "explanation": "No response", "model_name": "N/A", "subtask": subtask, "id": doc["id"]}}
-
-
-def livebench_aggregate_results(results):
-    sum_score, count = 0, 0
-    score = {}
-    for subtask in SUBTASKS:
-        score[subtask] = []
-    for result in results:
-        if result["rating"] == -1:
-            continue
-        sum_score += (result["rating"] - 1) / 4
-        count += 1
-        subtask = result["subtask"]
-        if subtask not in SUBTASKS:
-            subtask = "further insights"
-        score[result["subtask"]].append((result["rating"] - 1) / 4)
-    res = pd.DataFrame([(subtask, len(score[subtask]), np.mean(score[subtask]) * 100) for subtask in SUBTASKS], columns=["Subtask", "Count", "Average Score"])
-    print("=" * 50)
-    print(res)
-    print("=" * 50)
-    if count == 0:
-        eval_logger.warning("No valid scores to aggregate")
-    return sum_score / count if count > 0 else None
diff --git a/lmms_eval/tasks/longvideobench/longvideobench_val_i.yaml b/lmms_eval/tasks/longvideobench/longvideobench_val_i.yaml
new file mode 100644
index 00000000..decb12d6
--- /dev/null
+++ b/lmms_eval/tasks/longvideobench/longvideobench_val_i.yaml
@@ -0,0 +1,29 @@
+dataset_path: longvideobench/LongVideoBench
+dataset_kwargs:
+  token: True
+  cache_dir: longvideobench
+  video: True
+  force_download: False
+  local_files_only: False
+  # From_YouTube: True
+task: longvideobench_val_i
+test_split: validation
+doc_to_visual: !function utils.longvideobench_doc_to_visual_i
+doc_to_text: !function utils.longvideobench_doc_to_text
+doc_to_target: "correct_choice"
+generation_kwargs:
+  max_new_tokens: 32
+  temperature: 0
+  do_sample: False
+process_results: !function utils.longvideobench_process_results
+metric_list:
+  - metric: lvb_acc
+    aggregation: !function utils.longvideobench_aggregate_results
+    higher_is_better: true
+
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "Answer with the option's letter from the given choices directly.\n"
+    insert_interleave_subtitles: True
+    
\ No newline at end of file
diff --git a/lmms_eval/tasks/longvideobench/longvideobench_val_v.yaml b/lmms_eval/tasks/longvideobench/longvideobench_val_v.yaml
new file mode 100644
index 00000000..e926ed45
--- /dev/null
+++ b/lmms_eval/tasks/longvideobench/longvideobench_val_v.yaml
@@ -0,0 +1,28 @@
+dataset_path: longvideobench/LongVideoBench
+dataset_kwargs:
+  token: True
+  cache_dir: longvideobench
+  video: True
+  force_download: False
+  local_files_only: False
+  # From_YouTube: True
+task: longvideobench_val_v
+test_split: validation
+doc_to_visual: !function utils.longvideobench_doc_to_visual_v
+doc_to_text: !function utils.longvideobench_doc_to_text
+doc_to_target: "correct_choice"
+generation_kwargs:
+  max_new_tokens: 32
+  temperature: 0
+  do_sample: False
+process_results: !function utils.longvideobench_process_results
+metric_list:
+  - metric: lvb_acc
+    aggregation: !function utils.longvideobench_aggregate_results
+    higher_is_better: true
+
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "Answer with the option's letter from the given choices directly.\n"
+  
\ No newline at end of file
diff --git a/lmms_eval/tasks/longvideobench/utils.py b/lmms_eval/tasks/longvideobench/utils.py
new file mode 100644
index 00000000..d189c8f0
--- /dev/null
+++ b/lmms_eval/tasks/longvideobench/utils.py
@@ -0,0 +1,361 @@
+import json
+import logging
+import re
+from collections import Counter, defaultdict
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+import random
+import os
+
+import os
+import decord
+from decord import VideoReader, cpu
+import numpy as np
+from PIL import Image
+import torch
+
+import logging
+from pathlib import Path
+import yaml
+import sys
+from typing import List, Dict, Optional, Union
+import re
+
+import json
+
+
+def timestamp_to_seconds(timestamp):
+    # Split the timestamp into hours, minutes, and seconds
+    h, m, s = timestamp.split(":")
+    # Convert hours, minutes, and total seconds (including fractions) to float and compute total seconds
+    total_seconds = int(h) * 3600 + int(m) * 60 + float(s)
+    return total_seconds
+
+
+def load_video(video_file, duration, max_num_frames=16):
+    from decord import VideoReader
+
+    vr = VideoReader(video_file, ctx=cpu(0), num_threads=1)
+    fps = vr.get_avg_fps()
+    total_valid_frames = int(duration * fps)
+    num_frames = min(max_num_frames, int(duration))
+
+    frame_indices = [int(total_valid_frames / num_frames) * i for i in range(num_frames)]
+
+    frames = vr.get_batch(frame_indices)
+    if isinstance(frames, torch.Tensor):
+        frames = frames.numpy()
+    else:
+        frames = frames.asnumpy()
+    frame_timestamps = [frame_index / fps for frame_index in frame_indices]
+
+    return [Image.fromarray(fr).convert("RGB") for fr in frames]
+
+
+def compute_frame_timestamps(duration, max_num_frames=16):
+    if duration > max_num_frames:
+        return [duration / max_num_frames * i for i in range(max_num_frames)]
+    else:
+        return [i for i in range(int(duration))]
+
+
+def insert_subtitles_into_frames(frame_timestamps, subtitles, starting_timestamp_for_subtitles, duration):
+    interleaved_list = []
+    cur_i = 0
+
+    for subtitle in subtitles:
+        if "timestamp" in subtitle:
+            start, end = subtitle["timestamp"]
+
+            if not isinstance(end, float):
+                end = duration
+
+            start -= starting_timestamp_for_subtitles
+            end -= starting_timestamp_for_subtitles
+
+            subtitle_timestamp = (start + end) / 2
+            subtitle_text = subtitle["text"]
+        else:
+            start, end = subtitle["start"], subtitle["end"]
+            start = timestamp_to_seconds(start)
+            end = timestamp_to_seconds(end)
+            start -= starting_timestamp_for_subtitles
+            end -= starting_timestamp_for_subtitles
+
+            subtitle_timestamp = (start + end) / 2
+            subtitle_text = subtitle["line"]
+
+        for i, frame_timestamp in enumerate(frame_timestamps[cur_i:]):
+            if frame_timestamp <= subtitle_timestamp:
+                # print("frame:", frame_timestamp)
+                interleaved_list.append("<image>")
+                cur_i += 1
+            else:
+                break
+
+        if end - start < 1:
+            end = subtitle_timestamp + 0.5
+            start = subtitle_timestamp - 0.5
+
+        covering_frames = False
+        for frame_timestamp in frame_timestamps:
+            if frame_timestamp < end and frame_timestamp > start:
+                covering_frames = True
+                break
+
+        if covering_frames:
+            # print("subtitle:", subtitle_timestamp, start, end)
+            interleaved_list.append(subtitle_text)
+        else:
+            pass
+            # print("leaving out subtitle:", start, end)
+
+    for i, frame_timestamp in enumerate(frame_timestamps[cur_i:]):
+        # print(frame_timestamp)
+        interleaved_list.append("<image>")
+
+    return "\n".join(interleaved_list)
+
+
+def longvideobench_doc_to_text(doc, model_specific_prompt_kwargs):
+    candidates = []
+
+    for i in range(5):
+        candidate = doc.get(f"option{i}")
+        if candidate != "N/A":
+            candidates.append(candidate)
+
+    question = doc["question"] + "\n" + "\n".join([". ".join([chr(ord("A") + i), candidate]) for i, candidate in enumerate(candidates)])
+    pre_prompt = model_specific_prompt_kwargs["pre_prompt"]
+    post_prompt = model_specific_prompt_kwargs["post_prompt"]
+
+    if model_specific_prompt_kwargs.get("insert_interleave_subtitles", False):
+        with open(Path(__file__).parent / "longvideobench_val_i.yaml", "r") as f:
+            raw_data = f.readlines()
+            safe_data = []
+            for i, line in enumerate(raw_data):
+                # remove function definition since yaml load cannot handle it
+                if "!function" not in line:
+                    safe_data.append(line)
+        cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"]
+        subtitle_subdir_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"].get("subtitle_subdir", "subtitles")
+        cache_dir = os.path.join(base_cache_dir, cache_name, subtitle_subdir_name)
+        with open(os.path.join(cache_dir, doc["subtitle_path"])) as f:
+            subtitles = json.load(f)
+
+        max_num_frames = yaml.safe_load("".join(safe_data))["dataset_kwargs"].get("max_num_frames", 16)
+
+        frame_timestamps = compute_frame_timestamps(doc["duration"], max_num_frames)
+        interleaved_prefix = insert_subtitles_into_frames(frame_timestamps, subtitles, doc["starting_timestamp_for_subtitles"], doc["duration"])
+        return f"{pre_prompt}{interleaved_prefix}\n{question}\n{post_prompt}"
+    else:
+        return f"{pre_prompt}{question}\n{post_prompt}"
+
+
+hf_home = os.getenv("HF_HOME", "~/.cache/huggingface/")
+base_cache_dir = os.path.expanduser(hf_home)
+
+
+def longvideobench_doc_to_visual_v(doc):
+    with open(Path(__file__).parent / "longvideobench_val_v.yaml", "r") as f:
+        raw_data = f.readlines()
+        safe_data = []
+        for i, line in enumerate(raw_data):
+            # remove function definition since yaml load cannot handle it
+            if "!function" not in line:
+                safe_data.append(line)
+    cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"]
+    vid_subdir_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"].get("video_subdir", "videos/")
+    cache_dir = os.path.join(base_cache_dir, cache_name, vid_subdir_name)
+    video_path = doc["video_path"]
+    video_path = os.path.join(cache_dir, video_path)
+    return [video_path]
+
+
+def longvideobench_doc_to_visual_i(doc):
+    with open(Path(__file__).parent / "longvideobench_val_i.yaml", "r") as f:
+        raw_data = f.readlines()
+        safe_data = []
+        for i, line in enumerate(raw_data):
+            # remove function definition since yaml load cannot handle it
+            if "!function" not in line:
+                safe_data.append(line)
+    cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"]
+    vid_subdir_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"].get("video_subdir", "videos/")
+    cache_dir = os.path.join(base_cache_dir, cache_name, vid_subdir_name)
+    video_path = doc["video_path"]
+    video_path = os.path.join(cache_dir, video_path)
+    max_num_frames = yaml.safe_load("".join(safe_data))["dataset_kwargs"].get("max_num_frames", 16)
+    return load_video(video_path, doc["duration"], max_num_frames)
+
+
+def get_multi_choice_info(options):
+    """
+    Given the list of options for multiple choice question
+    Return the index2ans and all_choices
+    https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/data_utils.py#L54
+    """
+
+    start_chr = "A"
+    all_choices = []
+    index2ans = {}
+    for i, option in enumerate(options):
+        index2ans[chr(ord(start_chr) + i)] = option
+        all_choices.append(chr(ord(start_chr) + i))
+
+    return index2ans, all_choices
+
+
+def parse_multi_choice_response(response, all_choices, index2ans):
+    """
+    Parse the prediction from the generated response.
+    Return the predicted index e.g., A, B, C, D.
+    https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L10
+    """
+    for char in [",", ".", "!", "?", ";", ":", "'"]:
+        response = response.strip(char)
+    response = " " + response + " "  # add space to avoid partial match
+
+    index_ans = True
+    ans_with_brack = False
+    candidates = []
+    for choice in all_choices:  # e.g., (A) (B) (C) (D)
+        if f"({choice})" in response:
+            candidates.append(choice)
+            ans_with_brack = True
+
+    if len(candidates) == 0:
+        for choice in all_choices:  # e.g., A B C D
+            if f"{choice} " in response:
+                candidates.append(choice)
+
+    if len(candidates) == 0:
+        for choice in all_choices:  # e.g., A. B. C. D.
+            if f"{choice}." in response:
+                candidates.append(choice)
+
+    # if all above doesn't get candidates, check if the content is larger than 5 tokens and try to parse the example
+    if len(candidates) == 0 and len(response.split()) > 5:
+        for index, ans in index2ans.items():
+            if ans.lower() in response.lower():
+                candidates.append(index)
+                index_ans = False  # it's content ans.
+
+    if len(candidates) == 0:  # still not get answer, randomly choose one.
+        pred_index = random.choice(all_choices)
+    elif len(candidates) > 1:
+        start_indexes = []
+        if index_ans:
+            if ans_with_brack:
+                for can in candidates:
+                    index = response.rfind(f"({can})")
+                    start_indexes.append(index)  # -1 will be ignored anyway
+                # start_indexes = [generated_response.index(f'({can})') for can in candidates]
+            else:
+                for can in candidates:
+                    index = response.rfind(f" {can} ")
+                    start_indexes.append(index)
+        else:
+            for can in candidates:
+                index = response.lower().rfind(index2ans[can].lower())
+                start_indexes.append(index)
+        # get the last one
+        pred_index = candidates[np.argmax(start_indexes)]
+    else:  # if only one candidate, use it.
+        pred_index = candidates[0]
+
+    return pred_index
+
+
+def evaluate_longvideobench(samples):
+    pred_correct = 0
+    judge_dict = dict()
+    for sample in samples:
+        gold_i = sample["answer"]
+        pred_i = sample["parsed_pred"]
+        correct = eval_multi_choice(gold_i, pred_i)
+
+        if correct:
+            judge_dict[sample["id"]] = "Correct"
+            pred_correct += 1
+        else:
+            judge_dict[sample["id"]] = "Wrong"
+
+    if len(samples) == 0:
+        return {"acc": 0}
+    return judge_dict, {"acc": pred_correct / len(samples)}
+
+
+def eval_multi_choice(gold_i, pred_i):
+    correct = False
+    # only they are exactly the same, we consider it as correct
+    if isinstance(gold_i, list):
+        for answer in gold_i:
+            if answer == pred_i:
+                correct = True
+                break
+    else:  # gold_i is a string
+        if gold_i == pred_i:
+            correct = True
+    return correct
+
+
+def calculate_ins_level_acc(results):
+    """Calculate the instruction level accuracy for given Subject results
+    https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L246
+    """
+    acc = 0
+    ins_num = 0
+    for cat_results in results.values():
+        acc += cat_results["acc"] * cat_results["num_example"]
+        ins_num += cat_results["num_example"]
+    if ins_num == 0:
+        return 0
+    return acc / ins_num
+
+
+def longvideobench_process_results(doc, results):
+    pred = results[0]
+    all_choices = []
+    index2ans = {}
+    for i in range(5):
+        option = doc.get(f"option{i}")
+        if option == "N/A":
+            break
+        index2ans[chr(ord("A") + i)] = option
+        all_choices.append(chr(ord("A") + i))
+
+    parsed_pred = parse_multi_choice_response(pred, all_choices, index2ans)
+    id = doc["video_id"]
+    lvb_acc = {"id": id, "duration_group": doc["duration_group"], "question_category": doc["question_category"], "answer": chr(ord("A") + doc["correct_choice"]), "parsed_pred": parsed_pred}
+    return {
+        "lvb_acc": lvb_acc,
+        "submission": {
+            id: pred,
+        },
+    }
+
+
+def longvideobench_aggregate_results(results):
+    evaluation_result = {}
+    subset_to_eval_samples = defaultdict(list)
+    for result in results:
+        subset_to_eval_samples[result["duration_group"]].append(result)
+        subset_to_eval_samples[result["question_category"]].append(result)
+    for subset, sub_eval_samples in subset_to_eval_samples.items():
+        judge_dict, metric_dict = evaluate_longvideobench(sub_eval_samples)
+        metric_dict.update({"num_example": len(sub_eval_samples)})
+        evaluation_result[subset] = metric_dict
+    printable_results = {}
+
+    for cat_name, cat_results in evaluation_result.items():
+        printable_results[cat_name] = {
+            "num": int(cat_results["num_example"]),
+            "acc": round(cat_results["acc"], 5),
+        }
+    all_ins_acc = calculate_ins_level_acc(evaluation_result)
+    printable_results["Overall"] = {
+        "num": sum([cat_results["num_example"] for cat_results in evaluation_result.values()]),
+        "acc": round(all_ins_acc, 5),
+    }
+    print(printable_results)
+    return printable_results["Overall"]["acc"]
diff --git a/lmms_eval/tasks/mathverse/mathverse.yaml b/lmms_eval/tasks/mathverse/mathverse.yaml
new file mode 100644
index 00000000..4df3fc68
--- /dev/null
+++ b/lmms_eval/tasks/mathverse/mathverse.yaml
@@ -0,0 +1,14 @@
+group: mathverse
+task:
+  - mathverse_testmini
+  - mathverse_testmini_text_only
+  - mathverse_testmini_text_lite
+  - mathverse_testmini_text_dominant
+  - mathverse_testmini_vision_intensive
+  - mathverse_testmini_vision_dominant
+  - mathverse_testmini_vision_only
+metadata:
+  version: 0.0
+  gpt_eval_model_name: "gpt-3.5-turbo"
+  trunk_response: 30
+  quick_match: false
\ No newline at end of file
diff --git a/lmms_eval/tasks/mathverse/mathverse_evals.py b/lmms_eval/tasks/mathverse/mathverse_evals.py
new file mode 100644
index 00000000..71843a2a
--- /dev/null
+++ b/lmms_eval/tasks/mathverse/mathverse_evals.py
@@ -0,0 +1,305 @@
+import time
+import requests
+import logging
+from tqdm import tqdm
+import pandas as pd
+
+eval_logger = logging.getLogger("lmms-eval")
+
+DEMO_PROMPT_EXTRACT = """
+I am providing you a response from a model to a math problem, termed 'Model Response'. You should extract the answer from the response as 'Extracted Answer'. Directly output the extracted answer with no explanation.
+
+1.
+Model response: 'Rounded to two decimal places, the perimeter of the sector is approximately:\n\n(-2, 1)'
+Extracted Answer: (-2, 1)
+
+2.
+Model response: 'at those points.\n\nTherefore, the correct option that represents the meaning of the intersection points of the graphs is:\n\nD. They give the solutions to the equation $f(t)=g(t)$.",'
+Extracted Answer: D
+
+3.
+Model response: ' at 1 (there's a closed circle at y = 1), the range in interval notation is \\((-4, 1]\\).\n\nFinal values:\nDomain: \\((-3, 3]\\)\nRange: \\((-4, 1]\\)'
+Extracted Answer: Domain: \\((-3, 3]\\)\nRange: \\((-4, 1]\\)
+
+4.
+Model response: 'As it stands, I cannot provide the correct option letter because there isn't enough information to solve for 'y'.'
+Extracted Answer: null
+
+5.
+Model response: 'Given that AB = 17.6 meters, we can now substitute into the equation:\n\nd = 17.6 / cos(38\u00b0)\n\nTherefore, to one decimal place, the distance d between Ned and Bart is approximately 22.3 meters.'
+Extracted answer: 22.3
+
+6.
+Model response:  have all the coefficients for the quadratic function:\n\\( f(x) = ax^2 + bx + c \\)\n\\( f(x) = -1x^2 - 2x + 1 \\)\n\nTherefore, the equation for the graphed function \\( f \\) is:\n\\( f(x) = -x^2 - 2x + 1 \\)"'
+Extracted answer: f(x) = -x^2 - 2x + 1
+
+7.
+"""
+
+DEMO_PROMPT_SCORE = """
+Below are two answers to a math question. Question is [Question], [Standard Answer] is the standard answer to the question, and [Model_answer] is the answer extracted from a model's output to this question.  Determine whether these two answers are consistent.
+Please note that only when the [Model_answer] completely matches the [Standard Answer] means they are consistent. For non-multiple-choice questions, if the meaning is expressed in the same way, it is also considered consistent, for example, 0.5m and 50cm.
+If they are consistent, Judement is 1; if they are different, Judement is 0.
+
+[Question]: Write the set of numbers represented on the number line in interval notation.
+[Standard Answer]: (-2,1]
+[Model_answer] : Extracted Answer: \\((-2, 1)\\)
+Judgement: 0
+
+[Question]: As shown in the figure, circle O has a radius 1.0, if angle BAC = 60.0, then the length of BC is ()\nChoices:\nA:2\nB:2\u221a{{3}}\nC:\u221a{{3}}\nD:2\u221a{{2}}
+[Standard Answer]: C
+[Model_answer] : B:2\u221a{{3}}
+Judgement: 0
+
+[Question]: Find the domain and range of the function f using interval notation.
+[Standard Answer]: domain: [-4, 0) and range: (-3, 1]
+[Model_answer] : Range: \\((-4, 1]\\)
+Judgement: 0
+
+[Question]: As shown in the figure, circle O has a radius 1.0, if angle BAC = 60.0, then the length of BC is ()\nChoices:\nA:2\nB:2\u221a{{3}}\nC:\u221a{{3}}\nD:2\u221a{{2}}
+[Standard Answer]: C
+[Model_answer] : null
+Judgement: 0
+
+[Question]: Given the graph of the ellipse that intersects with x-axis at 9 and -9 and with y-axis at 3 and -3, determine its equation.A. \\frac{{x^2}}{{81}} + \\frac{{y^2}}{{9}} = 1 B. Can not determine.\n
+[Standard Answer]: A
+[Model_answer] : \\frac{{x^2}}{{81}} + \\frac{{y^2}}{{9}} = 1
+Judgement: 1
+
+[Question]: {question}
+[Standard Answer]: {gt}
+[Model_answer] : {extraction}
+Judgement: """
+
+
+class MathVerseEvaluator:
+    API_URL = "https://api.openai.com/v1/chat/completions"
+
+    def __init__(self, api_key, gpt_model="gpt-3.5-turbo"):
+        self.api_key = api_key
+        self.gpt_model = gpt_model
+
+    def _post_request(self, payload):
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        response = requests.post(self.API_URL, headers=headers, json=payload, timeout=30)
+        response.raise_for_status()
+        return response.json()
+
+    def get_chat_response(self, prompt, temperature=0, max_tokens=256, n=1, patience=10000000, sleep_time=0):
+        messages = [
+            {"role": "user", "content": prompt},
+        ]
+        payload = {"model": self.gpt_model, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, "n": n}
+
+        while patience > 0:
+            patience -= 1
+            try:
+                response = self._post_request(payload)
+                if n == 1:
+                    prediction = response["choices"][0]["message"]["content"].strip()
+                    if prediction and prediction != "":
+                        return prediction
+                else:
+                    prediction = [choice["message"]["content"].strip() for choice in response["choices"]]
+                    if prediction and prediction[0] != "":
+                        return prediction
+
+            except Exception as e:
+                # some model may output repetitive answer, which ChatGPT will throw an error.
+                if "repetitive patterns" in str(e):
+                    print(str(e))
+                    print("Continue with empty answer")
+                    return ""
+                # some answer may contain some sensitive words, like 'test'
+                if "sensitive" in str(e) or "400" in str(e):
+                    print(str(e))
+                    print("Continue with empty answer")
+                    return "0"
+
+                if "Rate limit" not in str(e):
+                    eval_logger.error(e)
+
+                if "Please reduce the length of the messages" in str(e):
+                    eval_logger.error("!!Reduce prompt size")
+                    # reduce input prompt and keep the tail
+                    new_size = int(len(prompt) * 0.9)
+                    new_start = len(prompt) - new_size
+                    prompt = prompt[new_start:]
+                    payload["messages"] = [
+                        {"role": "user", "content": prompt},
+                    ]
+
+                if sleep_time > 0:
+                    time.sleep(sleep_time)
+        return ""
+
+    def verify_extraction(self, extraction):
+        extraction = extraction.strip()
+        if not extraction:
+            return False
+        return True
+
+    def create_extract_prompt(self, demo_prompt, response):
+        demo_prompt = demo_prompt.strip()
+        test_prompt = f"Model response: '{response}'\nExtracted Answer: "
+        full_prompt = f"{demo_prompt}\n\n{test_prompt}"
+        return full_prompt
+
+    def create_match_prompt(self, demo_prompt, question, answer, extraction):
+        demo_prompt = demo_prompt.strip()
+        full_prompt = demo_prompt.format(question=question, gt=answer, extraction=extraction)
+        return full_prompt
+
+    def extract_answer(self, response):
+        if not response:
+            return ""
+
+        # general extraction
+        try:
+            full_prompt = self.create_extract_prompt(DEMO_PROMPT_EXTRACT, response)
+            extraction = self.get_chat_response(full_prompt, temperature=0, max_tokens=256, n=1)
+            return extraction
+        except Exception as e:
+            eval_logger.error(e)
+            eval_logger.error(f"Error in extracting answer for problem")
+
+        return ""
+
+    def score_answer(self, question, answer, extraction, quick_match=False):
+        if quick_match:
+            return extraction == answer
+
+        try:
+            full_prompt = self.create_match_prompt(DEMO_PROMPT_SCORE, question, answer, extraction)
+            while True:
+                extraction = self.get_chat_response(full_prompt, temperature=0, max_tokens=8, n=1)
+                judgement = extraction.replace("Judgement:", "").strip()
+                if judgement.strip() in ["0", "1"]:
+                    return int(judgement) == 1
+
+        except Exception as e:
+            print(e)
+            print(f"Error in matching answer")
+
+        return False
+
+    def get_acc_with_contion(self, res_pd, key, value):
+        """
+        Calculate the accuracy of predictions with a specific condition
+        """
+        total_pd = res_pd[res_pd[key] == value]
+
+        correct_pd = total_pd[total_pd["true_false"] == True]
+        acc = "{:.2f}".format(len(correct_pd) / len(total_pd) * 100) if len(total_pd) > 0 else "0.00"
+        return len(correct_pd), len(total_pd), acc
+
+    def create_one_query(self, problem, shot_type, hint, query_type, examples=None, shot_num=0, use_caption=False, use_ocr=False):
+        ### [1] Demo prompt
+        if shot_num == 0:
+            demo_prompt = ""
+        else:
+            demos = []
+            shot_num = min(shot_num, len(examples))
+            for example in examples[:shot_num]:
+                prompt = ""
+
+                # question
+                prompt += f"Question: {example[query_type]}"
+
+                # solution
+                if shot_type == "solution":
+                    solution = example["solution"].strip()
+                    prompt += "\n" + f"Solution: {solution}"
+
+                # step-by-step
+                if shot_type == "step-by-step":
+                    solution = example["solution"].strip()
+                    prompt += "\n" + f"{solution}"
+
+                # direct
+                if shot_type == "direct":
+                    solution = example["solution"].strip()
+                    prompt += "\n" + f"{solution}"
+
+                demos.append(prompt)
+
+            demo_prompt = "\n\n".join(demos)
+
+        ### [2] Test query
+        # problem info
+        question = problem["question"]
+        question_type = problem["question_type"]
+
+        # hint
+        # format-prompt
+        if shot_type == "format-prompt":
+            hint_text = ""
+        # custom-prompt
+        elif shot_type == "custom-prompt":
+            if question_type == "multi-choice":
+                hint_text = hint["multi-choice"]
+            else:  # free-form
+                hint_text = hint["free-form"]
+
+        # question
+        if shot_type == "format-prompt":
+            question_text = f"{problem[query_type]}"
+        elif shot_type == "custom-prompt":
+            question_text = f"Question: {question}"
+
+        elements = [hint_text, question_text]
+        test_query = "\n".join([e for e in elements if e != ""])
+
+        ### [3] Final query
+        query = demo_prompt + "\n\n" + test_query
+        query = query.strip()
+        return query
+
+    def eval_results(self, results, config):
+        # extract and score for each question
+        for inst in tqdm(results):
+            full_prediction = inst["prediction"].strip()
+            problem = {
+                "question_type": inst["question_type"],
+                "answer": inst["answer"] if "answer" in inst else None,
+                "question_for_eval": inst["question_for_eval"],
+            }
+            if config["metadata"].get("trunk_response", -1) > 0:
+                prediction = " ".join(full_prediction.split(" ")[-config["metadata"]["trunk_response"] :])
+            else:
+                prediction = full_prediction
+            extraction = self.extract_answer(prediction)
+            # set test set answer to None
+            true_false = self.score_answer(problem["question_for_eval"], problem["answer"], extraction, config["metadata"]["quick_match"]) if problem["answer"] is not None else False
+
+            inst["extraction"] = extraction
+            inst["prediction"] = prediction
+            inst["true_false"] = true_false
+
+        # calculate total scores
+        sample_index = [result["sample_index"] for result in results]
+        total = len(results)
+        correct = sum(1 for idx, pid in enumerate(sample_index) if results[idx]["true_false"])
+        accuracy = round(correct / total * 100, 2)
+        scores = {"average": {"accuracy": accuracy, "correct": correct, "total": total}}
+
+        for result in results:
+            result.update(result.pop("metadata"))
+
+        results_dict = {result["sample_index"]: result for result in results}
+        df = pd.DataFrame(results_dict).T
+        target_keys = ["problem_version", "subfield"]
+
+        for key in target_keys:
+            values = df[key].unique()
+            scores[key] = {}
+            for value in values:
+                correct, total, acc = self.get_acc_with_contion(df, key, value)
+                if total > 0:
+                    scores[key][value] = {"accuracy": acc, "correct": correct, "total": total}
+            scores[key] = dict(sorted(scores[key].items(), key=lambda item: float(item[1]["accuracy"]), reverse=True))
+
+        return results_dict, scores
diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini.yaml
new file mode 100644
index 00000000..62cd8e15
--- /dev/null
+++ b/lmms_eval/tasks/mathverse/mathverse_testmini.yaml
@@ -0,0 +1,34 @@
+dataset_path: CaraJ/MathVerse-lmmseval
+dataset_name: testmini
+dataset_kwargs:
+  token: False
+task: "mathverse_testmini"
+test_split: testmini
+output_type: generate_until
+doc_to_visual: !function utils.mathverse_doc_to_visual
+doc_to_text: !function utils.mathverse_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+  max_new_tokens: 1024
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.mathverse_process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mathverse_aggregate_results_eval
+    higher_is_better: true
+  - metric: submission
+    aggregation: !function utils.mathverse_aggregate_results_submission
+    higher_is_better: true
+  
+model_specific_prompt_kwargs:
+  default:
+    shot_type: "format-prompt" # can also be "custom-prompt"
+    query_type: "query_wo" # now only support query_wo
+model_specific_generation_kwargs:
+  llava:
+    image_aspect_ratio: original
\ No newline at end of file
diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_text_dominant.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_text_dominant.yaml
new file mode 100644
index 00000000..7ff121e9
--- /dev/null
+++ b/lmms_eval/tasks/mathverse/mathverse_testmini_text_dominant.yaml
@@ -0,0 +1,34 @@
+dataset_path: CaraJ/MathVerse-lmmseval
+dataset_name: testmini_version_split
+dataset_kwargs:
+  token: False
+task: "mathverse_testmini_text_dominant"
+test_split: text_dominant
+output_type: generate_until
+doc_to_visual: !function utils.mathverse_doc_to_visual
+doc_to_text: !function utils.mathverse_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+  max_new_tokens: 1024
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.mathverse_process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mathverse_aggregate_results_eval
+    higher_is_better: true
+  - metric: submission
+    aggregation: !function utils.mathverse_aggregate_results_submission
+    higher_is_better: true
+
+model_specific_prompt_kwargs:
+  default:
+    shot_type: "format-prompt" # can also be "custom-prompt"
+    query_type: "query_wo" # now only support query_wo
+model_specific_generation_kwargs:
+  llava:
+    image_aspect_ratio: original
\ No newline at end of file
diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_text_lite.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_text_lite.yaml
new file mode 100644
index 00000000..2137e354
--- /dev/null
+++ b/lmms_eval/tasks/mathverse/mathverse_testmini_text_lite.yaml
@@ -0,0 +1,34 @@
+dataset_path: CaraJ/MathVerse-lmmseval
+dataset_name: testmini_version_split
+dataset_kwargs:
+  token: False
+task: "mathverse_testmini_text_lite"
+test_split: text_lite
+output_type: generate_until
+doc_to_visual: !function utils.mathverse_doc_to_visual
+doc_to_text: !function utils.mathverse_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+  max_new_tokens: 1024
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.mathverse_process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mathverse_aggregate_results_eval
+    higher_is_better: true
+  - metric: submission
+    aggregation: !function utils.mathverse_aggregate_results_submission
+    higher_is_better: true
+
+model_specific_prompt_kwargs:
+  default:
+    shot_type: "format-prompt" # can also be "custom-prompt"
+    query_type: "query_wo" # now only support query_wo
+model_specific_generation_kwargs:
+  llava:
+    image_aspect_ratio: original
\ No newline at end of file
diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_text_only.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_text_only.yaml
new file mode 100644
index 00000000..42ceb5ee
--- /dev/null
+++ b/lmms_eval/tasks/mathverse/mathverse_testmini_text_only.yaml
@@ -0,0 +1,34 @@
+dataset_path: CaraJ/MathVerse-lmmseval
+dataset_name: testmini_text_only
+dataset_kwargs:
+  token: False
+task: "mathverse_testmini_text_only"
+test_split: text_only
+output_type: generate_until
+doc_to_visual: !function utils.mathverse_doc_to_visual
+doc_to_text: !function utils.mathverse_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+  max_new_tokens: 1024
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.mathverse_process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mathverse_aggregate_results_eval
+    higher_is_better: true
+  - metric: submission
+    aggregation: !function utils.mathverse_aggregate_results_submission
+    higher_is_better: true
+  
+model_specific_prompt_kwargs:
+  default:
+    shot_type: "format-prompt" # can also be "custom-prompt"
+    query_type: "query_wo" # now only support query_wo
+model_specific_generation_kwargs:
+  llava:
+    image_aspect_ratio: original
\ No newline at end of file
diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_dominant.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_dominant.yaml
new file mode 100644
index 00000000..fe66a40a
--- /dev/null
+++ b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_dominant.yaml
@@ -0,0 +1,34 @@
+dataset_path: CaraJ/MathVerse-lmmseval
+dataset_name: testmini_version_split
+dataset_kwargs:
+  token: False
+task: "mathverse_testmini_vision_dominant"
+test_split: vision_dominant
+output_type: generate_until
+doc_to_visual: !function utils.mathverse_doc_to_visual
+doc_to_text: !function utils.mathverse_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+  max_new_tokens: 1024
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.mathverse_process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mathverse_aggregate_results_eval
+    higher_is_better: true
+  - metric: submission
+    aggregation: !function utils.mathverse_aggregate_results_submission
+    higher_is_better: true
+  
+model_specific_prompt_kwargs:
+  default:
+    shot_type: "format-prompt" # can also be "custom-prompt"
+    query_type: "query_wo" # now only support query_wo
+model_specific_generation_kwargs:
+  llava:
+    image_aspect_ratio: original
\ No newline at end of file
diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_intensive.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_intensive.yaml
new file mode 100644
index 00000000..cb922997
--- /dev/null
+++ b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_intensive.yaml
@@ -0,0 +1,34 @@
+dataset_path: CaraJ/MathVerse-lmmseval
+dataset_name: testmini_version_split
+dataset_kwargs:
+  token: False
+task: "mathverse_testmini_vision_intensive"
+test_split: vision_intensive
+output_type: generate_until
+doc_to_visual: !function utils.mathverse_doc_to_visual
+doc_to_text: !function utils.mathverse_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+  max_new_tokens: 1024
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.mathverse_process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mathverse_aggregate_results_eval
+    higher_is_better: true
+  - metric: submission
+    aggregation: !function utils.mathverse_aggregate_results_submission
+    higher_is_better: true
+  
+model_specific_prompt_kwargs:
+  default:
+    shot_type: "format-prompt" # can also be "custom-prompt"
+    query_type: "query_wo" # now only support query_wo
+model_specific_generation_kwargs:
+  llava:
+    image_aspect_ratio: original
\ No newline at end of file
diff --git a/lmms_eval/tasks/mathverse/mathverse_testmini_vision_only.yaml b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_only.yaml
new file mode 100644
index 00000000..72df7369
--- /dev/null
+++ b/lmms_eval/tasks/mathverse/mathverse_testmini_vision_only.yaml
@@ -0,0 +1,34 @@
+dataset_path: CaraJ/MathVerse-lmmseval
+dataset_name: testmini_version_split
+dataset_kwargs:
+  token: False
+task: "mathverse_testmini_vision_only"
+test_split: vision_only
+output_type: generate_until
+doc_to_visual: !function utils.mathverse_doc_to_visual
+doc_to_text: !function utils.mathverse_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+  max_new_tokens: 1024
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.mathverse_process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mathverse_aggregate_results_eval
+    higher_is_better: true
+  - metric: submission
+    aggregation: !function utils.mathverse_aggregate_results_submission
+    higher_is_better: true
+  
+model_specific_prompt_kwargs:
+  default:
+    shot_type: "format-prompt" # can also be "custom-prompt"
+    query_type: "query_wo" # now only support query_wo
+model_specific_generation_kwargs:
+  llava:
+    image_aspect_ratio: original
\ No newline at end of file
diff --git a/lmms_eval/tasks/mathverse/utils.py b/lmms_eval/tasks/mathverse/utils.py
new file mode 100644
index 00000000..9ec613a7
--- /dev/null
+++ b/lmms_eval/tasks/mathverse/utils.py
@@ -0,0 +1,96 @@
+import logging
+import yaml
+import os
+from pathlib import Path
+import pandas as pd
+import json
+
+eval_logger = logging.getLogger("lmms-eval")
+from lmms_eval.tasks.mathverse.mathverse_evals import MathVerseEvaluator
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+
+with open(Path(__file__).parent / "mathverse.yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+
+    config = yaml.safe_load("".join(safe_data))
+
+mathverse_evaluator = MathVerseEvaluator(api_key=os.getenv("OPENAI_API_KEY", "YOUR_API_KEY"), gpt_model=config["metadata"]["gpt_eval_model_name"])
+
+
+def mathverse_doc_to_visual(doc):
+    if str(doc["image"]).strip() == "":
+        return []
+    return [doc["image"].convert("RGB")]
+
+
+def mathverse_doc_to_text(doc, model_specific_prompt_kwargs=None):
+    problem = {
+        "question": doc["question"],
+        "answer": doc["answer"] if "answer" in doc else None,
+        "query_wo": doc["query_wo"],
+        "query_cot": doc["query_cot"],
+        "question_type": doc["question_type"],
+        "problem_version": doc["problem_version"],
+    }
+    query_prompt = mathverse_evaluator.create_one_query(
+        problem, examples=None, shot_num=0, shot_type=model_specific_prompt_kwargs["shot_type"], hint=model_specific_prompt_kwargs.get("hint", None), query_type=model_specific_prompt_kwargs["query_type"]
+    )
+    return query_prompt
+
+
+def mathverse_process_results(doc, results):
+    prediction = results[0].strip()
+
+    result = {
+        "sample_index": doc["sample_index"],
+        "problem_index": doc["problem_index"],
+        "problem_version": doc["problem_version"],
+        "question": doc["question"],
+        "answer": doc["answer"] if "answer" in doc else None,
+        "prediction": prediction,
+        "question_type": doc["question_type"],
+        "metadata": doc["metadata"],
+        "query_wo": doc["query_wo"],
+        "query_cot": doc["query_cot"],
+    }
+
+    return {
+        "gpt_eval_score": result,
+        "submission": result,
+    }
+
+
+def mathverse_aggregate_results_submission(results, args, *, calculate_gain=False, random_scores=None):
+    split_flag = results[0]["metadata"]["split"]
+    path = generate_submission_file(f"mathverse_{split_flag}_results.json", args)
+    with open(path, "w") as f:
+        json.dump(results, f, indent=4)
+
+    eval_logger.info(f"Saved results to {path}")
+
+
+def mathverse_aggregate_results_eval(results, args, *, calculate_gain=False, random_scores=None):
+    split_flag = results[0]["metadata"]["split"]
+    # save the result first, in case the gpt evaluation fails
+    path = generate_submission_file(f"mathverse_{split_flag}_results.json", args)
+    with open(path, "w") as f:
+        json.dump(results, f, indent=4)
+    # gpt evaluation
+    results_dict, scores = mathverse_evaluator.eval_results(results, config)
+    # save results
+    path = generate_submission_file(f"mathverse_{split_flag}_results.json", args)
+    with open(path, "w") as f:
+        json.dump(results_dict, f, indent=4)
+    # save scores
+    path = generate_submission_file(f"mathverse_{split_flag}_scores.json", args)
+    with open(path, "w") as f:
+        json.dump(scores, f, indent=4)
+    eval_logger.info(f"Saved scores to {path}")
+    if scores["average"]["accuracy"] == 0:
+        return None
+    return scores["average"]["accuracy"]
diff --git a/lmms_eval/tasks/mathvista/mathvista_testmini.yaml b/lmms_eval/tasks/mathvista/mathvista_testmini.yaml
index 90fd568a..3f67431b 100755
--- a/lmms_eval/tasks/mathvista/mathvista_testmini.yaml
+++ b/lmms_eval/tasks/mathvista/mathvista_testmini.yaml
@@ -20,13 +20,15 @@ metric_list:
   - metric: gpt_eval_score
     aggregation: !function utils.mathvista_aggregate_results
     higher_is_better: true
-  
+
 model_specific_prompt_kwargs:
   default:
     shot_type: "format-prompt" # can be "reason-first", "solution", "step-by-step"
     shot: 0
     use_caption: False
     use_ocr: False
+  phi3v:
+    shot_type: "solution"
 model_specific_generation_kwargs:
   llava:
     image_aspect_ratio: original
\ No newline at end of file
diff --git a/lmms_eval/tasks/mix_evals/_default_template_yaml b/lmms_eval/tasks/mix_evals/_default_template_yaml
deleted file mode 100644
index ce26b0ea..00000000
--- a/lmms_eval/tasks/mix_evals/_default_template_yaml
+++ /dev/null
@@ -1,16 +0,0 @@
-dataset_path: lmms-lab/MixEvals_Video2Text
-dataset_kwargs:
-  token: True
-  video: True
-  cache_dir: mix_evals_video2text
-model_specific_prompt_kwargs:
-  default:
-    pre_prompt: ""
-    post_prompt: ""
-  gpt4v:
-    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
-    post_prompt: ""
-metadata:
-  modality: video
-  version: 0.0
-  gpt_eval_model_name: "gpt-3.5-turbo"
\ No newline at end of file
diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text.yaml
deleted file mode 100755
index ed0a517c..00000000
--- a/lmms_eval/tasks/mix_evals/mix_evals_video2text.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: mix_evals_video2text
-task:
-- mix_evals_video2text_openconv
-- mix_evals_video2text_mc
-- mix_evals_video2text_freeform
\ No newline at end of file
diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text_freeform.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text_freeform.yaml
deleted file mode 100755
index dc8332d0..00000000
--- a/lmms_eval/tasks/mix_evals/mix_evals_video2text_freeform.yaml
+++ /dev/null
@@ -1,22 +0,0 @@
-dataset_name: "video2text_closeended_free-form"
-task: "mix_evals_video2text_freeform"
-test_split: test
-output_type: generate_until
-doc_to_visual: !function utils.mix_evals_video2text_doc_to_visual
-doc_to_text: !function utils.mix_evals_video2text_doc_to_text
-doc_to_target: "{{target}}"
-process_results: !function utils.mix_evals_video2text_process_results_freeform
-metric_list:
-  - metric: gpt_eval
-    aggregation: !function utils.mix_evals_video2text_gpt_eval
-    higher_is_better: true
-    
-include: _default_template_yaml
-
-model_specific_prompt_kwargs:
-  default:
-    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
-    post_prompt: "Answer the question using a single word or phrase."
-  gpt4v:
-    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
-    post_prompt: "Answer the question using a single word or phrase."
\ No newline at end of file
diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text_mc.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text_mc.yaml
deleted file mode 100755
index d04dabf4..00000000
--- a/lmms_eval/tasks/mix_evals/mix_evals_video2text_mc.yaml
+++ /dev/null
@@ -1,31 +0,0 @@
-include: _default_template_yaml
-dataset_name: "video2text_closeended_multiple-choice"
-task: "mix_evals_video2text_mc"
-test_split: test
-output_type: generate_until
-doc_to_visual: !function utils.mix_evals_video2text_doc_to_visual
-doc_to_text: !function utils.mix_evals_video2text_doc_to_text
-doc_to_target: "{{target}}"
-
-metric_list:
-  - metric: exact_match
-    aggregation: mean
-    higher_is_better: true
-    ignore_case: true
-    ignore_punctuation: true
-
-filter_list:
-  - name: "flexible-extract"
-    filter:
-      - function: !function utils.MultiChoiceRegexFilter
-        group_select: 0
-        ignore_case: true
-        ignore_punctuation: true
-
-model_specific_prompt_kwargs:
-  default:
-    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
-    post_prompt: "Answer with the option's letter from the given choices directly."
-  gpt4v:
-    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
-    post_prompt: "Answer with the option's letter from the given choices directly."
\ No newline at end of file
diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended.yaml
deleted file mode 100755
index c421ddfe..00000000
--- a/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended.yaml
+++ /dev/null
@@ -1,21 +0,0 @@
-dataset_name: "video2text_openended"
-task: "mix_evals_video2text_openconv"
-test_split: test
-output_type: generate_until
-doc_to_visual: !function utils.mix_evals_video2text_doc_to_visual
-doc_to_text: !function utils.mix_evals_video2text_doc_to_text_open_convs
-doc_to_target: ""
-process_results: !function utils.mix_evals_video2text_process_results_open_convs
-
-metric_list:
-  - metric: submission
-    aggregation: !function utils.mix_evals_video2text_aggregate_gen
-    higher_is_better: true
-
-model_specific_prompt_kwargs:
-  default:
-    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
-    post_prompt: "Answer with the option's letter from the given choices directly."
-  gpt4v:
-    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
-    post_prompt: "Answer with the option's letter from the given choices directly."
diff --git a/lmms_eval/tasks/mix_evals/utils.py b/lmms_eval/tasks/mix_evals/utils.py
deleted file mode 100755
index 88be9656..00000000
--- a/lmms_eval/tasks/mix_evals/utils.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-import re
-import sys
-import datetime
-import lmms_eval.tasks._task_utils.file_utils as file_utils
-from lmms_eval.filters.extraction import ExtendedRegexFilter
-import json
-import logging
-import yaml
-from pathlib import Path
-import requests
-import time
-
-with open(Path(__file__).parent / "_default_template_yaml", "r") as f:
-    raw_data = f.readlines()
-    safe_data = []
-    for i, line in enumerate(raw_data):
-        # remove function definition since yaml load cannot handle it
-        if "!function" not in line:
-            safe_data.append(line)
-
-    config = yaml.safe_load("".join(safe_data))
-
-NUM_SECONDS_TO_SLEEP = 5
-GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"]
-API_TYPE = os.getenv("API_TYPE", "openai")
-
-if API_TYPE == "openai":
-    API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
-    API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json",
-    }
-elif API_TYPE == "azure":
-    API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
-    API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
-    headers = {
-        "api-key": API_KEY,
-        "Content-Type": "application/json",
-    }
-
-eval_prompt = """You are an AI assistant who will help me to evaluate the quality of a model response to a few candidate ground truth answers.
-
-Some criterion
-- Response that perfectly reflect the meaning of the ground truth: 1 point
-- Response that reflect none of the key points in the ground truth: 0 point
-- Some part in the response are correct but some parts in the ground truth are not mentioned in the response: 0.5 point
-- Some part in the response are correct but other parts in the response are not mentioned in the ground truth: 0.5 point
-
-Here're some examples about the scoring criterion and format:
-model response: Steam Cleaning Services
-ground truth: ["steam clean", "steam clean", "cleaning", "car", "steam clean"],
-Point: 1
-
-model response: A cowboy action shooter.
-ground truth: ["man"]
-Point: 1
-
-model response: I'm sorry, but I can't assist with that request.
-ground truth: ["quality"]
-Point: 0
-
-Let's begin this task:
-model response: {model_response}
-ground truth: {ground_truth}
-Point:"""
-
-
-def get_eval(model_response: str, ground_truth: str, max_tokens: int, retries: int = 5):
-    global headers
-    content = eval_prompt.format(model_response=model_response, ground_truth=ground_truth)
-
-    messages = [
-        {"role": "user", "content": content},
-    ]
-
-    payload = {
-        "model": GPT_EVAL_MODEL_NAME,
-        "messages": messages,
-        "temperature": 0.2,
-        "max_tokens": max_tokens,
-    }
-
-    for attempt in range(retries):
-        try:
-            response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
-            response.raise_for_status()
-            response_data = response.json()
-
-            content = response_data["choices"][0]["message"]["content"].strip()
-            if content != "":
-                return content, response_data["model"]
-            break  # If successful, break out of the loop
-
-        except Exception as e:
-            eval_logger.info(f"Attempt {attempt + 1} failed with error: {e}")
-            if attempt < retries:  # If we have retries left, sleep and then continue to next attempt
-                time.sleep(NUM_SECONDS_TO_SLEEP)
-            else:  # If this was the last attempt, log and return empty
-                eval_logger.error(f"All {retries} attempts failed. Last error message: {e}")
-                return "", ""
-    return "", ""
-
-
-# A bit ugly here
-# But the idea is that we will unzip all the zip files
-# To HF HOME cache dir
-# And load it here
-HF_HOME = os.environ["HF_HOME"]
-cache_dir = config["dataset_kwargs"]["cache_dir"]
-cache_dir = os.path.join(HF_HOME, cache_dir)
-cache_dir = os.path.join(cache_dir)
-
-
-eval_logger = logging.getLogger("lmms-eval")
-
-
-# Pass in video path here
-# Can only work correctly with video llm
-def mix_evals_video2text_doc_to_visual(doc):
-    video_path = doc["video_path"]
-    video_path = os.path.join(cache_dir, video_path)
-    if os.path.exists(video_path):
-        video_path = video_path
-    elif os.path.exists(video_path.replace("mp4", "MP4")):
-        video_path = video_path.replace("mp4", "MP4")
-    else:
-        sys.exit(f"video path:{video_path} does not exist, please check")
-    return [video_path]
-
-
-# This is the place where you format your question
-def mix_evals_video2text_doc_to_text(doc, model_specific_prompt_kwargs=None):
-    if model_specific_prompt_kwargs is None:
-        model_specific_prompt_kwargs = {}
-    pre_prompt = ""
-    post_prompt = ""
-    if "pre_prompt" in model_specific_prompt_kwargs:
-        pre_prompt = model_specific_prompt_kwargs["pre_prompt"]
-    if "post_prompt" in model_specific_prompt_kwargs:
-        post_prompt = model_specific_prompt_kwargs["post_prompt"]
-
-    user_prompt = doc["prompt"]
-
-    if "options" in doc:
-        option_prompt = "Here are the options:\n"
-        for idx, option in enumerate(doc["options"]):
-            char_idx = chr(ord("A") + idx)
-            option = option.strip()
-            option_prompt += f"{char_idx}. {option}\n"
-
-        option_prompt = option_prompt.rstrip("\n")
-        user_prompt = f"{user_prompt}\n{option_prompt}"
-
-    if pre_prompt:
-        user_prompt = f"{pre_prompt}\n{user_prompt}"
-
-    if post_prompt:
-        user_prompt = f"{user_prompt}\n{post_prompt}"
-    return user_prompt
-
-
-def mix_evals_video2text_doc_to_text_open_convs(doc, model_specific_prompt_kwargs=None):
-    if model_specific_prompt_kwargs is None:
-        model_specific_prompt_kwargs = {}
-    pre_prompt = ""
-    post_prompt = ""
-    if "pre_prompt" in model_specific_prompt_kwargs:
-        pre_prompt = model_specific_prompt_kwargs["pre_prompt"]
-    if "post_prompt" in model_specific_prompt_kwargs:
-        post_prompt = model_specific_prompt_kwargs["post_prompt"]
-
-    formatted_prompt = ""
-    first_turn_user_prompt = doc["first_turn_user_prompt"]
-
-    if pre_prompt:
-        formatted_prompt = f"{pre_prompt}\n{first_turn_user_prompt}"
-    else:
-        formatted_prompt = f"{first_turn_user_prompt}"
-
-    if "round2" in doc and doc["round2"]:
-        second_turn_user_prompt = doc["second_turn_user_prompt"]
-        formatted_prompt += f"{formatted_prompt}\n{second_turn_user_prompt}"
-        if post_prompt:
-            formatted_prompt += f"{formatted_prompt}\n{post_prompt}"
-        return formatted_prompt
-    else:
-        if post_prompt:
-            formatted_prompt += f"{formatted_prompt}\n{post_prompt}"
-        return formatted_prompt
-
-
-def mix_evals_video2text_process_results_open_convs(doc, result):
-    pred = result[0]
-    return {"submission": {"pred": pred, "question_idx": doc["question_index"], "first_turn_video_caption": doc["first_turn_video_caption"], "target": ""}}
-
-
-def mix_evals_video2text_process_results_freeform(doc, result):
-    pred = result[0]
-    ground_truth_str = ", ".join([f'"{gt}"' for gt in doc["target"]])
-    ground_truth_str = f"[{ground_truth_str}]"
-    content = eval_prompt.format(model_response=pred, ground_truth=ground_truth_str)
-    eval_answer, model_name = get_eval(model_response=pred, ground_truth=ground_truth_str, max_tokens=1024)
-    return {
-        "submission": {"pred": pred, "question_idx": doc["question_index"], "target": doc["target"], "eval_answer": eval_answer, "gpt_prompt": content},
-        "gpt_eval": {"pred": pred, "question_idx": doc["question_index"], "target": doc["target"], "eval_answer": eval_answer, "gpt_prompt": content},
-    }
-
-
-def mix_evals_video2text_aggregate_submissions(results, args, task):
-    now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
-    submission_file_name = f"mix_evals_video2text_{task}-{now_date_time}.json"
-    path = file_utils.generate_submission_file(submission_file_name, args)
-    with open(path, "w") as f:
-        json.dump(results, f)
-    eval_logger.info(f"Submission file saved to {path}")
-
-
-def mix_evals_video2text_gpt_eval(results, args):
-    score = 0
-    for result in results:
-        eval_answer = result["eval_answer"]
-        eval_score = re.search(r"([0-9.]+)", eval_answer).group(1)
-        try:
-            eval_score = float(eval_score)
-        except Exception as e:
-            eval_logger.error(f"Error parsing eval_score: {e}")
-            eval_score = 0.0
-        score += eval_score
-
-    return score / len(results)
-
-
-# Factory into different aggregate
-def mix_evals_video2text_aggregate_gen(results, args):
-    mix_evals_video2text_aggregate_submissions(results, args, "OpenConvs")
-
-
-class MultiChoiceRegexFilter(ExtendedRegexFilter):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def apply(self, resps, docs):
-        filtered_resps = []
-
-        for r, doc in zip(resps, docs):
-            # Regex to directly extract the option letter from the model response
-            option_letter_regex = re.compile(r"\b([A-Z])\.\s+([^\n]*)")
-
-            # Process each response
-            filtered = []
-            for resp in r:
-                # Try to match the option letter at the start of the response
-                match = option_letter_regex.match(resp)
-                if match:
-                    # If a match is found, append the matched letter
-                    filtered.append(match.group(1))
-                else:
-                    # If no match, return the original response
-                    filtered.append(resp)
-
-            # Assuming we need the first response that matches or the original response
-            filtered_resps.append(filtered[0])
-
-        return filtered_resps
diff --git a/lmms_eval/tasks/mmupd/_default_template_mmupd_yaml b/lmms_eval/tasks/mmupd/_default_template_mmupd_yaml
new file mode 100644
index 00000000..7aa8d812
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/_default_template_mmupd_yaml
@@ -0,0 +1,18 @@
+dataset_path: MM-UPD/MM-UPD
+doc_to_target: "answer"
+doc_to_visual: !function utils.mmupd_doc_to_visual
+doc_to_text: !function utils.mmupd_doc_to_text
+doc_to_target: "answer"
+process_results: !function utils.mmupd_process_results
+model_specific_generation_kwargs:
+  llava:
+    image_aspect_ratio: original
+output_type: generate_until
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+  max_new_tokens: 1024
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
diff --git a/lmms_eval/tasks/mmupd/mmaad_base.yaml b/lmms_eval/tasks/mmupd/mmaad_base.yaml
new file mode 100644
index 00000000..9a66b3e8
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmaad_base.yaml
@@ -0,0 +1,12 @@
+task: "mmaad_base"
+test_split: test
+dataset_name: mmaad_base
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\n"
+include: _default_template_mmupd_yaml
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mmaad_base
+    higher_is_better: true
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmaad_instruction.yaml b/lmms_eval/tasks/mmupd/mmaad_instruction.yaml
new file mode 100644
index 00000000..30a3bac1
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmaad_instruction.yaml
@@ -0,0 +1,12 @@
+task: "mmaad_instruction"
+test_split: test
+dataset_name: mmaad_base
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"."
+include: _default_template_mmupd_yaml
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mmaad_instruction
+    higher_is_better: true
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmaad_option.yaml b/lmms_eval/tasks/mmupd/mmaad_option.yaml
new file mode 100644
index 00000000..f110b822
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmaad_option.yaml
@@ -0,0 +1,12 @@
+task: "mmaad_option"
+test_split: test
+dataset_name: mmaad_option
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\nAnswer with the option's letter from the given choices directly."
+include: _default_template_mmupd_yaml
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mmaad_option
+    higher_is_better: true
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmiasd_base.yaml b/lmms_eval/tasks/mmupd/mmiasd_base.yaml
new file mode 100644
index 00000000..6a9159fd
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmiasd_base.yaml
@@ -0,0 +1,12 @@
+task: "mmiasd_base"
+test_split: test
+dataset_name: mmiasd_base
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\n"
+include: _default_template_mmupd_yaml
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mmiasd_base
+    higher_is_better: true
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml b/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml
new file mode 100644
index 00000000..d0ac3a00
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmiasd_instruction.yaml
@@ -0,0 +1,12 @@
+task: "mmiasd_instruction"
+test_split: test
+dataset_name: mmiasd_base
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\nIf all the options are incorrect, answer \"F. None of the above\"."
+include: _default_template_mmupd_yaml
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mmiasd_instruction
+    higher_is_better: true
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmiasd_option.yaml b/lmms_eval/tasks/mmupd/mmiasd_option.yaml
new file mode 100644
index 00000000..a1a1aead
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmiasd_option.yaml
@@ -0,0 +1,12 @@
+task: "mmiasd_option"
+test_split: test
+dataset_name: mmiasd_option
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\nAnswer with the option's letter from the given choices directly."
+include: _default_template_mmupd_yaml
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mmiasd_option
+    higher_is_better: true
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmivqd_base.yaml b/lmms_eval/tasks/mmupd/mmivqd_base.yaml
new file mode 100644
index 00000000..82853fde
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmivqd_base.yaml
@@ -0,0 +1,12 @@
+task: "mmivqd_base"
+test_split: test
+dataset_name: mmivqd_base
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\n"
+include: _default_template_mmupd_yaml
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mmivqd_base
+    higher_is_better: true
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml b/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml
new file mode 100644
index 00000000..a024022a
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmivqd_instruction.yaml
@@ -0,0 +1,12 @@
+task: "mmivqd_instruction"
+test_split: test
+dataset_name: mmivqd_base
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\nIf the given image is irrelevant to the question, answer \"F. The image and question are irrelevant.\"."
+include: _default_template_mmupd_yaml
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mmivqd_instruction
+    higher_is_better: true
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmivqd_option.yaml b/lmms_eval/tasks/mmupd/mmivqd_option.yaml
new file mode 100644
index 00000000..1363c345
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmivqd_option.yaml
@@ -0,0 +1,12 @@
+task: "mmivqd_option"
+test_split: test
+dataset_name: mmivqd_option
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "\nAnswer with the option's letter from the given choices directly."
+include: _default_template_mmupd_yaml
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.mmivqd_option
+    higher_is_better: true
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmupd.yaml b/lmms_eval/tasks/mmupd/mmupd.yaml
new file mode 100644
index 00000000..9bb09a7d
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmupd.yaml
@@ -0,0 +1,15 @@
+group: mmupd
+task:
+  - mmaad_base
+  - mmaad_option
+  - mmaad_instruction
+  - mmiasd_base
+  - mmiasd_option
+  - mmiasd_instruction
+  - mmivqd_base
+  - mmivqd_option
+  - mmivqd_instruction
+metadata:
+  version: 0.0
+  sys_prompt: ""
+  gpt_eval_model_name: "gpt-3.5-turbo-0125"
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmupd_base.yaml b/lmms_eval/tasks/mmupd/mmupd_base.yaml
new file mode 100644
index 00000000..8f0fe147
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmupd_base.yaml
@@ -0,0 +1,10 @@
+group: mmupd_base
+task:
+  - mmaad_base
+  - mmiasd_base
+  - mmivqd_base
+metadata:
+  version: 0.0
+  sys_prompt: ""
+  gpt_eval_model_name: "gpt-3.5-turbo-0125"
+  
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmupd_evals.py b/lmms_eval/tasks/mmupd/mmupd_evals.py
new file mode 100644
index 00000000..53055313
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmupd_evals.py
@@ -0,0 +1,621 @@
+import time
+import random as rd
+import string
+from collections import defaultdict
+import requests
+import math
+import numpy as np
+import pandas as pd
+import pickle
+import logging
+import json
+
+eval_logger = logging.getLogger("lmms-eval")
+
+
+def dump(data, f):
+    def dump_pkl(data, pth):
+        pickle.dump(data, open(pth, "wb"))
+
+    def dump_json(data, pth):
+        json.dump(data, open(pth, "w"))
+
+    def dump_jsonl(data, f):
+        lines = [json.dumps(x, ensure_ascii=False) for x in data]
+        with open(f, "w", encoding="utf8") as fout:
+            fout.write("\n".join(lines))
+
+    def dump_xlsx(data, f):
+        data.to_excel(f, index=False)
+
+    def dump_csv(data, f):
+        data.to_csv(f, index=False)
+
+    def dump_tsv(data, f):
+        data.to_csv(f, sep="\t", index=False)
+
+    handlers = dict(pkl=dump_pkl, json=dump_json, jsonl=dump_jsonl, xlsx=dump_xlsx, csv=dump_csv, tsv=dump_tsv)
+    suffix = f.split(".")[-1]
+    return handlers[suffix](data, f)
+
+
+def load(f):
+    """
+    Loads data from various file formats.
+
+    Parameters:
+    - file_path: Path to the file to be loaded.
+
+    Returns:
+    - Loaded data.
+    """
+
+    def load_pkl(pth):
+        return pickle.load(open(pth, "rb"))
+
+    def load_json(pth):
+        return json.load(open(pth, "r", encoding="utf-8"))
+
+    def load_jsonl(f):
+        lines = open(f, encoding="utf-8").readlines()
+        lines = [x.strip() for x in lines]
+        if lines[-1] == "":
+            lines = lines[:-1]
+        data = [json.loads(x) for x in lines]
+        return data
+
+    def load_xlsx(f):
+        df = pd.read_excel(f)
+        df = df.dropna(subset=["prediction"])
+
+        return df
+
+    def load_csv(f):
+        return pd.read_csv(f)
+
+    def load_tsv(f):
+        return pd.read_csv(f, sep="\t")
+
+    handlers = dict(pkl=load_pkl, json=load_json, jsonl=load_jsonl, xlsx=load_xlsx, csv=load_csv, tsv=load_tsv)
+    suffix = f.split(".")[-1]
+    return handlers[suffix](f)
+
+
+class MMUPD_Evaluator:
+    def __init__(self, sys_prompt="There are several options:", API_KEY="", API_URL="", model_version="gpt-3.5-turbo-0613"):
+        self.sys_prompt = sys_prompt
+        self.model_version = model_version
+        self.API_KEY = API_KEY
+        self.API_URL = API_URL
+
+    def create_options_prompt(self, row_data, option_candidate):
+        available_keys = set(row_data.keys()) & set(option_candidate)
+        options = {cand: row_data[cand] for cand in available_keys if row_data[cand]}
+        sorted_options = dict(sorted(options.items()))
+        options_prompt = f"{self.sys_prompt}\n"
+        for key, item in sorted_options.items():
+            if pd.notna(item) and item != "nan":
+                options_prompt += f"{key}. {item}\n"
+        return options_prompt.rstrip("\n"), sorted_options
+
+    def is_none(self, value):
+        if value is None:
+            return True
+        if pd.isna(value):
+            return True
+        if type(value) is float and math.isnan(value):
+            return True
+        if type(value) is str and value.lower() == "nan":
+            return True
+        if type(value) is str and value.lower() == "none":
+            return True
+        return False
+
+    # Prompt Building
+    def build_option_str(self, option_list):
+        chars = string.ascii_uppercase
+        s = "There are several options: \n"
+        for c, opt in zip(chars, option_list):
+            if not pd.isna(opt):
+                s += f"{c}. {opt}\n"
+            else:
+                return s
+        return s
+
+    def extract_options(self, item):
+        options = []
+        for c in "ABCDE":
+            try:
+                if self.is_none(item[c]) is False:
+                    options.append(item[c])
+            except KeyError:
+                continue
+        return options
+
+    def build_choices(self, item):
+        ret = {}
+        for ch in "ABCDE":
+            try:
+                if self.is_none(item[ch]) is False:
+                    ret[ch] = item[ch]
+            except KeyError:
+                continue
+        return ret
+
+    def build_option_str_w_gt(self, option_list, gt_text, eval_type, question_type, upd_type):
+        chars = string.ascii_uppercase
+        s = "There are several options: \n"
+        valid_option = []
+        answer_option = []
+
+        for c, opt in zip(chars, option_list):
+            if self.is_none(opt) is False:
+                s += f"{c}. {opt}\n"
+                valid_option.append(c)
+        if upd_type == "aad":
+            if eval_type == "aad":
+                gt_option = ""
+                for option in ["C", "D", "E", "F"]:
+                    if option not in valid_option:
+                        gt_option = option
+                        break
+                none_option_mapping = {"C": "D", "D": "E", "E": "F", "F": "G"}
+                s += f"{gt_option}. {gt_text}\n"
+                none_option = none_option_mapping[gt_option]
+                s += f"{none_option}. The correct answer is No answer, None of the above, \
+                    all provided options are incorrect, or I cannot answer.\n"
+                valid_option.append(gt_option)
+                valid_option.append(none_option)
+                answer_option = [gt_option, none_option]
+                if question_type == "inst":
+                    s += f"F. None of the above\n"
+                    valid_option.append("F")
+                    answer_option = [gt_option, none_option, "F"]
+
+            if eval_type == "standard":
+                none_option = ""
+                for option in ["C", "D", "E", "F"]:
+                    if option not in valid_option:
+                        none_option = option
+                        break
+                s += f"{none_option}. The correct answer is No answer, None of the above, \
+                    all provided options are incorrect, or I cannot answer.\n"
+                valid_option.append(none_option)
+                if question_type == "inst":
+                    s += "F. None of the above\n"
+                    valid_option.append("F")
+        elif upd_type == "iasd":
+            if eval_type == "iasd":
+                gt_option = ""
+                for option in ["C", "D", "E", "F"]:
+                    if option not in valid_option:
+                        gt_option = option
+                        break
+
+                s += f"{gt_option}. {gt_text}\n"
+                valid_option.append(gt_option)
+
+                if question_type == "inst":
+                    if gt_option == "E":
+                        s += f"F. None of the above\n"
+                        valid_option.append("F")
+                        s += "G. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n"
+                        valid_option.append("G")
+                        answer_option = [gt_option, "F", "G"]
+                    else:
+                        none_option_mapping = {"C": "D", "D": "E"}
+                        none_option = none_option_mapping[gt_option]
+                        s += f"{none_option}. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n"
+                        valid_option.append(none_option)
+                        s += f"F. None of the above\n"
+                        valid_option.append("F")
+                        answer_option = [gt_option, none_option, "F"]
+                else:
+                    none_option_mapping = {"C": "D", "D": "E", "E": "F", "F": "G"}
+                    none_option = none_option_mapping[gt_option]
+                    s += f"{none_option}. The correct answer is No answer, None of the above, all provided options are irrelevant or incorrect, or I cannot answer.\n"
+                    valid_option.append(none_option)
+                    answer_option = [gt_option, none_option]
+
+            if eval_type == "standard":
+                none_option = ""
+                for option in ["C", "D", "E", "F"]:
+                    if option not in valid_option:
+                        none_option = option
+                        break
+                s += f"{none_option}. The correct answer is No answer, None of the above, \
+                    all provided options are irrelevant or incorrect, or I cannot answer.\n"
+                valid_option.append(none_option)
+                if question_type == "inst":
+                    s += f"F. None of the above\n"
+                    valid_option.append("F")
+        elif upd_type == "ivqd":
+            if eval_type == "ivqd":
+                none_option = ""
+                for option in ["C", "D", "E", "F"]:
+                    if option not in valid_option:
+                        none_option = option
+                        break
+                s += f"{none_option}. The correct answer is that The image is incompatible with the question, or I cannot answer.\n"
+                valid_option.append(none_option)
+                answer_option = [none_option]
+                if question_type == "inst":
+                    s += f"F. The image and question are irrelevant.\n"
+                    valid_option.append("F")
+                    answer_option = [none_option, "F"]
+
+            if eval_type == "standard":
+                none_option = ""
+                for option in ["C", "D", "E", "F"]:
+                    if option not in valid_option:
+                        none_option = option
+                        break
+                s += f"{none_option}. The correct answer is that The image is incompatible with the question, or I cannot answer.\n"
+                valid_option.append(none_option)
+                if question_type == "inst":
+                    s += f"F. The image and question are irrelevant.\n"
+                    valid_option.append("F")
+        return s, valid_option, answer_option
+
+    def build_prompt(self, question, options, prediction, upd_type):
+        if upd_type == "aad":
+            tmpl = (
+                "You are an AI assistant who will help me to match an answer "
+                "with several options of a single-choice question. "
+                "You are provided with a question, several options, and an answer, "
+                "and you need to find which option is most similar to the answer. "
+                "If the meaning of all options are significantly different "
+                "from the answer, output H. "
+                "Your should output a single uppercase character in A, B, C, D, E, F, G "
+                "(if they are valid options), and H. \n"
+                "Example 1: \n"
+                "Question: What is the main object in image?\nOptions: A. teddy bear "
+                "B. rabbit C. cat D. dog E. no answer \nAnswer: a cute teddy bear\nYour output: A\n"
+                "Example 2: \n"
+                "Question: What is the main object in image?\nOptions: A. teddy bear "
+                "B. rabbit C. cat D. dog E. None of the above  \nAnswer: no answer \nYour output: E\n"
+                "Example 3: \n"
+                "Question: What is the main object in image?\nOptions: A. teddy bear "
+                "B. rabbit C. cat D. dog E. None of the above \nAnswer: fish \nYour output: H\n"
+                "Example 4: \n"
+                "Question: {}?\nOptions: {}\nAnswer: {}\nYour output: "
+            )
+        elif upd_type == "iasd":
+            tmpl = (
+                "You are an AI assistant who will help me to match an answer "
+                "with several options of a single-choice question. "
+                "You are provided with a question, several options, and an answer, "
+                "and you need to find which option is most similar to the answer. "
+                "If the meaning of all options are significantly different "
+                "from the answer, output H. "
+                "Your should output a single uppercase character in A, B, C, D, E, F, G "
+                "(if they are valid options), and H. \n"
+                "Example 1: \n"
+                "Question: What is the main object in image?\nOptions: A. teddy bear "
+                "B. rabbit C. cat D. dog E. no answer \nAnswer: a cute teddy bear\nYour output: A\n"
+                "Example 2: \n"
+                "Question: What is the main object in image?\nOptions: A. teddy bear "
+                "B. rabbit C. cat D. dog E. None of the above  \nAnswer: no answer \nYour output: E\n"
+                "Example 3: \n"
+                "Question: What is the main object in image?\nOptions: A. teddy bear "
+                "B. rabbit C. cat D. dog E. None of the above \nAnswer: fish \nYour output: H\n"
+                "Example 4: \n"
+                "Question: {}?\nOptions: {}\nAnswer: {}\nYour output: "
+            )
+        elif upd_type == "ivqd":
+            tmpl = (
+                "You are an AI assistant who will help me to match an answer "
+                "with several options of a single-choice question. "
+                "You are provided with a question, several options, and an answer, "
+                "and you need to find which option is most similar to the answer. "
+                "If the meaning of all options are significantly different "
+                "from the answer, output H. "
+                "Your should output a single uppercase character in A, B, C, D, E, F, G "
+                "(if they are valid options), and H. \n"
+                "Example 1: \n"
+                "Question: What is the main object in image?\nOptions: A. teddy bear "
+                "B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: a cute teddy bear\nYour output: A\n"
+                "Example 2: \n"
+                "Question: What is the main object in image?\nOptions: A. teddy bear "
+                "B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: The updloaded image and question are incompatible. \nYour output: E\n"
+                "Example 3: \n"
+                "Question: What is the main object in image?\nOptions: A. teddy bear "
+                "B. rabbit C. cat D. dog E. The image and question are irrelevant \nAnswer: fish \nYour output: H\n"
+                "Example 4: \n"
+                "Question: {}?\nOptions: {}\nAnswer: {}\nYour output: "
+            )
+        return tmpl.format(question, options, prediction)
+
+    # Prefetch Answers
+    def can_infer_option(self, answer, option_dict, question_type=None, valid_option=None):
+        if valid_option is None:
+            valid_option = list(option_dict.keys())
+            if question_type == "inst":
+                valid_option.append("F")
+
+        if "Failed to obtain answer via API" in answer:
+            return False
+
+        answer = answer.strip()
+
+        ch_cand_list = []
+
+        punctuations = [".", ")", ","]
+        if "A" in valid_option:
+            characters = ["B", "C", "D", "E", "F", "G"]
+            combinations = [char + punct for char in characters for punct in punctuations]
+            start_patterns = ["A)", "A.", "A,", "(A)"]
+            if answer == "A" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)):
+                ch_cand_list.append("A")
+        if "B" in valid_option:
+            characters = ["A", "C", "D", "E", "F", "G"]
+            combinations = [char + punct for char in characters for punct in punctuations]
+            start_patterns = ["B)", "B.", "B,", "(B)"]
+            if answer == "B" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)):
+                ch_cand_list.append("B")
+        if "C" in valid_option:
+            characters = ["A", "B", "D", "E", "F", "G"]
+            combinations = [char + punct for char in characters for punct in punctuations]
+            start_patterns = ["C)", "C.", "C,", "(C)"]
+            if answer == "C" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)):
+                ch_cand_list.append("C")
+        if "D" in valid_option:
+            characters = ["A", "B", "C", "E", "F", "G"]
+            combinations = [char + punct for char in characters for punct in punctuations]
+            start_patterns = ["D)", "D.", "D,", "(D)"]
+            if answer == "D" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)):
+                ch_cand_list.append("D")
+        if "E" in valid_option:
+            characters = ["A", "B", "C", "D", "F", "G"]
+            combinations = [char + punct for char in characters for punct in punctuations]
+            start_patterns = ["E)", "E.", "E,", "(E)"]
+            if answer == "E" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)):
+                ch_cand_list.append("E")
+        if "F" in valid_option:
+            characters = ["A", "B", "C", "D", "E", "G"]
+            combinations = [char + punct for char in characters for punct in punctuations]
+            start_patterns = ["F)", "F.", "F,", "(F)"]
+            if answer == "F" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)):
+                ch_cand_list.append("F")
+        if "G" in valid_option:
+            characters = ["A", "B", "C", "D", "E", "F"]
+            combinations = [char + punct for char in characters for punct in punctuations]
+
+            start_patterns = ["G)", "G.", "G,", "(G)"]
+            if answer == "G" or (any(answer.startswith(pattern) for pattern in start_patterns) and all(x not in answer for x in combinations)):
+                ch_cand_list.append("G")
+
+        if len(ch_cand_list) == 1:
+            return ch_cand_list[0]
+
+        return False
+
+    def can_infer(self, answer, choices, question_type=None, valid_option=None):
+        copt = self.can_infer_option(answer, choices, question_type, valid_option=valid_option)
+        return copt if copt else False
+
+    def prefetch_answer(self, item, question_type):
+        choices = self.build_choices(item)
+        return self.can_infer(item["prediction"], choices, question_type=question_type)
+
+    def _post_request(self, payload):
+        headers = {
+            "Authorization": f"Bearer {self.API_KEY}",
+            "Content-Type": "application/json",
+        }
+        response = requests.post(self.API_URL, headers=headers, json=payload, timeout=30)
+        response.raise_for_status()
+        return response.json()
+
+    def get_chat_response(self, prompt, temperature=0, max_tokens=256, n=1, patience=5, sleep_time=3):
+        messages = [
+            {"role": "user", "content": prompt},
+        ]
+        payload = {"model": self.model_version, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, "n": n}
+
+        while patience > 0:
+            patience -= 1
+            try:
+                response = self._post_request(payload)
+                if n == 1:
+                    prediction = response["choices"][0]["message"]["content"].strip()
+                    if prediction and prediction != "":
+                        return prediction
+                else:
+                    prediction = [choice["message"]["content"].strip() for choice in response["choices"]]
+                    if prediction and prediction[0] != "":
+                        return prediction
+
+            except Exception as e:
+                eval_logger.info(f"Attempt {patience + 1} failed with error: {e}")
+                if sleep_time > 0:
+                    time.sleep(sleep_time)
+
+        return "Failed to obtain answer via API"
+
+    def extract_answer_from_item(self, item, gt_text, eval_type, question_type, upd_type):
+        options = self.extract_options(item)
+        option_str, valid_option, answer_option = self.build_option_str_w_gt(options, gt_text, eval_type, question_type=question_type, upd_type=upd_type)
+
+        prompt = self.build_prompt(item["question"], option_str, item["prediction"], upd_type=upd_type)
+        retry = 3
+        choices = self.build_choices(item)
+
+        ret = self.can_infer(item["prediction"], choices, valid_option=valid_option)
+        if ret:
+            return ret, item["prediction"], answer_option
+
+        while retry:
+            ans = self.get_chat_response(prompt, temperature=0.7)
+            if "Failed to obtain answer via API" in ans:
+                msg = "GPT API failed to answer. "
+                eval_logger.info(msg)
+                retry -= 1
+            else:
+                ret = self.can_infer(ans, choices, valid_option=valid_option)
+                if ret:
+                    return ret, ans, answer_option
+                else:
+                    eval_logger.info(f'GPT output includes 0 / >1 letter in "{valid_option}": {ans}')
+                    retry -= 1
+
+            if retry == 0:
+                return "H", "Failed to predict. ", answer_option
+
+    def eval_sub_data(self, sub_data, answer_map, gt_text_map, question_type, eval_type, upd_type):
+        lt = len(sub_data)
+        GT, PRED = [], []
+
+        for i in range(lt):
+            item = sub_data.iloc[i]
+            idx = item["index"]
+            GT.append(answer_map[idx])
+            PRED.append(self.prefetch_answer(item, question_type))
+            if PRED[-1] and (GT[-1] != PRED[-1]):
+                return 0
+
+        for i in range(lt):
+            if PRED[i]:
+                continue
+            else:
+                item = sub_data.iloc[i]
+                idx = item["index"]
+                gt_text = gt_text_map[idx] if gt_text_map is not None else None
+                ret, _, answer_option = self.extract_answer_from_item(sub_data.iloc[i], gt_text, eval_type, question_type=question_type, upd_type=upd_type)
+                PRED[i] = ret
+                if eval_type == "standard":
+                    if PRED[i] != GT[i]:
+                        return 0
+                else:
+                    if GT[i] == "F":
+                        if PRED[i] not in answer_option:
+                            return 0
+                    else:
+                        if PRED[i] != GT[i] and PRED[i] not in answer_option:
+                            return 0
+        return 1
+
+    def calculate_hit_rates(self, data):
+        overall_hit_rate = data["hit"].mean()
+
+        category_hit_rate = {}
+        if "category" in data.columns:
+            # Category-based hit rate
+            category_hit_rate = data.groupby("category")["hit"].mean().to_dict()
+
+        return overall_hit_rate, category_hit_rate
+
+    # Evaluate Results
+    def eval_result(self, results, eval_method, upd_type, question_type, eval_type):
+        """
+        Parameters:
+        - args: Arguments.
+        - results: Results to evaluate.
+        - eval_method: The evaluation method. either "openai".
+        - upd_type: The type of UPD. either "aad", "iasd", or "ivqd".
+        - question_type: The type of question. either "base", "option", or "inst".
+        - eval_type: The type of evaluation. either "standard", "aad", "iasd", "ivqd".
+        """
+
+        rd.seed(2680)
+        assert eval_method == "openai"
+
+        result = {}
+        data = pd.DataFrame(results)
+
+        if eval_type == "standard":
+            data = data[data["type"] == "standard"]
+        else:
+            data = data[data["type"] == "upd"]
+
+        data = data.sort_values(by="index")
+
+        data["prediction"] = [str(x) for x in data["prediction"]]
+        for k in data.keys():
+            data[k.lower() if k not in "ABCDE" else k] = data.pop(k)
+
+        # meta = load(meta_file)
+
+        data_main = data[data["index"] < int(1e6)]
+
+        data_main["hit"] = 0
+        cate_map = {i: c for i, c in zip(data["index"], data["category"])}
+        answer_map = {i: c for i, c in zip(data["index"], data["answer"])}
+
+        gt_text_map = {i: c for i, c in zip(data["index"], data["masked_answer"])}
+
+        lt = len(data_main)
+        hit, tot = 0, 0
+
+        for i in range(lt):
+            # Dealing with the normal part
+            item_main = data_main.iloc[i]
+            idx = item_main["index"]
+
+            if idx in result:
+                correct = result[idx]
+                assert correct in [0, 1]
+                hit += correct
+                tot += 1
+                continue
+
+            sub_data = data[data["index"] % int(1e6) == idx]
+
+            ret = self.eval_sub_data(sub_data, answer_map, gt_text_map, question_type=question_type, eval_type=eval_type, upd_type=upd_type)
+            result[idx] = ret
+            hit += ret
+            tot += 1
+
+            data_main.loc[data_main["index"] == idx, "hit"] = ret
+
+        indices = data_main["index"]
+
+        data_main["category"] = [cate_map[i] if not math.isnan(i) else "uncategorized" for i in indices]
+
+        overall_hit_rate, category_hit_rate = self.calculate_hit_rates(data_main)
+
+        return overall_hit_rate, category_hit_rate, data_main
+
+    def report_acc(self, df, groupd="category"):
+        assert "split" in df
+        assert groupd in [None, "category", "l2-category"]
+
+        res = defaultdict(list)
+        res["split"] = ["test"]
+        if groupd is None:
+            res["overall"] = [
+                np.mean(df["hit"]),
+            ]
+            return pd.DataFrame(res)
+
+        elif groupd in df:
+            abilities = list(set(df[groupd]))
+            abilities.sort()
+            for ab in abilities:
+                sub_df = df[df[groupd] == ab]
+                res[ab] = [
+                    np.mean(sub_df["hit"]),
+                ]
+            return pd.DataFrame(res)
+
+    def calculate_dual_acc(self, standard_df, upd_df):
+        # dual results
+        dual_df = pd.merge(standard_df, upd_df, on="index", suffixes=("_standard", "_upd"))
+        dual_df["hit"] = dual_df.apply(lambda row: 1 if row["hit_standard"] == 1 and row["hit_upd"] == 1 else 0, axis=1)
+        dual_df["split"] = dual_df["split_standard"]
+        dual_df["category"] = dual_df["category_standard"]
+
+        # Evaluate dual results
+        overall_hit_rate, category_hit_rate = self.calculate_hit_rates(dual_df)
+        print("Overall Dual Acc.", overall_hit_rate)
+
+        if "category" in dual_df.columns:
+            print("Category Dual Acc.:")
+            for category_key in category_hit_rate:
+                if category_key == "split":
+                    continue
+
+                category_percentage = category_hit_rate[category_key] * 100
+                print(f"\t{category_key}: {category_percentage:.3f}")
+
+        return overall_hit_rate, category_hit_rate, dual_df
diff --git a/lmms_eval/tasks/mmupd/mmupd_instruction.yaml b/lmms_eval/tasks/mmupd/mmupd_instruction.yaml
new file mode 100644
index 00000000..6aae6049
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmupd_instruction.yaml
@@ -0,0 +1,9 @@
+group: mmupd_instruction
+task:
+  - mmaad_instruction
+  - mmiasd_instruction
+  - mmivqd_instruction
+metadata:
+  version: 0.0
+  sys_prompt: ""
+  gpt_eval_model_name: "gpt-3.5-turbo-0125"
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/mmupd_option.yaml b/lmms_eval/tasks/mmupd/mmupd_option.yaml
new file mode 100644
index 00000000..32e514d9
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/mmupd_option.yaml
@@ -0,0 +1,9 @@
+group: mmupd_option
+task:
+  - mmaad_option
+  - mmiasd_option
+  - mmivqd_option
+metadata:
+  version: 0.0
+  sys_prompt: ""
+  gpt_eval_model_name: "gpt-3.5-turbo-0125"
\ No newline at end of file
diff --git a/lmms_eval/tasks/mmupd/utils.py b/lmms_eval/tasks/mmupd/utils.py
new file mode 100644
index 00000000..9a90bf4b
--- /dev/null
+++ b/lmms_eval/tasks/mmupd/utils.py
@@ -0,0 +1,167 @@
+import logging
+import yaml
+import os
+from pathlib import Path
+import pandas as pd
+import json
+from PIL import Image
+from io import BytesIO
+import base64
+from lmms_eval.tasks.mmupd.mmupd_evals import MMUPD_Evaluator
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+
+eval_logger = logging.getLogger("lmms-eval")
+
+with open(Path(__file__).parent / "mmupd.yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+
+    config = yaml.safe_load("".join(safe_data))
+
+GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"]
+API_TYPE = os.getenv("API_TYPE", "openai")
+
+if API_TYPE == "openai":
+    API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
+    API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
+elif API_TYPE == "azure":
+    API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
+    API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
+
+
+mmupd_evaluator = MMUPD_Evaluator(sys_prompt=config["metadata"]["sys_prompt"], API_KEY=API_KEY, API_URL=API_URL, model_version=GPT_EVAL_MODEL_NAME)
+
+
+def mmupd_doc_to_visual(doc):
+    return [Image.open(BytesIO(base64.b64decode(doc["image"])))]
+
+
+def mmupd_doc_to_text(doc, model_specific_prompt_kwargs=None):
+    option_candidate = ["A", "B", "C", "D", "E"]
+    options_prompt, options_dict = mmupd_evaluator.create_options_prompt(doc, option_candidate)
+
+    data = {
+        # "img": doc["image"],
+        "question": doc["question"],
+        "answer": doc.get("answer", None),
+        "options": options_prompt,
+        "category": doc["category"],
+        "options_dict": options_dict,
+        "index": doc["index"],
+        "hint": doc["hint"],
+        "source": doc["source"],
+        "split": doc["split"],
+    }
+
+    query_prompt = f"{data['hint']}\n{data['question']}{data['options']}" if pd.notna(data["hint"]) and data["hint"] != "nan" else f"{data['question']}{data['options']}"
+
+    if model_specific_prompt_kwargs:
+        query_prompt = f"{query_prompt}{model_specific_prompt_kwargs['post_prompt']}"
+
+    return query_prompt
+
+
+def mmupd_process_results(doc, results):
+    model_response = results[0].strip()
+    data = {
+        "gpt_eval_score": {
+            "index": doc["index"],
+            "question": doc["question"],
+            "answer": doc["answer"],
+            "prediction": model_response,
+            "hint": doc["hint"],
+            "source": doc["source"],
+            "split": doc["split"],
+            "category": doc["category"],
+            "type": doc["type"],
+            "masked_answer": doc["masked_answer"],
+        },
+        "submission": {
+            "index": doc["index"],
+            "question": doc["question"],
+            "answer": doc["answer"],
+            "prediction": model_response,
+            "hint": doc["hint"],
+            "source": doc["source"],
+            "split": doc["split"],
+            "category": doc["category"],
+            "type": doc["type"],
+            "masked_answer": doc["masked_answer"],
+        },
+    }
+    option_candidate = ["A", "B", "C", "D", "E"]
+    for c in option_candidate:
+        data["submission"][c] = doc.get(c, "nan")
+        data["gpt_eval_score"][c] = doc.get(c, "nan")
+    return data
+
+
+def mmaad_base(results, args):
+    return mmupd_results_eval(results, args, upd_type="aad", question_type="base")
+
+
+def mmaad_option(results, args):
+    return mmupd_results_eval(results, args, upd_type="aad", question_type="option")
+
+
+def mmaad_instruction(results, args):
+    return mmupd_results_eval(results, args, upd_type="aad", question_type="inst")
+
+
+def mmiasd_base(results, args):
+    return mmupd_results_eval(results, args, upd_type="iasd", question_type="base")
+
+
+def mmiasd_option(results, args):
+    return mmupd_results_eval(results, args, upd_type="iasd", question_type="option")
+
+
+def mmiasd_instruction(results, args):
+    return mmupd_results_eval(results, args, upd_type="iasd", question_type="inst")
+
+
+def mmivqd_base(results, args):
+    return mmupd_results_eval(results, args, upd_type="ivqd", question_type="base")
+
+
+def mmivqd_option(results, args):
+    return mmupd_results_eval(results, args, upd_type="ivqd", question_type="option")
+
+
+def mmivqd_instruction(results, args):
+    return mmupd_results_eval(results, args, upd_type="ivqd", question_type="inst")
+
+
+def mmupd_results_eval(results, args, upd_type, question_type):
+    print("============= MMUPD Bench Detailed Results =============")
+
+    overall_acc_standard, category_acc_standard, standard_results_df = mmupd_evaluator.eval_result(results, eval_method="openai", upd_type=upd_type, question_type=question_type, eval_type="standard")
+    overall_acc_upd, category_acc_upd, upd_results_df = mmupd_evaluator.eval_result(results, eval_method="openai", upd_type=upd_type, question_type=question_type, eval_type=upd_type)
+
+    overall_acc_dual, category_acc_dual, dual_results_df = mmupd_evaluator.calculate_dual_acc(standard_results_df, upd_results_df)
+
+    file_json = generate_submission_file(f"mmupd_{upd_type}_{question_type}_dual_results.json", args)
+
+    details_info = {
+        "overall_acc_dual": overall_acc_dual,
+        "category_acc_dual": category_acc_dual,
+        "overall_acc_standard": overall_acc_standard,
+        "category_acc_standard": category_acc_standard,
+        "overall_acc_upd": overall_acc_upd,
+        "category_acc_upd": category_acc_upd,
+    }
+
+    with open(file_json, "w") as f:
+        json.dump(details_info, f)
+
+    file_excel = generate_submission_file(f"mmupd_{upd_type}_{question_type}_dual_results_detail.xlsx", args)
+    dual_results_df.to_excel(file_excel, index=False)
+
+    file_json = generate_submission_file(f"mmupd_{upd_type}_{question_type}_dual_results_detail.json", args)
+    dual_results_df.to_json(file_json, orient="records", indent=2)  # for huggingface leaderboard submission
+
+    return overall_acc_dual * 100
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/README.md b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/README.md
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/README.md
@@ -0,0 +1 @@
+
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_default_template.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_default_template.yaml
new file mode 100644
index 00000000..3ae9260b
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/_default_template.yaml
@@ -0,0 +1,35 @@
+test_split: train
+output_type: generate_until
+doc_to_visual: !function utils.llava_doc_to_visual
+doc_to_text: !function utils.llava_doc_to_text
+doc_to_target: "gpt_answer"
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+  image_aspect_ratio: original
+  max_new_tokens: 1024
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.llava_process_results
+metric_list:
+  - metric: gpt_eval_llava_all
+    aggregation: !function utils.llava_all_aggregation
+    higher_is_better: true
+  - metric: gpt_eval_llava_conv
+    aggregation: !function utils.llava_conv_aggregation
+    higher_is_better: true
+  - metric: gpt_eval_llava_detail
+    aggregation: !function utils.llava_detail_aggregation
+    higher_is_better: true
+  - metric: gpt_eval_llava_complex
+    aggregation: !function utils.llava_complex_aggregation
+    higher_is_better: true
+metadata:
+  version: 0.0
+  gpt_eval_model_name: "gpt-4-0613"
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: ""
\ No newline at end of file
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/arabic_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/arabic_llava_in_the_wild.yaml
new file mode 100644
index 00000000..8e36630f
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/arabic_llava_in_the_wild.yaml
@@ -0,0 +1,6 @@
+dataset_path: "gagan3012/multilingual-llava-bench"
+dataset_kwargs:
+    config: arabic
+    token: True
+task: "llava_in_the_wild_arabic"
+include: _default_template.yaml
\ No newline at end of file
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/bengali_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/bengali_llava_in_the_wild.yaml
new file mode 100644
index 00000000..cabe38a8
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/bengali_llava_in_the_wild.yaml
@@ -0,0 +1,6 @@
+dataset_path: "gagan3012/multilingual-llava-bench"
+dataset_kwargs:
+    config: bengali
+    token: True
+task: "llava_in_the_wild_bengali"
+include: _default_template.yaml
\ No newline at end of file
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/chinese_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/chinese_llava_in_the_wild.yaml
new file mode 100644
index 00000000..3abda8a9
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/chinese_llava_in_the_wild.yaml
@@ -0,0 +1,6 @@
+dataset_path: "gagan3012/multilingual-llava-bench"
+dataset_kwargs:
+    config: chinese
+    token: True
+task: "llava_in_the_wild_chinese"
+include: _default_template.yaml
\ No newline at end of file
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/french_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/french_llava_in_the_wild.yaml
new file mode 100644
index 00000000..cd30f2c4
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/french_llava_in_the_wild.yaml
@@ -0,0 +1,6 @@
+dataset_path: "gagan3012/multilingual-llava-bench"
+dataset_kwargs:
+    config: french
+    token: True
+task: "llava_in_the_wild_french"
+include: _default_template.yaml
\ No newline at end of file
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/hindi_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/hindi_llava_in_the_wild.yaml
new file mode 100644
index 00000000..45dc0d91
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/hindi_llava_in_the_wild.yaml
@@ -0,0 +1,6 @@
+dataset_path: "gagan3012/multilingual-llava-bench"
+dataset_kwargs:
+    config: hindi
+    token: True
+task: "llava_in_the_wild_hindi"
+include: _default_template.yaml
\ No newline at end of file
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/japanese_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/japanese_llava_in_the_wild.yaml
new file mode 100644
index 00000000..ac4083a8
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/japanese_llava_in_the_wild.yaml
@@ -0,0 +1,6 @@
+dataset_path: "gagan3012/multilingual-llava-bench"
+dataset_kwargs:
+    config: japanese
+    token: True
+task: "llava_in_the_wild_japanese"
+include: _default_template.yaml
\ No newline at end of file
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/rule.json b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/rule.json
new file mode 100644
index 00000000..26c7f4e0
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/rule.json
@@ -0,0 +1,11 @@
+{
+    "coding": {"role": "Assistant", "prompt": "Your task is to evaluate the coding abilities of the above two assistants. They have been asked to implement a program to solve a given problem. Please review their code submissions, paying close attention to their problem-solving approach, code structure, readability, and the inclusion of helpful comments.\n\nPlease ensure that the assistants' submissions:\n\n1. Correctly implement the given problem statement.\n2. Contain accurate and efficient code.\n3. Include clear and concise comments that explain the code's logic and functionality.\n4. Adhere to proper coding standards and best practices.\n\nOnce you have carefully reviewed both submissions, provide detailed feedback on their strengths and weaknesses, along with any suggestions for improvement. You should first output a single line containing two scores on the scale of 1-10 (1: no code/no sense; 10: perfect) for Assistant 1 and 2, respectively. Then give extra comments starting from the next line."},
+    "math":  {"role": "Assistant", "prompt": "We would like to request your feedback on the mathematical proficiency of two AI assistants regarding the given user question.\nFirstly, please solve the problem independently, without referring to the answers provided by Assistant 1 and Assistant 2.\nAfterward, please examine the problem-solving process of Assistant 1 and Assistant 2 step-by-step to ensure their correctness, identifying any incorrect steps if present. Your evaluation should take into account not only the answer but also the problem-solving steps.\nFinally, please output a Python tuple containing two numerical scores for Assistant 1 and Assistant 2, ranging from 1 to 10, respectively. If applicable, explain the reasons for any variations in their scores and determine which assistant performed better."},
+    "default":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "conv":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "detail":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "complex":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "llava_bench_conv":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "llava_bench_detail":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."},
+    "llava_bench_complex":  {"role": "Assistant", "prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with a few sentences describing the image. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}
+}
\ No newline at end of file
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/russian_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/russian_llava_in_the_wild.yaml
new file mode 100644
index 00000000..31e29ed8
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/russian_llava_in_the_wild.yaml
@@ -0,0 +1,6 @@
+dataset_path: "gagan3012/multilingual-llava-bench"
+dataset_kwargs:
+    config: russian
+    token: True
+task: "llava_in_the_wild_russian"
+include: _default_template.yaml
\ No newline at end of file
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/spanish_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/spanish_llava_in_the_wild.yaml
new file mode 100644
index 00000000..ac614bde
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/spanish_llava_in_the_wild.yaml
@@ -0,0 +1,6 @@
+dataset_path: "gagan3012/multilingual-llava-bench"
+dataset_kwargs:
+  config: spanish
+  token: True
+task: "llava_in_the_wild_spanish"
+include: _default_template.yaml
\ No newline at end of file
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/urdu_llava_in_the_wild.yaml b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/urdu_llava_in_the_wild.yaml
new file mode 100644
index 00000000..c30dda11
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/urdu_llava_in_the_wild.yaml
@@ -0,0 +1,6 @@
+dataset_path: "gagan3012/multilingual-llava-bench"
+dataset_kwargs:
+    config: urdu
+    token: True
+task: "llava_in_the_wild_urdu"
+include: _default_template.yaml
\ No newline at end of file
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py
new file mode 100644
index 00000000..6666de45
--- /dev/null
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py
@@ -0,0 +1,197 @@
+import json
+import logging
+import os
+import requests
+import numpy as np
+import openai
+from openai import OpenAI
+import time
+import yaml
+from pathlib import Path
+from copy import deepcopy
+
+eval_logger = logging.getLogger("lmms-eval")
+NUM_SECONDS_TO_SLEEP = 5
+
+LLAVA_W_METRICS = ["gpt_eval_llava_conv", "gpt_eval_llava_detail", "gpt_eval_llava_complex"]
+
+rule_dict = json.load(open(os.path.join(os.path.dirname(os.path.abspath(__file__)), "rule.json"), "r"))
+
+with open(Path(__file__).parent / "_default_template.yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+
+    config = yaml.safe_load("".join(safe_data))
+
+GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"]
+
+API_TYPE = os.getenv("API_TYPE", "openai")
+
+if API_TYPE == "openai":
+    API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
+    API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
+    headers = {
+        "Authorization": f"Bearer {API_KEY}",
+        "Content-Type": "application/json",
+    }
+elif API_TYPE == "azure":
+    API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
+    API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
+    headers = {
+        "api-key": API_KEY,
+        "Content-Type": "application/json",
+    }
+
+
+def get_eval(content: str, max_tokens: int, retries: int = 5):
+    global headers
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful and precise assistant for checking the quality of the answer.",
+        },
+        {"role": "user", "content": content},
+    ]
+
+    payload = {
+        "model": GPT_EVAL_MODEL_NAME,
+        "messages": messages,
+        "temperature": 0.2,
+        "max_tokens": max_tokens,
+    }
+
+    for attempt in range(retries):
+        try:
+            response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
+            response.raise_for_status()
+            response_data = response.json()
+
+            content = response_data["choices"][0]["message"]["content"].strip()
+            if content != "":
+                return content, response_data["model"]
+            break  # If successful, break out of the loop
+
+        except Exception as e:
+            eval_logger.info(f"Attempt {attempt + 1} failed with error: {e}")
+            if attempt < retries:  # If we have retries left, sleep and then continue to next attempt
+                time.sleep(NUM_SECONDS_TO_SLEEP)
+            else:  # If this was the last attempt, log and return empty
+                eval_logger.error(f"All {retries} attempts failed. Last error message: {e}")
+                return "", ""
+    return "", ""
+
+
+def parse_score(review):
+    try:
+        score_pair = review.split("\n")[0]
+        score_pair = score_pair.replace(",", " ")
+        sp = score_pair.split(" ")
+        if len(sp) == 2:
+            return [float(sp[0]), float(sp[1])]
+        else:
+            eval_logger.debug(f"Can not split: {review}. Returning [-1, -1]")
+            return [-1, -1]
+    except Exception as e:
+        eval_logger.debug(f"Error: {e}. Returning [-1, -1]")
+        return [-1, -1]
+
+
+def llava_doc_to_visual(doc):
+    return [doc["image"].convert("RGB")]
+
+
+def llava_doc_to_text(doc, model_specific_prompt_kwargs=None):
+    if model_specific_prompt_kwargs is None:
+        model_specific_prompt_kwargs = {}
+    pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "")
+    post_prompt = model_specific_prompt_kwargs.get("post_prompt", "")
+    return f"{pre_prompt}{doc['question']}{post_prompt}"
+
+
+def llava_process_results(doc, result):
+    """
+    Args:
+        doc: a instance of the eval dataset
+        results: [pred]
+    Returns:
+        a dictionary with key: metric name (in this case coco_bleu), value: metric value
+    """
+    try:
+        question = doc.get("question", "")
+        ans1 = doc.get("gpt_answer", "")
+        ans2 = result[0] if result else ""
+        captions = doc.get("caption", [])
+        context = "\n".join(captions) if isinstance(captions, list) else captions
+        category = "llava_bench_" + doc.get("category", "")
+        rule = rule_dict.get(category, {})
+        prompt = rule.get("prompt", "")
+        role = rule.get("role", "user")
+        content = f"[Context]\n{context}\n\n" f"[Question]\n{question}\n\n" f"[{role} 1]\n{ans1}\n\n[End of {role} 1]\n\n" f"[{role} 2]\n{ans2}\n\n[End of {role} 2]\n\n" f"[System]\n{prompt}\n\n"
+
+        review, model_name = get_eval(content, 1024)
+        scores = parse_score(review)
+    except Exception as e:
+        eval_logger.error(f"Error for Question ID: {doc.get('question_id', 'Unknown')}: {e}")
+        review = "Failed to Get a Proper Review."
+        model_name = "Failed Request"
+        scores = [-1, -1]
+
+    metric = f"gpt_eval_llava_{doc.get('category', 'all')}"
+    category_review_dict = {"question": question, "ans1": ans1, "ans2": ans2, "context": context, "category": category, "review": review, "scores": scores, "eval_model": model_name, "content": content}
+
+    non_category_review_dict = deepcopy(category_review_dict)
+    non_category_review_dict["scores"] = [-999, -999]
+
+    data_dict = {}
+    for m in LLAVA_W_METRICS:
+        if m == metric:
+            data_dict[m] = category_review_dict
+        else:
+            data_dict[m] = non_category_review_dict
+    data_dict["gpt_eval_llava_all"] = category_review_dict
+
+    # return {"gpt_eval_llava_all": review_dict}
+    return data_dict
+
+
+def llava_conv_aggregation(results):
+    return llava_aggregation(results, "conv")
+
+
+def llava_complex_aggregation(results):
+    return llava_aggregation(results, "complex")
+
+
+def llava_detail_aggregation(results):
+    return llava_aggregation(results, "detail")
+
+
+def llava_all_aggregation(results):
+    return llava_aggregation(results, "all")
+
+
+def llava_aggregation(results, category):
+    try:
+        scores = []
+        for result in results:
+            if -999 in result["scores"]:
+                continue
+            scores.append(result["scores"])
+
+        stats = np.asarray(scores).mean(0).tolist()
+        stats = [round(x, 3) for x in stats]
+        # gpt4_score_percentage = stats[0] * 10
+        # model_score_percentage = stats[1] * 10
+        # eval_logger.info(f"Category: {category}")
+        # eval_logger.info(f"GPT4 Score: {gpt4_score_percentage:.1f}%")
+        # eval_logger.info(f"Model Score: {model_score_percentage:.1f}%")
+        # eval_logger.info("=========================")
+        return round(stats[1] / stats[0] * 100, 1)
+    except Exception as e:
+        eval_logger.info(f"Error in llava_aggregation: {e}, and in category: {category}")
+        return None
diff --git a/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml b/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml
index 8d74eb11..a6950129 100755
--- a/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml
+++ b/lmms_eval/tasks/ok_vqa/_default_template_vqa_yaml
@@ -13,7 +13,7 @@ metric_list:
     ignore_case: true
     ignore_punctuation: true
   - metric: submission
-    aggregation: !function utils.ok_vqa_aggreate_submissions
+    aggregation: !function utils.ok_vqa_aggregate_submissions
     higher_is_better: true
 process_results: !function utils.ok_vqa_process_results
 model_specific_prompt_kwargs:
diff --git a/lmms_eval/tasks/ok_vqa/utils.py b/lmms_eval/tasks/ok_vqa/utils.py
index 52faa11d..f1bb7742 100755
--- a/lmms_eval/tasks/ok_vqa/utils.py
+++ b/lmms_eval/tasks/ok_vqa/utils.py
@@ -61,7 +61,7 @@ def ok_vqa_doc_to_text(doc, model_specific_prompt_kwargs=None):
     return f"{pre_prompt}{question}{post_prompt}"
 
 
-def ok_vqa_aggreate_submissions(results, args):
+def ok_vqa_aggregate_submissions(results, args):
     now_date_time = datetime.datetime.now().strftime("%Y-%m%d-%H%M-%S")
     file = f"ok_vqa-test-submission-{now_date_time}.json"
     path = generate_submission_file(file, args)
diff --git a/lmms_eval/tasks/pope/pope_adv.yaml b/lmms_eval/tasks/pope/pope_adv.yaml
new file mode 100644
index 00000000..9a185723
--- /dev/null
+++ b/lmms_eval/tasks/pope/pope_adv.yaml
@@ -0,0 +1,35 @@
+dataset_path: lmms-lab/POPE
+dataset_name: Full
+dataset_kwargs:
+  token: True
+task: "pope_adv"
+test_split: adversarial 
+output_type: generate_until
+doc_to_visual: !function utils.pope_doc_to_visual
+doc_to_text: !function utils.pope_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  max_new_tokens: 128
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.pope_process_results
+metric_list:
+  - metric: pope_accuracy
+    aggregation: !function utils.pope_aggregate_accuracy
+    higher_is_better: true
+  - metric: pope_precision
+    aggregation: !function utils.pope_aggregate_precision
+    higher_is_better: true
+  - metric: pope_recall
+    aggregation: !function utils.pope_aggregate_recall
+    higher_is_better: true
+  - metric: pope_f1_score
+    aggregation: !function utils.pope_aggregate_f1_score
+    higher_is_better: true
+  - metric: pope_yes_ratio
+    aggregation: !function utils.pope_aggregate_yes_ratio
+    higher_is_better: true
+metadata:
+  - version: 0.0
\ No newline at end of file
diff --git a/lmms_eval/tasks/pope/pope_full.yaml b/lmms_eval/tasks/pope/pope_full.yaml
new file mode 100644
index 00000000..d43c4f32
--- /dev/null
+++ b/lmms_eval/tasks/pope/pope_full.yaml
@@ -0,0 +1,5 @@
+group : pope_full
+task:
+  - pope_adv
+  - pope_pop
+  - pope_random
\ No newline at end of file
diff --git a/lmms_eval/tasks/pope/pope_pop.yaml b/lmms_eval/tasks/pope/pope_pop.yaml
new file mode 100644
index 00000000..1f5cf658
--- /dev/null
+++ b/lmms_eval/tasks/pope/pope_pop.yaml
@@ -0,0 +1,35 @@
+dataset_path: lmms-lab/POPE
+dataset_name: Full
+dataset_kwargs:
+  token: True
+task: "pope_pop"
+test_split: popular 
+output_type: generate_until
+doc_to_visual: !function utils.pope_doc_to_visual
+doc_to_text: !function utils.pope_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  max_new_tokens: 128
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.pope_process_results
+metric_list:
+  - metric: pope_accuracy
+    aggregation: !function utils.pope_aggregate_accuracy
+    higher_is_better: true
+  - metric: pope_precision
+    aggregation: !function utils.pope_aggregate_precision
+    higher_is_better: true
+  - metric: pope_recall
+    aggregation: !function utils.pope_aggregate_recall
+    higher_is_better: true
+  - metric: pope_f1_score
+    aggregation: !function utils.pope_aggregate_f1_score
+    higher_is_better: true
+  - metric: pope_yes_ratio
+    aggregation: !function utils.pope_aggregate_yes_ratio
+    higher_is_better: true
+metadata:
+  - version: 0.0
\ No newline at end of file
diff --git a/lmms_eval/tasks/pope/pope_random.yaml b/lmms_eval/tasks/pope/pope_random.yaml
new file mode 100644
index 00000000..c635f2ee
--- /dev/null
+++ b/lmms_eval/tasks/pope/pope_random.yaml
@@ -0,0 +1,35 @@
+dataset_path: lmms-lab/POPE
+dataset_name: Full
+dataset_kwargs:
+  token: True
+task: "pope_random"
+test_split: random 
+output_type: generate_until
+doc_to_visual: !function utils.pope_doc_to_visual
+doc_to_text: !function utils.pope_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  max_new_tokens: 128
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.pope_process_results
+metric_list:
+  - metric: pope_accuracy
+    aggregation: !function utils.pope_aggregate_accuracy
+    higher_is_better: true
+  - metric: pope_precision
+    aggregation: !function utils.pope_aggregate_precision
+    higher_is_better: true
+  - metric: pope_recall
+    aggregation: !function utils.pope_aggregate_recall
+    higher_is_better: true
+  - metric: pope_f1_score
+    aggregation: !function utils.pope_aggregate_f1_score
+    higher_is_better: true
+  - metric: pope_yes_ratio
+    aggregation: !function utils.pope_aggregate_yes_ratio
+    higher_is_better: true
+metadata:
+  - version: 0.0
\ No newline at end of file
diff --git a/lmms_eval/tasks/qbench/abench_dev.yaml b/lmms_eval/tasks/qbench/abench_dev.yaml
new file mode 100644
index 00000000..8b44b5f7
--- /dev/null
+++ b/lmms_eval/tasks/qbench/abench_dev.yaml
@@ -0,0 +1,22 @@
+dataset_path: q-future/A-Bench-HF
+task: "abench_dev"
+test_split: dev
+output_type: generate_until
+doc_to_visual: !function utils.q_bench_doc_to_visual
+doc_to_text: !function utils.q_bench_doc_to_text
+doc_to_target: "correct_choice"
+generation_kwargs:
+  max_new_tokens: 32
+  temperature: 0
+  do_sample: False
+process_results: !function utils.a_bench_process_results
+metric_list:
+  - metric: abench_acc
+    aggregation: !function utils.a_bench_aggregate_results
+    higher_is_better: true
+
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "Answer with the option's letter from the given choices directly.\n"
+  
\ No newline at end of file
diff --git a/lmms_eval/tasks/qbench/qbench2_dev.yaml b/lmms_eval/tasks/qbench/qbench2_dev.yaml
new file mode 100644
index 00000000..7412cb25
--- /dev/null
+++ b/lmms_eval/tasks/qbench/qbench2_dev.yaml
@@ -0,0 +1,22 @@
+dataset_path: q-future/Q-Bench2-HF
+task: "qbench2_dev"
+test_split: dev
+output_type: generate_until
+doc_to_visual: !function utils.q_bench_doc_to_visual
+doc_to_text: !function utils.q_bench_doc_to_text
+doc_to_target: "correct_choice"
+generation_kwargs:
+  max_new_tokens: 32
+  temperature: 0
+  do_sample: False
+process_results: !function utils.q_bench_process_results
+metric_list:
+  - metric: qbench_acc
+    aggregation: !function utils.q_bench_aggregate_results
+    higher_is_better: true
+
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "Answer with the option's letter from the given choices directly.\n"
+  
\ No newline at end of file
diff --git a/lmms_eval/tasks/qbench/qbench_dev.yaml b/lmms_eval/tasks/qbench/qbench_dev.yaml
new file mode 100644
index 00000000..be901333
--- /dev/null
+++ b/lmms_eval/tasks/qbench/qbench_dev.yaml
@@ -0,0 +1,22 @@
+dataset_path: q-future/Q-Bench-HF
+task: "qbench_dev"
+test_split: dev
+output_type: generate_until
+doc_to_visual: !function utils.q_bench_doc_to_visual
+doc_to_text: !function utils.q_bench_doc_to_text
+doc_to_target: "correct_choice"
+generation_kwargs:
+  max_new_tokens: 32
+  temperature: 0
+  do_sample: False
+process_results: !function utils.q_bench_process_results
+metric_list:
+  - metric: qbench_acc
+    aggregation: !function utils.q_bench_aggregate_results
+    higher_is_better: true
+
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "Answer with the option's letter from the given choices directly.\n"
+  
\ No newline at end of file
diff --git a/lmms_eval/tasks/qbench/qbenchs_dev.yaml b/lmms_eval/tasks/qbench/qbenchs_dev.yaml
new file mode 100644
index 00000000..3f7fc914
--- /dev/null
+++ b/lmms_eval/tasks/qbench/qbenchs_dev.yaml
@@ -0,0 +1,5 @@
+group: qbenchs_dev
+task:
+- qbench_dev
+- qbench2_dev
+- abench_dev
diff --git a/lmms_eval/tasks/qbench/utils.py b/lmms_eval/tasks/qbench/utils.py
new file mode 100644
index 00000000..609efb00
--- /dev/null
+++ b/lmms_eval/tasks/qbench/utils.py
@@ -0,0 +1,249 @@
+import json
+import logging
+import re
+from collections import Counter, defaultdict
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+
+
+def q_bench_doc_to_text(doc, model_specific_prompt_kwargs):
+    candidates = []
+    for i in range(4):
+        candidate = doc.get(f"option{i}")
+        if candidate != "N/A":
+            candidates.append(candidate)
+
+    question = doc["question"] + "\n" + "\n".join([". ".join([chr(ord("A") + i), candidate]) for i, candidate in enumerate(candidates)])
+    pre_prompt = model_specific_prompt_kwargs["pre_prompt"]
+    post_prompt = model_specific_prompt_kwargs["post_prompt"]
+    return f"{pre_prompt}{question}\n{post_prompt}"
+
+
+def q_bench_doc_to_visual(doc):
+    if "image2" not in doc:
+        return [doc["image"].convert("RGB")]
+    else:
+        return [doc["image1"].convert("RGB"), doc["image2"].convert("RGB")]
+
+
+def get_multi_choice_info(options):
+    """
+    Given the list of options for multiple choice question
+    Return the index2ans and all_choices
+    https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/data_utils.py#L54
+    """
+
+    start_chr = "A"
+    all_choices = []
+    index2ans = {}
+    for i, option in enumerate(options):
+        index2ans[chr(ord(start_chr) + i)] = option
+        all_choices.append(chr(ord(start_chr) + i))
+
+    return index2ans, all_choices
+
+
+def parse_multi_choice_response(response, all_choices, index2ans):
+    """
+    Parse the prediction from the generated response.
+    Return the predicted index e.g., A, B, C, D.
+    https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L10
+    """
+    for char in [",", ".", "!", "?", ";", ":", "'"]:
+        response = response.strip(char)
+    response = " " + response + " "  # add space to avoid partial match
+
+    index_ans = True
+    ans_with_brack = False
+    candidates = []
+    for choice in all_choices:  # e.g., (A) (B) (C) (D)
+        if f"({choice})" in response:
+            candidates.append(choice)
+            ans_with_brack = True
+
+    if len(candidates) == 0:
+        for choice in all_choices:  # e.g., A B C D
+            if f"{choice} " in response:
+                candidates.append(choice)
+
+    if len(candidates) == 0:
+        for choice in all_choices:  # e.g., A. B. C. D.
+            if f"{choice}." in response:
+                candidates.append(choice)
+
+    # if all above doesn't get candidates, check if the content is larger than 5 tokens and try to parse the example
+    if len(candidates) == 0 and len(response.split()) > 5:
+        for index, ans in index2ans.items():
+            if ans.lower() in response.lower():
+                candidates.append(index)
+                index_ans = False  # it's content ans.
+
+    if len(candidates) == 0:  # still not get answer, randomly choose one.
+        pred_index = random.choice(all_choices)
+    elif len(candidates) > 1:
+        start_indexes = []
+        if index_ans:
+            if ans_with_brack:
+                for can in candidates:
+                    index = response.rfind(f"({can})")
+                    start_indexes.append(index)  # -1 will be ignored anyway
+                # start_indexes = [generated_response.index(f'({can})') for can in candidates]
+            else:
+                for can in candidates:
+                    index = response.rfind(f" {can} ")
+                    start_indexes.append(index)
+        else:
+            for can in candidates:
+                index = response.lower().rfind(index2ans[can].lower())
+                start_indexes.append(index)
+        # get the last one
+        pred_index = candidates[np.argmax(start_indexes)]
+    else:  # if only one candidate, use it.
+        pred_index = candidates[0]
+
+    return pred_index
+
+
+def evaluate_q_bench(samples):
+    pred_correct = 0
+    judge_dict = dict()
+    for sample in samples:
+        gold_i = sample["answer"]
+        pred_i = sample["parsed_pred"]
+        correct = eval_multi_choice(gold_i, pred_i)
+
+        if correct:
+            judge_dict[sample["id"]] = "Correct"
+            pred_correct += 1
+        else:
+            judge_dict[sample["id"]] = "Wrong"
+
+    if len(samples) == 0:
+        return {"acc": 0}
+    return judge_dict, {"acc": pred_correct / len(samples)}
+
+
+def eval_multi_choice(gold_i, pred_i):
+    correct = False
+    # only they are exactly the same, we consider it as correct
+    if isinstance(gold_i, list):
+        for answer in gold_i:
+            if answer == pred_i:
+                correct = True
+                break
+    else:  # gold_i is a string
+        if gold_i == pred_i:
+            correct = True
+    return correct
+
+
+def calculate_ins_level_acc(results):
+    """Calculate the instruction level accuracy for given Subject results
+    https://github.com/MMMU-Benchmark/MMMU/blob/51ce7f3e829c16bb44bc5445782686b4c3508794/eval/eval_utils.py#L246
+    """
+    acc = 0
+    ins_num = 0
+    for cat_results in results.values():
+        acc += cat_results["acc"] * cat_results["num_example"]
+        ins_num += cat_results["num_example"]
+    if ins_num == 0:
+        return 0
+    return acc / ins_num
+
+
+def q_bench_process_results(doc, results):
+    pred = results[0]
+    all_choices = []
+    index2ans = {}
+    for i in range(4):
+        option = doc.get(f"option{i}")
+        if option == "N/A":
+            break
+        index2ans[chr(ord("A") + i)] = option
+        all_choices.append(chr(ord("A") + i))
+
+    parsed_pred = parse_multi_choice_response(pred, all_choices, index2ans)
+    id = doc["id"]
+    qbench_acc = {"id": id, "question_concern": doc["question_concern"], "question_type": doc["question_type"], "answer": doc["correct_choice"], "parsed_pred": parsed_pred}
+    return {
+        "qbench_acc": qbench_acc,
+        "submission": {
+            id: pred,
+        },
+    }
+
+
+concern_list = ["Global Distortion", "Global Others", "Local Distortion", "Local Others"]
+question_list = ["Yes/No", "How", "What"]
+
+
+def q_bench_aggregate_results(results):
+    evaluation_result = {}
+    subset_to_eval_samples = defaultdict(list)
+    for result in results:
+        subset_to_eval_samples[concern_list[result["question_concern"]]].append(result)
+        subset_to_eval_samples[question_list[result["question_type"]]].append(result)
+    for subset, sub_eval_samples in subset_to_eval_samples.items():
+        judge_dict, metric_dict = evaluate_q_bench(sub_eval_samples)
+        metric_dict.update({"num_example": len(sub_eval_samples)})
+        evaluation_result[subset] = metric_dict
+    printable_results = {}
+
+    for cat_name, cat_results in evaluation_result.items():
+        printable_results[cat_name] = {
+            "num": int(cat_results["num_example"]),
+            "acc": round(cat_results["acc"], 5),
+        }
+    all_ins_acc = calculate_ins_level_acc(evaluation_result)
+    printable_results["Overall"] = {
+        "num": sum([cat_results["num_example"] for cat_results in evaluation_result.values()]),
+        "acc": round(all_ins_acc, 5),
+    }
+    print(printable_results)
+    return printable_results["Overall"]["acc"]
+
+
+def a_bench_process_results(doc, results):
+    pred = results[0]
+    all_choices = []
+    index2ans = {}
+    for i in range(4):
+        option = doc.get(f"option{i}")
+        if option == "N/A":
+            break
+        index2ans[chr(ord("A") + i)] = option
+        all_choices.append(chr(ord("A") + i))
+
+    parsed_pred = parse_multi_choice_response(pred, all_choices, index2ans)
+    id = doc["id"]
+    abench_acc = {"id": id, "category": doc["category"], "answer": doc["correct_choice"], "parsed_pred": parsed_pred}
+    return {
+        "abench_acc": abench_acc,
+        "submission": {
+            id: pred,
+        },
+    }
+
+
+def a_bench_aggregate_results(results):
+    evaluation_result = {}
+    subset_to_eval_samples = defaultdict(list)
+    for result in results:
+        subset_to_eval_samples[result["category"]].append(result)
+    for subset, sub_eval_samples in subset_to_eval_samples.items():
+        judge_dict, metric_dict = evaluate_q_bench(sub_eval_samples)
+        metric_dict.update({"num_example": len(sub_eval_samples)})
+        evaluation_result[subset] = metric_dict
+    printable_results = {}
+
+    for cat_name, cat_results in evaluation_result.items():
+        printable_results[cat_name] = {
+            "num": int(cat_results["num_example"]),
+            "acc": round(cat_results["acc"], 5),
+        }
+    all_ins_acc = calculate_ins_level_acc(evaluation_result)
+    printable_results["Overall"] = {
+        "num": sum([cat_results["num_example"] for cat_results in evaluation_result.values()]),
+        "acc": round(all_ins_acc, 5),
+    }
+    print(printable_results)
+    return printable_results["Overall"]["acc"]
diff --git a/lmms_eval/tasks/screenspot/README.md b/lmms_eval/tasks/screenspot/README.md
new file mode 100644
index 00000000..840b36c6
--- /dev/null
+++ b/lmms_eval/tasks/screenspot/README.md
@@ -0,0 +1,54 @@
+# SceenSpot
+
+## GUI Grounding Benchmark: ScreenSpot
+
+ScreenSpot is an evaluation benchmark for GUI grounding, comprising over 1200 instructions from iOS, Android, macOS, Windows and Web environments, along with annotated element types (Text or Icon/Widget).
+
+
+## Groups
+
+- `screenspot`: This group bundles both the original grounding task and the new instruction generation task.
+
+## Tasks
+- `screenspot_rec_test`: the original evaluation of `{img} {instruction} --> {bounding box}` called grounding or Referring Expression Completion (REC);
+- `screenspot_reg_test`: the new evaluation of `{img} {bounding box} --> {instruction}` called instruction generation or Referring Expression Generation (REG).
+
+### REC Metrics
+
+REC/Grounding requires that a model outputs a bounding box for the target element in the image. The evaluation metrics are:
+- `IoU`: Intersection over Union (IoU) between the predicted bounding box and the ground truth bounding box. 
+- `ACC@IoIU`: We use `IoU` to create `ACC@IoU` metrics at different IoU thresholds where an output with an IoU above the threshold is considered correct.
+- `CENTER ACC`: The predicted bounding box is considered correct if the center of the predicted bounding box is within the ground truth bounding box. This is what's reported in the paper.
+
+### REG Metrics
+
+REG/Generation requires that a model outputs the instruction that describes the target element in the image. Currently, this element will be highlighted in red in the image. The evaluation metrics are:
+- `CIDEr`: The CIDEr metric is used to evaluate the quality of the generated instruction. As the paper doesn't consider this task, we have selected this metric as a standard for evaluating the quality of the generated instruction. This matches with what other works like ScreenAI have done for instruction generation for RICO datasets.
+
+## Baseline Scores
+
+As a Baseline, here is how LLaVA-v1.5-7b performs on the ScreenSpot dataset:
+- `IoU`: 0.051
+- `ACC@0.1`: 0.195
+- `ACC@0.3`: 0.042
+- `ACC@0.5`: 0.006
+- `ACC@0.7`: 0.000
+- `ACC@0.9`: 0.000
+- `CENTER ACC`: 0.097
+- `CIDEr`: 0.097
+
+## References 
+
+- ArXiv: [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://arxiv.org/abs/2401.10935)
+- GitHub: [njucckevin/SeeClick](https://github.com/njucckevin/SeeClick)
+
+```bibtex
+@misc{cheng2024seeclick,
+      title={SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents}, 
+      author={Kanzhi Cheng and Qiushi Sun and Yougang Chu and Fangzhi Xu and Yantao Li and Jianbing Zhang and Zhiyong Wu},
+      year={2024},
+      eprint={2401.10935},
+      archivePrefix={arXiv},
+      primaryClass={cs.HC}
+}
+```
diff --git a/lmms_eval/tasks/screenspot/_default_template_rec_yaml b/lmms_eval/tasks/screenspot/_default_template_rec_yaml
new file mode 100644
index 00000000..7f9aad54
--- /dev/null
+++ b/lmms_eval/tasks/screenspot/_default_template_rec_yaml
@@ -0,0 +1,33 @@
+dataset_path: rootsautomation/ScreenSpot
+output_type: generate_until
+doc_to_visual: !function utils_rec.screenspot_rec_doc_to_visual
+doc_to_text: !function utils_rec.screenspot_rec_doc_to_text
+doc_to_target: "bbox"
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+process_results: !function utils_rec.screenspot_rec_process_result
+metric_list:
+  - metric: screenspot_IoU
+    aggregation : !function utils_rec.screenspot_rec_iou
+    higher_is_better : true
+  - metric: screenspot_ACC@0.1
+    aggregation : !function utils_rec.screenspot_rec_acc01
+    higher_is_better : true
+  - metric: screenspot_ACC@0.3
+    aggregation : !function utils_rec.screenspot_rec_acc03
+    higher_is_better : true
+  - metric: screenspot_ACC@0.5
+    aggregation : !function utils_rec.screenspot_rec_acc05
+    higher_is_better : true
+  - metric: screenspot_ACC@0.7
+    aggregation : !function utils_rec.screenspot_rec_acc07
+    higher_is_better : true
+  - metric: screenspot_ACC@0.9
+    aggregation : !function utils_rec.screenspot_rec_acc09
+    higher_is_better : true
+  - metric: screenspot_Center_ACC
+    aggregation : !function utils_rec.screenspot_rec_center_acc
+    higher_is_better : true
+metadata:
+  version: '0.0'
\ No newline at end of file
diff --git a/lmms_eval/tasks/screenspot/_default_template_reg_yaml b/lmms_eval/tasks/screenspot/_default_template_reg_yaml
new file mode 100644
index 00000000..0ef72057
--- /dev/null
+++ b/lmms_eval/tasks/screenspot/_default_template_reg_yaml
@@ -0,0 +1,15 @@
+dataset_path: rootsautomation/ScreenSpot
+output_type: generate_until
+doc_to_visual: !function utils.screenspot_bbox_doc_to_visual
+doc_to_text: !function utils.screenspot_doc_to_text
+doc_to_target: "instruction"
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+process_results: !function utils.screenspot_process_result
+metric_list:
+  - metric: screenspot_CIDEr
+    aggregation : !function utils.screenspot_cider
+    higher_is_better : true
+metadata:
+  version: '0.0'
\ No newline at end of file
diff --git a/lmms_eval/tasks/screenspot/_screenspot.yaml b/lmms_eval/tasks/screenspot/_screenspot.yaml
new file mode 100644
index 00000000..c31f58de
--- /dev/null
+++ b/lmms_eval/tasks/screenspot/_screenspot.yaml
@@ -0,0 +1,4 @@
+group: screenspot
+task:
+- screenspot_reg_test
+- screenspot_rec_test
\ No newline at end of file
diff --git a/lmms_eval/tasks/screenspot/screenspot_rec_test.yaml b/lmms_eval/tasks/screenspot/screenspot_rec_test.yaml
new file mode 100644
index 00000000..baccc82a
--- /dev/null
+++ b/lmms_eval/tasks/screenspot/screenspot_rec_test.yaml
@@ -0,0 +1,4 @@
+group: screenspot_rec
+task: screenspot_rec_test
+include: _default_template_rec_yaml
+test_split: test
diff --git a/lmms_eval/tasks/screenspot/screenspot_reg_test.yaml b/lmms_eval/tasks/screenspot/screenspot_reg_test.yaml
new file mode 100644
index 00000000..ab064712
--- /dev/null
+++ b/lmms_eval/tasks/screenspot/screenspot_reg_test.yaml
@@ -0,0 +1,4 @@
+group: screenspot_reg
+task: screenspot_reg_test
+include: _default_template_reg_yaml
+test_split: test
diff --git a/lmms_eval/tasks/screenspot/utils.py b/lmms_eval/tasks/screenspot/utils.py
new file mode 100644
index 00000000..d5d53df5
--- /dev/null
+++ b/lmms_eval/tasks/screenspot/utils.py
@@ -0,0 +1,126 @@
+from PIL import ImageDraw
+from pycocoevalcap.eval import COCOEvalCap, Bleu, Meteor, Rouge, Cider, Spice
+from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
+from pycocotools.coco import COCO
+
+# COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"]  # , "SPICE"]
+COCO_METRICS = ["CIDEr"]
+
+import logging
+
+eval_logger = logging.getLogger("lmms-eval")
+
+
+def screenspot_bbox_doc_to_visual(doc):
+    bbox = doc["bbox"]
+    image = doc["image"].convert("RGB")
+    draw = ImageDraw.Draw(image)
+    bbox_xy = [bbox[0], bbox[1], bbox[2], bbox[3]]
+    draw.rectangle(bbox_xy, outline="red", width=3)
+    return [image.convert("RGB")]
+
+
+def screenspot_process_result(doc, result):
+    """
+    Args:
+        doc: a instance of the eval dataset
+        results: [pred]
+    Returns:
+        a dictionary with key: metric name (in this case coco_bleu), value: metric value
+    """
+    pred = result[0] if len(result) > 0 else ""
+    ann_id = doc["file_name"]
+    data_dict = {"instruction": doc["instruction"], "pred": pred, "ann_id": ann_id, "data_type": doc["data_type"], "data_source": doc["data_source"]}
+    return {f"screenspot_{metric}": data_dict for metric in COCO_METRICS}
+
+
+def screenspot_doc_to_text(doc):
+    return f"Direct a user to interact with the highlighted region [{doc['bbox'][0]:.2f}, {doc['bbox'][1]:.2f}, {doc['bbox'][2]:.2f}, {doc['bbox'][3]:.2f}]."
+
+
+def screenspot_aggregation_result(results, metric):
+    # scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr"), (Spice(), "SPICE")]
+    scorers = [(Cider(), "CIDEr")]
+    scorers_dict = {s[1]: s for s in scorers}
+
+    stored_results = []
+    # In order to make the coco eval tools to successfully create index
+    # We need at least two dict in the dataset
+    # 'annotation' and 'images'
+    # 'annotation' exactly reproduce the original annotation
+    # 'images' however only need the image id which is contained in the file name
+    dataset = {"annotations": [], "images": []}
+    idx = 0
+    ann_id = 0
+    for result in results:
+        stored_results.append({"image_id": idx, "caption": result["pred"]})
+        # for s in result["answer"]:
+        dataset["annotations"].append({"image_id": idx, "caption": result["instruction"], "id": ann_id})
+        ann_id += 1
+
+        dataset["images"].append({"id": idx})
+        idx += 1
+
+    coco = COCO()
+    # Manually create index here
+    coco.dataset = dataset
+    coco.createIndex()
+
+    coco_result = coco.loadRes(stored_results)
+    coco_eval = COCOEvalCap(coco, coco_result)
+
+    imgIds = coco_eval.params["image_id"]
+    gts = {}
+    res = {}
+    for imgId in imgIds:
+        gts[imgId] = coco_eval.coco.imgToAnns[imgId]
+        res[imgId] = coco_eval.cocoRes.imgToAnns[imgId]
+
+    eval_logger.info("tokenization...")
+    tokenizer = PTBTokenizer()
+    gts = tokenizer.tokenize(gts)
+    res = tokenizer.tokenize(res)
+
+    eval_logger.info(f"Computing {metric} scores...")
+
+    score, scores = scorers_dict[metric][0].compute_score(gts, res)
+    # coco_eval.setEval(score, metric)
+
+    # When metric is one of the Bleu, score will be a list
+    if type(score) == list:
+        n = int(metric.split("_")[-1])
+        score = score[n - 1]
+
+    return score
+
+
+def screenspot_bleu4(results):
+    return screenspot_aggregation_result(results, "Bleu_4")
+
+
+def screenspot_bleu3(results):
+    return screenspot_aggregation_result(results, "Bleu_3")
+
+
+def screenspot_bleu2(results):
+    return screenspot_aggregation_result(results, "Bleu_2")
+
+
+def screenspot_bleu1(results):
+    return screenspot_aggregation_result(results, "Bleu_1")
+
+
+def screenspot_meteor(results):
+    return screenspot_aggregation_result(results, "METEOR")
+
+
+def screenspot_rougel(results):
+    return screenspot_aggregation_result(results, "ROUGE_L")
+
+
+def screenspot_cider(results):
+    return screenspot_aggregation_result(results, "CIDEr")
+
+
+def screenspot_spice(results):
+    return screenspot_aggregation_result(results, "SPICE")
diff --git a/lmms_eval/tasks/screenspot/utils_rec.py b/lmms_eval/tasks/screenspot/utils_rec.py
new file mode 100644
index 00000000..09539022
--- /dev/null
+++ b/lmms_eval/tasks/screenspot/utils_rec.py
@@ -0,0 +1,218 @@
+import re
+import logging
+from datasets import Dataset
+
+eval_logger = logging.getLogger("lmms-eval")
+
+REC_METRICS = ["IoU", "ACC@0.1", "ACC@0.3", "ACC@0.5", "ACC@0.7", "ACC@0.9", "Center_ACC"]
+
+
+def screenspot_rec_doc_to_visual(doc):
+    # Image is presented as is
+    image = doc["image"].convert("RGB")
+    return [image.convert("RGB")]
+
+
+def screenspot_rec_doc_to_text(doc):
+    return (
+        "Bounding box coordinates are specified in the format (top-left x, top-left y, bottom-right x, bottom-right y). All values are floating point numbers bounded between 0 and 1 with two decimal places of precision (e.g., 0.15). Please provide the bounding box coordinates of the region that corresponds to the command: "
+        + doc["instruction"]
+    )
+
+
+def parse_float_sequence_within(input_str):
+    """
+    Extract the first sequence of four floating-point numbers within square brackets from a string.
+
+    Args:
+    input_str (str): A string that may contain a sequence of four floats within square brackets.
+
+    Returns:
+    list: A list of four floats if the pattern is found, or a list of four zeros if the pattern is not found.
+    """
+    # Define the regex pattern to find the first instance of four floats within square brackets
+    pattern = r"\[\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?),\s*(-?\d+(?:\.\d+)?)\s*\]"
+
+    # Use re.search to find the first match of the pattern in the input string
+    match = re.search(pattern, input_str)
+
+    # If a match is found, convert the captured groups into a list of floats
+    if match:
+        return [float(match.group(i)) for i in range(1, 5)]
+
+    # If the input does not contain the pattern, return the null float sequence
+    return [0, 0, 0, 0]
+
+
+def screenspot_rec_process_result(doc, result):
+    """
+    Args:
+        doc: a instance of the eval dataset
+        results: [pred]
+    Returns:
+        a dictionary with key: metric name, value: metric value
+    """
+    pred = result[0] if len(result) > 0 else ""
+    pred = parse_float_sequence_within(pred)
+    ann_id = doc["file_name"]
+    data_dict = {"instruction": doc["instruction"], "pred": pred, "ann_id": ann_id, "bbox": doc["bbox"], "data_type": doc["data_type"], "data_source": doc["data_source"]}
+    return {f"screenspot_{metric}": data_dict for metric in REC_METRICS}
+
+
+def compute_iou(box1, box2):
+    """
+    Compute the Intersection over Union (IoU) of two bounding boxes.
+
+    Parameters:
+    - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max].
+    - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max].
+
+    Returns:
+    - float: IoU of box1 and box2.
+    """
+    # Determine the coordinates of the intersection rectangle
+    x_left = max(box1[0], box2[0])
+    y_top = max(box1[1], box2[1])
+    x_right = min(box1[2], box2[2])
+    y_bottom = min(box1[3], box2[3])
+
+    # Compute the area of intersection
+    intersection_area = max(0, x_right - x_left) * max(0, y_bottom - y_top)
+
+    # Compute the area of both bounding boxes
+    box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
+    box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1])
+
+    # Compute the area of the union
+    union_area = box1_area + box2_area - intersection_area
+
+    # Compute the Intersection over Union
+    iou = intersection_area / union_area
+
+    return iou
+
+
+def compute_accuracy(box1, box2, threshold=0.5):
+    """
+    Compute the accuracy of two bounding boxes based on a specified threshold.
+
+    Parameters:
+    - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max].
+    - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max].
+    - threshold (float): Threshold for the IoU to consider the prediction correct.
+
+    Returns:
+    - float: Accuracy of the prediction based on the IoU threshold.
+    """
+    iou = compute_iou(box1, box2)
+    return iou >= threshold
+
+
+def compute_center_accuracy(box1, box2):
+    """
+    Compute if the center point of box 2 is within box 1.
+
+    Parameters:
+    - box1 (list of float): Bounding box [x_min, y_min, x_max, y_max].
+    - box2 (list of float): Bounding box [x_min, y_min, x_max, y_max].
+
+    Returns:
+    - bool: True if the center point of box 2 is within box 1, False otherwise.
+    """
+    # Compute the center point of box 2
+    center_x = (box2[0] + box2[2]) / 2
+    center_y = (box2[1] + box2[3]) / 2
+
+    # Check if the center point is within box 1
+    return box1[0] <= center_x <= box1[2] and box1[1] <= center_y <= box1[3]
+
+
+def screenspot_rec_aggregation_result(results, metric):
+    """
+    Aggregate the results of the screenspot evaluation task using the specified metric.
+
+    Args:
+    - results (list of dict): List of result dictionaries.
+    - metric (str): Metric to use for aggregation.
+
+    Returns:
+    - dict: Dictionary containing the aggregated results for the specified metric.
+    """
+    scorers = {
+        "IoU": compute_iou,
+        "ACC@0.1": lambda x, y: compute_accuracy(x, y, 0.1),
+        "ACC@0.3": lambda x, y: compute_accuracy(x, y, 0.3),
+        "ACC@0.5": lambda x, y: compute_accuracy(x, y, 0.5),
+        "ACC@0.7": lambda x, y: compute_accuracy(x, y, 0.7),
+        "ACC@0.9": lambda x, y: compute_accuracy(x, y, 0.9),
+        "Center_ACC": compute_center_accuracy,
+    }
+    results_dict = {
+        metric: [],
+        metric + "-mobile_text": [],
+        metric + "-mobile_icon": [],
+        metric + "-web_text": [],
+        metric + "-web_icon": [],
+        metric + "-desktop_text": [],
+        metric + "-desktop_icon": [],
+    }
+    for result in results:
+        # Extract the ground truth and predicted bounding boxes
+        gt = result["bbox"]
+        pred = result["pred"]
+
+        # Compute the specified metric between the ground truth and predicted bounding boxes
+        score = scorers[metric](gt, pred)
+
+        results_dict[metric].append(score)
+        if result["data_type"] == "text":
+            if "ios" in result["data_source"] or "android" in result["data_source"]:
+                results_dict[metric + "-mobile_text"].append(score)
+            elif "macos" in result["data_source"] or "windows" in result["data_source"]:
+                results_dict[metric + "-desktop_text"].append(score)
+            else:
+                results_dict[metric + "-web_text"].append(score)
+        else:
+            if "ios" in result["data_source"] or "android" in result["data_source"]:
+                results_dict[metric + "-mobile_icon"].append(score)
+            elif "macos" in result["data_source"] or "windows" in result["data_source"]:
+                results_dict[metric + "-desktop_icon"].append(score)
+            else:
+                results_dict[metric + "-web_icon"].append(score)
+
+    for key in results_dict:
+        if len(results_dict[key]) == 0:
+            results_dict[key] = 0
+        else:
+            results_dict[key] = sum(results_dict[key]) / len(results_dict[key])
+
+        print(f"{key}: {results_dict[key]:0.4f}")
+    return results_dict[metric]
+
+
+def screenspot_rec_iou(results):
+    return screenspot_rec_aggregation_result(results, "IoU")
+
+
+def screenspot_rec_acc01(results):
+    return screenspot_rec_aggregation_result(results, "ACC@0.1")
+
+
+def screenspot_rec_acc03(results):
+    return screenspot_rec_aggregation_result(results, "ACC@0.3")
+
+
+def screenspot_rec_acc05(results):
+    return screenspot_rec_aggregation_result(results, "ACC@0.5")
+
+
+def screenspot_rec_acc07(results):
+    return screenspot_rec_aggregation_result(results, "ACC@0.7")
+
+
+def screenspot_rec_acc09(results):
+    return screenspot_rec_aggregation_result(results, "ACC@0.9")
+
+
+def screenspot_rec_center_acc(results):
+    return screenspot_rec_aggregation_result(results, "Center_ACC")
diff --git a/lmms_eval/tasks/sft_eval/_default_sft_eval_ocr_rec_template_yaml b/lmms_eval/tasks/sft_eval/_default_sft_eval_ocr_rec_template_yaml
deleted file mode 100644
index b00317d6..00000000
--- a/lmms_eval/tasks/sft_eval/_default_sft_eval_ocr_rec_template_yaml
+++ /dev/null
@@ -1,23 +0,0 @@
-dataset_path: lmms-lab/sft-eval
-output_type: generate_until
-doc_to_visual: !function utils.sft_eval_doc_to_visual
-doc_to_text: !function utils.sft_eval_doc_to_text
-doc_to_target: "answer"
-generation_kwargs:
-  max_new_tokens: 128
-  until:
-    - "ASSISTANT:"
-process_results: !function utils.process_result_ocr_rec
-metric_list:
-  - metric: edit_distance 
-    aggregation : !function utils.sft_eval_edit_dist_agg
-    higher_is_better : true
-  - metric: edit_acc 
-    aggregation : !function utils.sft_eval_edit_dist_acc_agg
-    higher_is_better : true
-metadata:
-  version: '0.0'
-model_specific_prompt_kwargs:
-  default:
-    pre_prompt: ""
-    post_prompt: ""
\ No newline at end of file
diff --git a/lmms_eval/tasks/sft_eval/_default_sft_eval_rest_template_yaml b/lmms_eval/tasks/sft_eval/_default_sft_eval_rest_template_yaml
deleted file mode 100644
index b658dfb4..00000000
--- a/lmms_eval/tasks/sft_eval/_default_sft_eval_rest_template_yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-dataset_path: lmms-lab/sft-eval
-output_type: generate_until
-doc_to_visual: !function utils.sft_eval_doc_to_visual
-doc_to_text: !function utils.sft_eval_doc_to_text
-doc_to_target: "answer"
-generation_kwargs:
-  max_new_tokens: 128
-  until:
-    - "ASSISTANT:"
-process_results: !function utils.process_result_rest
-metric_list:
-  - metric: fuzzy_match 
-    aggregation : !function utils.sft_eval_acc_agg
-    higher_is_better : true
-metadata:
-  version: '0.0'
-model_specific_prompt_kwargs:
-  default:
-    pre_prompt: ""
-    post_prompt: ""
\ No newline at end of file
diff --git a/lmms_eval/tasks/sft_eval/_generate_config.py b/lmms_eval/tasks/sft_eval/_generate_config.py
deleted file mode 100644
index e1dbcb83..00000000
--- a/lmms_eval/tasks/sft_eval/_generate_config.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import os
-import yaml
-
-splits = [
-    "sft-activity",
-    "sft-arts",
-    "sft-body",
-    "sft-car",
-    "sft-color",
-    "sft-commodity",
-    "sft-count",
-    "sft-daily",
-    "sft-engineer",
-    "sft-entertainment",
-    "sft-exist",
-    "sft-face",
-    "sft-food",
-    "sft-healthcare",
-    "sft-landmark",
-    "sft-logo",
-    "sft-natural",
-    "sft-ocr_qa_adv",
-    "sft-ocr_qa_chart",
-    "sft-ocr_qa_form",
-    "sft-ocr_qa_scene",
-    "sft-ocr_qa_screen",
-    "sft-ocr_rec_adv",
-    "sft-ocr_rec_doc",
-    "sft-ocr_rec_handwrite",
-    "sft-ocr_rec_markdown",
-    "sft-ocr_rec_scene",
-    "sft-ocr_rec_screen",
-    "sft-place",
-    "sft-position",
-    "sft-sport",
-    "sft-status",
-]
-dir_path = os.path.dirname(os.path.realpath(__file__))
-
-local_name2official_name = {
-    "sft-ocr_rec_scene": "sft_ocr_scene_cn_eval",
-    "sft-ocr_rec_screen": "sft_ocr_screen_cn_eval",
-    "sft-ocr_rec_handwrite": "sft_ocr_handwrite_cn_eval",
-    "sft-ocr_rec_adv": "sft_ocr_adv_cn_eval",
-    "sft-ocr_rec_doc": "sft_ocr_doc_cn_eval",
-    "sft-ocr_qa_scene": "sft_ocr_sceneQA_cn_eval",
-    "sft-ocr_qa_screen": "sft_ocr_screenQA_cn_eval",
-    "sft-ocr_qa_adv": "sft_ocr_ecommerceQA_cn_eval",
-    "sft-ocr_rec_markdown": "sft_ocr_markdown_cn_eval",
-    "sft-ocr_qa_form": "sft_ocr_formQA_cn_eval",
-    "sft-ocr_qa_chart": "sft_ocr_chartQA_cn_eval",
-    "sft-face": "sft_celeb_cn_eval",
-    "sft-body": "sft_body_cn_eval",
-    "sft-count": "sft_count_cn_eval",
-    "sft-position": "sft_position_cn_eval",
-    "#": "sft_visualprompt_cn_eval",
-    "!": "sft_grounding_cn_eval",
-    "sft-exist": "sft_exist_cn_eval",
-    "sft-color": "sft_color_cn_eval",
-    "sft-status": "sft_status_cn_eval",
-    "sft-activity": "sft_activity_cn_eval",
-    "sft-place": "sft_place_cn_eval",
-    "sft-daily": "sft_daily_cn_eval",
-    "sft-arts": "sft_arts_cn_eval",
-    "sft-natural": "sft_natural_cn_eval",
-    "sft-engineer": "sft_engineer_cn_eval",
-    "sft-healthcare": "sft_healthcare_cn_eval",
-    "sft-entertainment": "sft_entertainment_cn_eval",
-    "sft-sport": "sft_sport_cn_eval",
-    "sft-commodity": "sft_commodity_cn_eval",
-    "sft-food": "sft_food_cn_eval",
-    "sft-car": "sft_car_cn_eval",
-    "sft-landmark": "sft_landmark_cn_eval",
-}
-
-task_name = [local_name2official_name[split] if split in local_name2official_name else split for split in splits]
-splits = [split.replace("-", "_") for split in splits]
-
-if __name__ == "__main__":
-    for split, task in zip(splits, task_name):
-        yaml_dict = {"group": f"sft_eval", "task": task, "test_split": split, "dataset_name": split}
-        save_path = os.path.join(dir_path, f"{split}.yaml")
-        if "ocr" in split and "rec" in split:
-            yaml_dict["include"] = "_default_sft_eval_ocr_rec_template_yaml"
-        else:
-            yaml_dict["include"] = "_default_sft_eval_rest_template_yaml"
-        print(f"Saving to {save_path}")
-        with open(save_path, "w") as f:
-            yaml.dump(yaml_dict, f, default_flow_style=False, sort_keys=False)
-
-    group_dict = {"group": "sft_eval", "task": splits}
-
-    with open(os.path.join(dir_path, "_sft_eval.yaml"), "w") as f:
-        yaml.dump(group_dict, f, default_flow_style=False, indent=4)
diff --git a/lmms_eval/tasks/sft_eval/_sft_eval.yaml b/lmms_eval/tasks/sft_eval/_sft_eval.yaml
deleted file mode 100644
index c336a4dd..00000000
--- a/lmms_eval/tasks/sft_eval/_sft_eval.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-group: sft_eval
-task:
-- sft_activity
-- sft_arts
-- sft_body
-- sft_car
-- sft_color
-- sft_commodity
-- sft_count
-- sft_daily
-- sft_engineer
-- sft_entertainment
-- sft_exist
-- sft_face
-- sft_food
-- sft_healthcare
-- sft_landmark
-- sft_logo
-- sft_natural
-- sft_ocr_qa_adv
-- sft_ocr_qa_chart
-- sft_ocr_qa_form
-- sft_ocr_qa_scene
-- sft_ocr_qa_screen
-- sft_ocr_rec_adv
-- sft_ocr_rec_doc
-- sft_ocr_rec_handwrite
-- sft_ocr_rec_markdown
-- sft_ocr_rec_scene
-- sft_ocr_rec_screen
-- sft_place
-- sft_position
-- sft_sport
-- sft_status
diff --git a/lmms_eval/tasks/sft_eval/sft_activity.yaml b/lmms_eval/tasks/sft_eval/sft_activity.yaml
deleted file mode 100644
index d8bc91b5..00000000
--- a/lmms_eval/tasks/sft_eval/sft_activity.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_activity_cn_eval
-test_split: sft_activity
-dataset_name: sft_activity
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_arts.yaml b/lmms_eval/tasks/sft_eval/sft_arts.yaml
deleted file mode 100644
index 592b2892..00000000
--- a/lmms_eval/tasks/sft_eval/sft_arts.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_arts_cn_eval
-test_split: sft_arts
-dataset_name: sft_arts
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_body.yaml b/lmms_eval/tasks/sft_eval/sft_body.yaml
deleted file mode 100644
index 6e0edb3f..00000000
--- a/lmms_eval/tasks/sft_eval/sft_body.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_body_cn_eval
-test_split: sft_body
-dataset_name: sft_body
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_car.yaml b/lmms_eval/tasks/sft_eval/sft_car.yaml
deleted file mode 100644
index e8216934..00000000
--- a/lmms_eval/tasks/sft_eval/sft_car.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_car_cn_eval
-test_split: sft_car
-dataset_name: sft_car
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_color.yaml b/lmms_eval/tasks/sft_eval/sft_color.yaml
deleted file mode 100644
index 85f912af..00000000
--- a/lmms_eval/tasks/sft_eval/sft_color.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_color_cn_eval
-test_split: sft_color
-dataset_name: sft_color
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_commodity.yaml b/lmms_eval/tasks/sft_eval/sft_commodity.yaml
deleted file mode 100644
index 3c37c5e0..00000000
--- a/lmms_eval/tasks/sft_eval/sft_commodity.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_commodity_cn_eval
-test_split: sft_commodity
-dataset_name: sft_commodity
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_count.yaml b/lmms_eval/tasks/sft_eval/sft_count.yaml
deleted file mode 100644
index b4184448..00000000
--- a/lmms_eval/tasks/sft_eval/sft_count.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_count_cn_eval
-test_split: sft_count
-dataset_name: sft_count
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_daily.yaml b/lmms_eval/tasks/sft_eval/sft_daily.yaml
deleted file mode 100644
index 540f657e..00000000
--- a/lmms_eval/tasks/sft_eval/sft_daily.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_daily_cn_eval
-test_split: sft_daily
-dataset_name: sft_daily
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_engineer.yaml b/lmms_eval/tasks/sft_eval/sft_engineer.yaml
deleted file mode 100644
index bd2532b7..00000000
--- a/lmms_eval/tasks/sft_eval/sft_engineer.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_engineer_cn_eval
-test_split: sft_engineer
-dataset_name: sft_engineer
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_entertainment.yaml b/lmms_eval/tasks/sft_eval/sft_entertainment.yaml
deleted file mode 100644
index 04f84127..00000000
--- a/lmms_eval/tasks/sft_eval/sft_entertainment.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_entertainment_cn_eval
-test_split: sft_entertainment
-dataset_name: sft_entertainment
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_exist.yaml b/lmms_eval/tasks/sft_eval/sft_exist.yaml
deleted file mode 100644
index b3ec83f7..00000000
--- a/lmms_eval/tasks/sft_eval/sft_exist.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_exist_cn_eval
-test_split: sft_exist
-dataset_name: sft_exist
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_face.yaml b/lmms_eval/tasks/sft_eval/sft_face.yaml
deleted file mode 100644
index 5dd6962f..00000000
--- a/lmms_eval/tasks/sft_eval/sft_face.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_celeb_cn_eval
-test_split: sft_face
-dataset_name: sft_face
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_food.yaml b/lmms_eval/tasks/sft_eval/sft_food.yaml
deleted file mode 100644
index a6a3dad4..00000000
--- a/lmms_eval/tasks/sft_eval/sft_food.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_food_cn_eval
-test_split: sft_food
-dataset_name: sft_food
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_healthcare.yaml b/lmms_eval/tasks/sft_eval/sft_healthcare.yaml
deleted file mode 100644
index c63d093a..00000000
--- a/lmms_eval/tasks/sft_eval/sft_healthcare.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_healthcare_cn_eval
-test_split: sft_healthcare
-dataset_name: sft_healthcare
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_landmark.yaml b/lmms_eval/tasks/sft_eval/sft_landmark.yaml
deleted file mode 100644
index 4c4c1209..00000000
--- a/lmms_eval/tasks/sft_eval/sft_landmark.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_landmark_cn_eval
-test_split: sft_landmark
-dataset_name: sft_landmark
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_logo.yaml b/lmms_eval/tasks/sft_eval/sft_logo.yaml
deleted file mode 100644
index 35116a2d..00000000
--- a/lmms_eval/tasks/sft_eval/sft_logo.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft-logo
-test_split: sft_logo
-dataset_name: sft_logo
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_natural.yaml b/lmms_eval/tasks/sft_eval/sft_natural.yaml
deleted file mode 100644
index 78668e9f..00000000
--- a/lmms_eval/tasks/sft_eval/sft_natural.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_natural_cn_eval
-test_split: sft_natural
-dataset_name: sft_natural
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_qa_adv.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_qa_adv.yaml
deleted file mode 100644
index 0e7243ff..00000000
--- a/lmms_eval/tasks/sft_eval/sft_ocr_qa_adv.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_ocr_ecommerceQA_cn_eval
-test_split: sft_ocr_qa_adv
-dataset_name: sft_ocr_qa_adv
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_qa_chart.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_qa_chart.yaml
deleted file mode 100644
index 031eaa72..00000000
--- a/lmms_eval/tasks/sft_eval/sft_ocr_qa_chart.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_ocr_chartQA_cn_eval
-test_split: sft_ocr_qa_chart
-dataset_name: sft_ocr_qa_chart
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_qa_form.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_qa_form.yaml
deleted file mode 100644
index 18e059e8..00000000
--- a/lmms_eval/tasks/sft_eval/sft_ocr_qa_form.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_ocr_formQA_cn_eval
-test_split: sft_ocr_qa_form
-dataset_name: sft_ocr_qa_form
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_qa_scene.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_qa_scene.yaml
deleted file mode 100644
index 0af342d9..00000000
--- a/lmms_eval/tasks/sft_eval/sft_ocr_qa_scene.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_ocr_sceneQA_cn_eval
-test_split: sft_ocr_qa_scene
-dataset_name: sft_ocr_qa_scene
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_qa_screen.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_qa_screen.yaml
deleted file mode 100644
index 586194a9..00000000
--- a/lmms_eval/tasks/sft_eval/sft_ocr_qa_screen.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_ocr_screenQA_cn_eval
-test_split: sft_ocr_qa_screen
-dataset_name: sft_ocr_qa_screen
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_adv.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_adv.yaml
deleted file mode 100644
index 69e116f8..00000000
--- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_adv.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_ocr_adv_cn_eval
-test_split: sft_ocr_rec_adv
-dataset_name: sft_ocr_rec_adv
-include: _default_sft_eval_ocr_rec_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_doc.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_doc.yaml
deleted file mode 100644
index 0d498c46..00000000
--- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_doc.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_ocr_doc_cn_eval
-test_split: sft_ocr_rec_doc
-dataset_name: sft_ocr_rec_doc
-include: _default_sft_eval_ocr_rec_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_handwrite.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_handwrite.yaml
deleted file mode 100644
index 5eb39256..00000000
--- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_handwrite.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_ocr_handwrite_cn_eval
-test_split: sft_ocr_rec_handwrite
-dataset_name: sft_ocr_rec_handwrite
-include: _default_sft_eval_ocr_rec_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_markdown.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_markdown.yaml
deleted file mode 100644
index 7fb34090..00000000
--- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_markdown.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_ocr_markdown_cn_eval
-test_split: sft_ocr_rec_markdown
-dataset_name: sft_ocr_rec_markdown
-include: _default_sft_eval_ocr_rec_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_scene.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_scene.yaml
deleted file mode 100644
index 8ef84382..00000000
--- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_scene.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_ocr_scene_cn_eval
-test_split: sft_ocr_rec_scene
-dataset_name: sft_ocr_rec_scene
-include: _default_sft_eval_ocr_rec_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_ocr_rec_screen.yaml b/lmms_eval/tasks/sft_eval/sft_ocr_rec_screen.yaml
deleted file mode 100644
index eba50bdb..00000000
--- a/lmms_eval/tasks/sft_eval/sft_ocr_rec_screen.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_ocr_screen_cn_eval
-test_split: sft_ocr_rec_screen
-dataset_name: sft_ocr_rec_screen
-include: _default_sft_eval_ocr_rec_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_place.yaml b/lmms_eval/tasks/sft_eval/sft_place.yaml
deleted file mode 100644
index 1bc615b4..00000000
--- a/lmms_eval/tasks/sft_eval/sft_place.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_place_cn_eval
-test_split: sft_place
-dataset_name: sft_place
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_position.yaml b/lmms_eval/tasks/sft_eval/sft_position.yaml
deleted file mode 100644
index ef489818..00000000
--- a/lmms_eval/tasks/sft_eval/sft_position.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_position_cn_eval
-test_split: sft_position
-dataset_name: sft_position
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_sport.yaml b/lmms_eval/tasks/sft_eval/sft_sport.yaml
deleted file mode 100644
index 94a6140b..00000000
--- a/lmms_eval/tasks/sft_eval/sft_sport.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_sport_cn_eval
-test_split: sft_sport
-dataset_name: sft_sport
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/sft_status.yaml b/lmms_eval/tasks/sft_eval/sft_status.yaml
deleted file mode 100644
index 9f28ba51..00000000
--- a/lmms_eval/tasks/sft_eval/sft_status.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-group: sft_eval
-task: sft_status_cn_eval
-test_split: sft_status
-dataset_name: sft_status
-include: _default_sft_eval_rest_template_yaml
diff --git a/lmms_eval/tasks/sft_eval/utils.py b/lmms_eval/tasks/sft_eval/utils.py
deleted file mode 100644
index 8ed2ef0c..00000000
--- a/lmms_eval/tasks/sft_eval/utils.py
+++ /dev/null
@@ -1,109 +0,0 @@
-from rapidfuzz.distance import Levenshtein
-
-
-def _normalize_text(text):
-    import string
-
-    text = "".join(filter(lambda x: x in (string.digits + string.ascii_letters), text))
-
-
-def process_result_ocr_rec(doc, result):
-    return {"edit_distance": {"gts": [gt.replace("\n", "") for gt in doc["answer"]], "predictions": result}, "edit_acc": {"gts": [gt.replace("\n", "") for gt in doc["answer"]], "predictions": result}}
-
-
-def process_result_rest(doc, result):
-    return {"fuzzy_match": {"gts": [gt.replace("\n", "") for gt in doc["answer"]], "predictions": result, "question_id": doc["question_id"]}}
-
-
-def run_editdistance(gts, predictions, ignore_space=True, is_filter=False):
-    eps = 1e-6
-    correct_num = 0
-    all_num = 0
-    norm_edit_dis = 0.0
-    edit_norm_score_list = list()
-    for idx in range(len(predictions)):
-        target, pred = gts[idx][0], predictions[idx]
-        if ignore_space:
-            pred = pred.replace(" ", "")
-            target = target.replace(" ", "")
-        if is_filter:
-            pred = _normalize_text(pred)
-            target = _normalize_text(target)
-
-        ned = Levenshtein.normalized_distance(pred, target)
-        norm_edit_dis += ned
-        edit_norm_score_list.append(1 - ned)
-        if pred == target:
-            correct_num += 1
-        all_num += 1
-    # import pdb; pdb.set_trace()
-    metric = {"acc": correct_num / (all_num + eps), "norm_edit_dis": 1 - norm_edit_dis / (all_num + eps)}
-    return metric, edit_norm_score_list
-
-
-def fuzzy_match_multi_answers(results, gt_dict):
-    acc = []
-    for result in results:
-        question_id = result["question_id"]
-        try:
-            gt_ans = gt_dict[question_id]
-        except:
-            import pdb
-
-            pdb.set_trace()
-        pred = result["text"]
-        for gt in gt_ans:
-            vqa_acc = 1
-            if not (
-                (gt == "是" and gt in pred and "不是" not in pred)
-                or (gt == "对" and gt in pred and "不对" not in pred)
-                or (gt == "相同" and gt in pred and "不相同" not in pred)
-                or (gt == "有" and gt in pred and "没有" not in pred)
-                or (gt == "在" and gt in pred and "不在" not in pred)
-                or (gt == "一样" and gt in pred and "不一样" not in pred)
-                or (gt not in ["是", "在", "对", "有", "一样", "相同"] and gt.lower() in pred.lower())
-            ):
-                vqa_acc = 0
-            if vqa_acc == 1:
-                break
-        acc.append(vqa_acc)
-    accuracy = sum(acc) / len(acc) * 100
-    return {"Acc": accuracy}
-
-
-def sft_eval_doc_to_visual(doc):
-    return [doc["image"].convert("RGB")]
-
-
-def sft_eval_doc_to_text(doc):
-    return doc["question"]
-
-
-def sft_eval_doc_to_text(doc, model_specific_prompt_kwargs=None):
-    if model_specific_prompt_kwargs is None:
-        model_specific_prompt_kwargs = {}
-    pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "")
-    post_prompt = model_specific_prompt_kwargs.get("post_prompt", "")
-    question = f"{pre_prompt}{doc['question']}{post_prompt}"
-    return question
-
-
-def sft_eval_edit_dist_acc_agg(results):
-    predictions = [result["predictions"][0] for result in results]
-    gts = [result["gts"] for result in results]
-    acc, _ = run_editdistance(gts, predictions)
-    return acc["acc"]
-
-
-def sft_eval_edit_dist_agg(results):
-    predictions = [result["predictions"][0] for result in results]
-    gts = [result["gts"] for result in results]
-    acc, _ = run_editdistance(gts, predictions)
-    return acc["norm_edit_dis"]
-
-
-def sft_eval_acc_agg(results):
-    gts_dict = {result["question_id"]: result["gts"] for result in results}
-    predictions = [{"question_id": result["question_id"], "text": result["predictions"][0]} for result in results]
-    acc = fuzzy_match_multi_answers(predictions, gts_dict)
-    return acc["Acc"]
diff --git a/lmms_eval/tasks/textvqa/textvqa_test.yaml b/lmms_eval/tasks/textvqa/textvqa_test.yaml
index 15b02c4f..d22ef571 100755
--- a/lmms_eval/tasks/textvqa/textvqa_test.yaml
+++ b/lmms_eval/tasks/textvqa/textvqa_test.yaml
@@ -2,6 +2,6 @@ task: textvqa_test
 test_split: test
 metric_list:
   - metric: submission
-    aggregation: !function utils.textvqa_aggreate_submissions
+    aggregation: !function utils.textvqa_aggregate_submissions
     higher_is_better: true
 include: _default_template_textvqa_yaml
diff --git a/lmms_eval/tasks/textvqa/textvqa_val.yaml b/lmms_eval/tasks/textvqa/textvqa_val.yaml
index e23e9b2e..8ce39a49 100755
--- a/lmms_eval/tasks/textvqa/textvqa_val.yaml
+++ b/lmms_eval/tasks/textvqa/textvqa_val.yaml
@@ -7,6 +7,6 @@ metric_list:
     ignore_case: true
     ignore_punctuation: true
   - metric: submission
-    aggregation: !function utils.textvqa_aggreate_submissions
+    aggregation: !function utils.textvqa_aggregate_submissions
     higher_is_better: true
 include: _default_template_textvqa_yaml
diff --git a/lmms_eval/tasks/textvqa/utils.py b/lmms_eval/tasks/textvqa/utils.py
index ea3b503b..71a4ccaa 100755
--- a/lmms_eval/tasks/textvqa/utils.py
+++ b/lmms_eval/tasks/textvqa/utils.py
@@ -59,7 +59,7 @@ def textvqa_doc_to_text(doc, model_specific_prompt_kwargs=None):
     return f"{pre_prompt}{doc['question'].capitalize()}{ocr_ref}{post_prompt}"
 
 
-def textvqa_aggreate_submissions(results, args):
+def textvqa_aggregate_submissions(results, args):
     now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
     path = generate_submission_file(f"textvqa_submission_{now_date_time}.json", args)
     with open(path, "w") as f:
diff --git a/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml b/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml
new file mode 100644
index 00000000..37ab5e74
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/_default_template_vcr_yaml
@@ -0,0 +1,17 @@
+
+dataset_kwargs:
+  token: True
+output_type: generate_until
+doc_to_visual: !function utils.vcr_doc_to_visual
+doc_to_text: !function utils.vcr_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  max_new_tokens: 120
+  temperature: 0
+  top_p: 0
+  num_beams: 1
+  do_sample: false
+# The return value of process_results will be used by metrics
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metadata:
+  - version: 0.0.1
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py
new file mode 100644
index 00000000..95bdae1a
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/utils.py
@@ -0,0 +1,300 @@
+import datetime
+import json
+import logging
+import os
+from difflib import SequenceMatcher as SM
+from functools import partial
+
+import evaluate
+import numpy as np
+import spacy
+from nltk.util import ngrams
+from spacy.cli import download
+
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+
+# Download the English and Chinese models
+try:
+    nlp_en = spacy.load("en_core_web_sm")
+except Exception as e:
+    download("en_core_web_sm")
+    nlp_en = spacy.load("en_core_web_sm")
+
+try:
+    nlp_zh = spacy.load("zh_core_web_sm")
+except Exception as e:
+    download("zh_core_web_sm")
+    nlp_zh = spacy.load("zh_core_web_sm")
+
+nlp = {"en": nlp_en, "zh": nlp_zh}
+rouge = evaluate.load("rouge")
+
+eval_logger = logging.getLogger("lmms-eval")
+dir_name = os.path.dirname(os.path.abspath(__file__))
+
+aggregate_results_template = {
+    "max_sim_val": 0,
+    "precision": 0,
+    "recall": 0,
+    "f1": 0,
+    "jaccard": 0,
+    "rouge1": 0,
+}
+
+
+def fast_filter(answer_text):
+    if "I can't" in answer_text:
+        return True
+    elif "I cannot" in answer_text:
+        return True
+    elif "sorry" in answer_text.lower():
+        return True
+    if "无法" in answer_text:
+        return True
+    elif "抱歉" in answer_text:
+        return True
+    else:
+        return False
+
+
+def vcr_doc_to_visual(doc):
+    return [doc["stacked_image"].convert("RGB")]
+
+
+def vcr_doc_to_text(doc, model_specific_prompt_kwargs=None):
+    if "pre_prompt" in model_specific_prompt_kwargs:
+        pre_prompt = model_specific_prompt_kwargs["pre_prompt"]
+    if "post_prompt" in model_specific_prompt_kwargs:
+        post_prompt = model_specific_prompt_kwargs["post_prompt"]
+    return f"{pre_prompt}{post_prompt}"
+
+
+def tokenize(text, language):
+    """
+    Tokenize the text and return the tokens.
+
+    Parameters:
+    text (str): The text to tokenize.
+    language (str): The language of the text.
+
+    Returns:
+    list: The list of tokens.
+    """
+    assert language in ["en", "zh"]
+    nlp_lang = nlp[language]
+    processed_text = nlp_lang(text)
+    return [token.text for token in processed_text]
+
+
+def vcr_process_results_single(crossed_text, result, language):
+    """
+    Args:
+        doc: an instance of the eval dataset
+        results: [pred]
+    Returns:
+        a dictionary with key: metric name (in this case vcr score), value: metric value
+    """
+
+    assert language in ["en", "zh"], f"Language {language} is not supported."
+
+    if fast_filter(result):
+        return {
+            "crossed_text": crossed_text,
+            "max_sim_val": 0,
+            "max_sim_string": "",
+            "precision": 0,
+            "recall": 0,
+            "f1": 0,
+            "jaccard": 0,
+            "rouge1": 0,
+            "exact_match": 0,
+        }
+    tokens_result = tokenize(result, language)
+    tokens_crossed_text = tokenize(crossed_text, language)
+
+    splitter = " " if language == "en" else ""
+    ngrams_ = ngrams(tokens_result, len(tokens_crossed_text))
+    max_sim_val = 0
+    max_sim_string = ""
+    max_sim_ngram = []
+    tokens_crossed_text_set = set(tokens_crossed_text)
+    ngrams_hasjoint = [ngram for ngram in ngrams_ if not set(ngram).isdisjoint(tokens_crossed_text_set)]
+
+    for ngram in ngrams_hasjoint:
+        result_ngram = splitter.join(ngram)
+        similarity = SM(None, result_ngram, crossed_text).ratio()
+        if similarity > max_sim_val:
+            max_sim_val = similarity
+            max_sim_string = result_ngram
+            max_sim_ngram = ngram
+
+    # Evaluate
+    if len(max_sim_ngram) == 0:
+        return {
+            "crossed_text": crossed_text,
+            "max_sim_val": 0,
+            "max_sim_string": "",
+            "precision": 0,
+            "recall": 0,
+            "f1": 0,
+            "jaccard": 0,
+            "rouge1": 0,
+            "exact_match": 0,
+        }
+    pred_set = set(max_sim_ngram)
+    ref_set = set(tokens_crossed_text)
+    correct_tokens = pred_set.intersection(ref_set)
+    len_correct_tokens = len(correct_tokens)
+
+    precision = len_correct_tokens / len(pred_set)
+    recall = len_correct_tokens / len(ref_set)
+    if (precision + recall) == 0:
+        f1 = 0
+    else:
+        f1 = 2 * precision * recall / (precision + recall)
+    union = pred_set.union(ref_set)
+    jaccard = len_correct_tokens / len(union) if len(union) > 0 else 0
+    rouge_1 = rouge.compute(
+        predictions=[max_sim_string],
+        references=[crossed_text],
+        tokenizer=partial(tokenize, language=language),
+        rouge_types=["rouge1"],
+    )["rouge1"]
+    exact_match = float(list(max_sim_ngram) == list(tokens_crossed_text))
+    out = {
+        "crossed_text": crossed_text,
+        "max_sim_string": max_sim_string,
+        "max_sim_val": max_sim_val,
+        "precision": precision,
+        "recall": recall,
+        "f1": f1,
+        "jaccard": jaccard,
+        "rouge1": rouge_1,
+        "exact_match": exact_match,
+    }
+    return out
+
+
+def vcr_en_process_results(doc, results):
+    """
+    Args:
+        doc: an instance of the eval dataset
+        results: [pred], with length = 1
+    Returns:
+        a dictionary with key: metric name (in this case vcr score), value: metric value
+    """
+    output = {
+        "max_sim_val": [],
+        "precision": [],
+        "recall": [],
+        "f1": [],
+        "jaccard": [],
+        "rouge1": [],
+        "exact_match": [],
+    }
+    crossed_text = doc["crossed_text"]
+    for i in range(len(crossed_text)):
+        tmp = vcr_process_results_single(crossed_text[i], results[0], "en")
+        for k in output.keys():
+            output[k].append(
+                {
+                    "score": tmp[k],
+                    "pred_ngram": tmp["max_sim_string"],
+                    "gt_ngram": crossed_text[i],
+                    "caption": doc["caption"],
+                }
+            )
+    return output
+
+
+def vcr_zh_process_results(doc, results):
+    """
+    Args:
+        doc: an instance of the eval dataset
+        results: [pred], with length = 1
+    Returns:
+        a dictionary with key: metric name (in this case vcr score), value: metric value and other info
+    """
+    output = {
+        "max_sim_val": [],
+        "precision": [],
+        "recall": [],
+        "f1": [],
+        "jaccard": [],
+        "rouge1": [],
+        "exact_match": [],
+    }
+    crossed_text = doc["crossed_text"]
+    for i in range(len(crossed_text)):
+        tmp = vcr_process_results_single(crossed_text[i], results[0], "zh")
+        for k in output.keys():
+            output[k].append(
+                {
+                    "question_id": doc["question_id"],
+                    "score": tmp[k],
+                    "pred_ngram": tmp["max_sim_string"],
+                    "gt_ngram": crossed_text[i],
+                    "caption": doc["caption"],
+                }
+            )
+    return output
+
+
+def bootstrap_std(data, n_bootstrap=1000, ci=0.95):
+    """
+    Args:
+        data: a list of values
+        n_bootstrap: number of bootstrap samples
+        ci: confidence interval
+    Returns:
+        a tuple of mean, lower bound, upper bound
+    """
+    n = len(data)
+    means = []
+    for _ in range(n_bootstrap):
+        sample = np.random.choice(data, n, replace=True)
+        means.append(np.mean(sample))
+    means = np.array(means)
+    lower_bound = np.percentile(means, (1 - ci) / 2 * 100)
+    upper_bound = np.percentile(means, (1 + ci) / 2 * 100)
+    std = np.std(means)
+    return std, lower_bound, upper_bound
+
+
+def vcr_aggregate_results(results, args, metric="exact_match"):
+    """
+    Args:
+        results: List[List[Dict]], list of results returned by process_results
+    Returns:
+        A float value representing the final score of jaccard index or exact match
+    """
+    scores = []
+    output_dict_detail_result = {}
+    for i in range(len(results)):
+        for blank_id in range(len(results[i])):
+            scores.append(results[i][blank_id]["score"])
+        output_dict_detail_result[str(i)] = results[i]
+    mean_score = np.mean(scores)
+    std, lb, ub = bootstrap_std(scores, n_bootstrap=1000, ci=0.95)
+    output_dict = {
+        "mean_score": mean_score,
+        "std_score": std,
+        "percentile_2.5": lb,
+        "percentie_97.5": ub,
+        "detailed_results": output_dict_detail_result,
+    }
+    now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+    path = generate_submission_file(f"vcr_submission_{metric}_{now_date_time}.json", args)
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(output_dict, f, indent=4, ensure_ascii=False)
+    # print(f"Submission file saved to {path}")
+    eval_logger.info(f"Submission file saved to {path}")
+    return mean_score
+
+
+def vcr_aggregate_exact_match(results, args):
+    return vcr_aggregate_results(results, args, metric="exact_match")
+
+
+def vcr_aggregate_jaccard(results, args):
+    return vcr_aggregate_results(results, args, metric="jaccard")
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml
new file mode 100644
index 00000000..9ce88299
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-en-easy-test
+task: "vcr_wiki_en_easy"
+test_split: test
+process_results: !function utils.vcr_en_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations."
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml
new file mode 100644
index 00000000..37f0130b
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_100.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-en-easy-test-100
+task: "vcr_wiki_en_easy_100"
+test_split: test
+process_results: !function utils.vcr_en_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations."
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml
new file mode 100644
index 00000000..0ccc1ed0
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_easy_500.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-en-easy-test-500
+task: "vcr_wiki_en_easy_500"
+test_split: test
+process_results: !function utils.vcr_en_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations."
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml
new file mode 100644
index 00000000..8c001e4e
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-en-hard-test
+task: "vcr_wiki_en_hard"
+test_split: test
+process_results: !function utils.vcr_en_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations."
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml
new file mode 100644
index 00000000..16c79b15
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_100.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-en-hard-test-100
+task: "vcr_wiki_en_hard_100"
+test_split: test
+process_results: !function utils.vcr_en_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations."
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml
new file mode 100644
index 00000000..65616894
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_en_hard_500.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-en-hard-test-500
+task: "vcr_wiki_en_hard_500"
+test_split: test
+process_results: !function utils.vcr_en_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "What is the covered texts in the image? Please restore the covered texts without outputting the explanations."
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml
new file mode 100644
index 00000000..a07cf230
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-zh-easy-test
+task: "vcr_wiki_zh_easy"
+test_split: test
+process_results: !function utils.vcr_zh_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。"
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml
new file mode 100644
index 00000000..fb41b668
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_100.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-zh-easy-test-100
+task: "vcr_wiki_zh_easy_100"
+test_split: test
+process_results: !function utils.vcr_zh_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。"
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml
new file mode 100644
index 00000000..e3654aed
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_easy_500.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-zh-easy-test-500
+task: "vcr_wiki_zh_easy_500"
+test_split: test
+process_results: !function utils.vcr_zh_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。"
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml
new file mode 100644
index 00000000..0401b3bd
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-zh-hard-test
+task: "vcr_wiki_zh_hard"
+test_split: test
+process_results: !function utils.vcr_zh_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。"
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml
new file mode 100644
index 00000000..f6512e29
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_100.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-zh-hard-test-100
+task: "vcr_wiki_zh_hard_100"
+test_split: test
+process_results: !function utils.vcr_zh_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。"
\ No newline at end of file
diff --git a/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml
new file mode 100644
index 00000000..a36bb328
--- /dev/null
+++ b/lmms_eval/tasks/vcr_wiki/vcr_wiki_zh_hard_500.yaml
@@ -0,0 +1,16 @@
+"include": "_default_template_vcr_yaml"
+dataset_path: vcr-org/VCR-wiki-zh-hard-test-500
+task: "vcr_wiki_zh_hard_500"
+test_split: test
+process_results: !function utils.vcr_zh_process_results
+metric_list:
+  - metric: jaccard
+    aggregation: !function utils.vcr_aggregate_jaccard
+    higher_is_better: true
+  - metric: exact_match
+    aggregation: !function utils.vcr_aggregate_exact_match
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: "图像中被覆盖的文本是什么？请在不输出解释的情况下还原被覆盖的文本。"
\ No newline at end of file
diff --git a/lmms_eval/tasks/vizwiz_vqa/utils.py b/lmms_eval/tasks/vizwiz_vqa/utils.py
index 63afb4e4..9ff8d3d8 100755
--- a/lmms_eval/tasks/vizwiz_vqa/utils.py
+++ b/lmms_eval/tasks/vizwiz_vqa/utils.py
@@ -61,7 +61,7 @@ def vizwiz_vqa_doc_to_text(doc, model_specific_prompt_kwargs=None):
     return text
 
 
-def vizwiz_vqa_aggreate_submissions(results, args):
+def vizwiz_vqa_aggregate_submissions(results, args):
     now_date_time = datetime.datetime.now().strftime("%Y-%m%d-%H%M-%S")
     submission_file_name = f"vizwiz_vqa-test-submission-{now_date_time}.json"
     path = generate_submission_file(submission_file_name, args)
diff --git a/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_test.yaml b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_test.yaml
index dec140f6..82fafcaa 100755
--- a/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_test.yaml
+++ b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_test.yaml
@@ -10,5 +10,5 @@ metric_list:
   #   ignore_case: true
   #   ignore_punctuation: true
   - metric: submission
-    aggregation: !function utils.vizwiz_vqa_aggreate_submissions
+    aggregation: !function utils.vizwiz_vqa_aggregate_submissions
     higher_is_better: true
diff --git a/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val.yaml b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val.yaml
index ac8ecc98..3343bbdc 100755
--- a/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val.yaml
+++ b/lmms_eval/tasks/vizwiz_vqa/vizwiz_vqa_val.yaml
@@ -9,5 +9,5 @@ metric_list:
     ignore_case: true
     ignore_punctuation: true
   # - metric: submission
-  #   aggregation: !function utils.vizwiz_vqa_aggreate_submissions
+  #   aggregation: !function utils.vizwiz_vqa_aggregate_submissions
   #   higher_is_better: true
\ No newline at end of file
diff --git a/lmms_eval/tasks/vqav2/utils.py b/lmms_eval/tasks/vqav2/utils.py
index 1a3e9082..0951712a 100755
--- a/lmms_eval/tasks/vqav2/utils.py
+++ b/lmms_eval/tasks/vqav2/utils.py
@@ -80,7 +80,7 @@ def vqav2_doc_to_text(doc, model_specific_prompt_kwargs=None):
     return f"{pre_prompt}{doc['question']}{post_prompt}"
 
 
-def vqav2_aggreate_submissions(results, args):
+def vqav2_aggregate_submissions(results, args):
     now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
     submission_file_name = f"vqav2-test-submission-{now_date_time}.json"
     path = file_utils.generate_submission_file(submission_file_name, args)
diff --git a/lmms_eval/tasks/vqav2/vqav2_test.yaml b/lmms_eval/tasks/vqav2/vqav2_test.yaml
index 94c69209..4c038604 100755
--- a/lmms_eval/tasks/vqav2/vqav2_test.yaml
+++ b/lmms_eval/tasks/vqav2/vqav2_test.yaml
@@ -3,6 +3,6 @@ include: _default_template_vqav2_yaml
 test_split: test
 metric_list:
   - metric: submission
-    aggregation: !function utils.vqav2_aggreate_submissions
+    aggregation: !function utils.vqav2_aggregate_submissions
     higher_is_better: true
 process_results: !function utils.vqav2_process_results_test
diff --git a/lmms_eval/tasks/websrc/README.md b/lmms_eval/tasks/websrc/README.md
new file mode 100644
index 00000000..a631e6d8
--- /dev/null
+++ b/lmms_eval/tasks/websrc/README.md
@@ -0,0 +1,51 @@
+# WebSRC
+
+## Paper 
+
+Title: WebSRC: A Dataset for Web-Based Structural Reading Comprehension
+
+Abstract: https://arxiv.org/abs/2101.09465
+
+Homepage: https://x-lance.github.io/WebSRC/#
+
+WebSRC is a dataset for web-based structural reading comprehension.
+Its full train/dev/test split contains over 400k questions across 6.4k webpages. 
+This version of the dataset does not contain OCR or original HTML, it simply treats WebSRC as a image-and-text-based multimodal Q&A benchmark on webpage screenshots.
+
+## Citation
+
+```bibtex
+@inproceedings{chen2021websrc,
+  title={WebSRC: A Dataset for Web-Based Structural Reading Comprehension},
+  author={Chen, Xingyu and Zhao, Zihan and Chen, Lu and Ji, Jiabao and Zhang, Danyang and Luo, Ao and Xiong, Yuxuan and Yu, Kai},
+  booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
+  pages={4173--4185},
+  year={2021}
+}
+```
+
+## Groups & Tasks
+
+### Groups 
+
+- `websrc`: Evaluates `websrc-val` and generates a submission file for `websrc-test`.
+
+### Tasks
+
+- `websrc-val`: Given a question and a web page, predict the answer.
+- `websrc-test`: Given a question and a web page, predict the answer. Ground truth is not provided for this task.
+
+## Metrics
+
+This task uses SQUAD-style evaluation metrics, of which F1 score over tokens is used. 
+The orignal paper also uses Exact Match (EM) score, but this is not implemented here as that metric is more conducive for Encoder-only extraction models.
+
+### F1 Score
+
+F1 Score is the harmonic mean of precision and recall.
+We calculate precision and recall at the token level, then compute the F1 score as normal using these values.
+
+### Test Submission
+
+When evaluaing on the test split, a prediction JSON will be compiled instead of metrics computed. 
+Instructions for submission are available on the [WebSRC homepage](https://x-lance.github.io/WebSRC/#) and in their [Original GitHub Repo](https://github.com/X-LANCE/WebSRC-Baseline#obtain-test-result).
\ No newline at end of file
diff --git a/lmms_eval/tasks/websrc/utils.py b/lmms_eval/tasks/websrc/utils.py
new file mode 100644
index 00000000..5cc09e6d
--- /dev/null
+++ b/lmms_eval/tasks/websrc/utils.py
@@ -0,0 +1,159 @@
+from collections import defaultdict
+import re
+import ast
+import base64
+import io
+import random
+import numpy as np
+import os
+import json
+import logging
+from PIL import Image
+
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+
+lmms_logger = logging.getLogger("lmms-eval")
+
+OPEN_ENDED_PROMPT = "Answer the question using a single word or phrase."
+
+
+def construct_prompt(doc):
+    question = doc["question"]
+    question = f"{OPEN_ENDED_PROMPT}\n{question}"
+    return question
+
+
+def websrc_doc_to_text(doc):
+    question = construct_prompt(doc)
+    return question
+
+
+def websrc_doc_to_visual(doc):
+    img_bs64 = doc["image"]
+    img = Image.open(io.BytesIO(base64.b64decode(img_bs64)))
+    del doc["image"]
+    return [img]
+
+
+def websrc_process_results(doc, results):
+    pred = results[0]
+    parsed_pred = pred
+    id = doc["page_id"]
+    websrc_ans = {"id": id, "domain": doc["domain"], "parsed_pred": parsed_pred}
+    if "answer" in doc:
+        websrc_ans["answer"] = doc["answer"]
+
+    if "id" in doc:
+        websrc_ans["question_id"] = doc["id"]
+
+    return {
+        "websrc_squad_f1": websrc_ans,
+        "submission": {
+            websrc_ans["question_id"]: pred,
+        }
+        if "question_id" in websrc_ans
+        else None,
+    }
+
+
+def websrc_test_aggregate_results_for_submission(results, args):
+    path = generate_submission_file("websrc_test_for_submission.json", args)
+    with open(path, "w") as f:
+        out = {}
+        for result in results:
+            out.update(result)
+        json.dump(out, f, indent=4)
+    lmms_logger.info(f"Results saved to {path}.")
+
+
+def websrc_aggregate_results(results):
+    evaluation_result = {}
+
+    # Group results by domain
+    subset_to_eval_samples = defaultdict(list)
+    for result in results:
+        subset_to_eval_samples[result["domain"]].append(result)
+
+    # Evaluate each domain
+    for subset, sub_eval_samples in subset_to_eval_samples.items():
+        judge_dict, metric_dict = evaluate_websrc(sub_eval_samples)
+        metric_dict.update({"num_example": len(sub_eval_samples)})
+        evaluation_result[subset] = metric_dict
+
+    # Aggregate results for all domains
+    printable_results = {}
+    for domain in DOMAINS:
+        if domain not in evaluation_result:
+            continue
+        printable_results[domain] = {
+            "num": int(evaluation_result[domain]["num_example"]),
+            "f1": round(evaluation_result[domain]["f1"], 3),
+        }
+    all_ins_f1 = np.sum([cat_results["f1"] * cat_results["num_example"] for cat_results in evaluation_result.values()]) / sum([cat_results["num_example"] for cat_results in evaluation_result.values()])
+    printable_results["Overall"] = {
+        "num": sum([cat_results["num_example"] for cat_results in evaluation_result.values()]),
+        "f1": round(all_ins_f1, 3),
+    }
+    print(printable_results)
+    return printable_results["Overall"]["f1"]
+
+
+##################
+# Helper functions written by official MMMU repo.
+##################
+DOMAINS = [
+    "auto",
+    "book",
+    "camera",
+    "game",
+    "jobs",
+    "movie",
+    "phone",
+    "restaurant",
+    "sports",
+    "university",
+    "hotel",
+]
+
+
+def evaluate_websrc(samples):
+    def _normalize_str(string):
+        # lower it
+        string = string.lower()
+
+        # strip leading and trailing whitespaces
+        string = string.strip()
+
+        return string
+
+    def _tokenize(text):
+        # Regex pattern to match words and isolate punctuation
+        pattern = r"\w+|[^\w\s]"
+        tokens = re.findall(pattern, text)
+        return tokens
+
+    def _compute_f1(sa, sb):
+        sa = _normalize_str(sa)
+        sb = _normalize_str(sb)
+
+        sa = _tokenize(sa)
+        sb = _tokenize(sb)
+
+        sa = set(sa)
+        sb = set(sb)
+
+        if len(sa) == 0 or len(sb) == 0:
+            return 0.0
+
+        comm = sa.intersection(sb)
+        prec = len(comm) / len(sb)
+        rec = len(comm) / len(sa)
+        f1 = 2 * prec * rec / (prec + rec) if prec + rec > 0 else 0
+        return f1
+
+    judge_list = []
+    for sample in samples:
+        judge_list.append(_compute_f1(sample["answer"], sample["parsed_pred"]))
+
+    f1 = np.mean(judge_list)
+    return judge_list, {"f1": f1}
diff --git a/lmms_eval/tasks/websrc/websrc.yaml b/lmms_eval/tasks/websrc/websrc.yaml
new file mode 100644
index 00000000..e32bed9b
--- /dev/null
+++ b/lmms_eval/tasks/websrc/websrc.yaml
@@ -0,0 +1,4 @@
+group: websrc
+task:
+- websrc_val
+- websrc_test
diff --git a/lmms_eval/tasks/websrc/websrc_test.yaml b/lmms_eval/tasks/websrc/websrc_test.yaml
new file mode 100644
index 00000000..b19ec2ca
--- /dev/null
+++ b/lmms_eval/tasks/websrc/websrc_test.yaml
@@ -0,0 +1,19 @@
+dataset_path: rootsautomation/websrc-test
+task: "websrc_test"
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.websrc_doc_to_visual
+doc_to_text: !function utils.websrc_doc_to_text
+doc_to_target: "answer"
+# The return value of process_results will be used by metrics
+process_results: !function utils.websrc_process_results
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+generation_kwargs:
+  max_new_tokens: 16
+  image_aspect_ratio: pad
+metric_list:
+  - metric: submission
+    aggregation: !function utils.websrc_test_aggregate_results_for_submission
+    higher_is_better: true
+metadata:
+  - version: 0.0
\ No newline at end of file
diff --git a/lmms_eval/tasks/websrc/websrc_val.yaml b/lmms_eval/tasks/websrc/websrc_val.yaml
new file mode 100644
index 00000000..aeac7d06
--- /dev/null
+++ b/lmms_eval/tasks/websrc/websrc_val.yaml
@@ -0,0 +1,19 @@
+dataset_path: rootsautomation/websrc
+task: "websrc_val"
+test_split: dev
+output_type: generate_until
+doc_to_visual: !function utils.websrc_doc_to_visual
+doc_to_text: !function utils.websrc_doc_to_text
+doc_to_target: "answer"
+# The return value of process_results will be used by metrics
+process_results: !function utils.websrc_process_results
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+generation_kwargs:
+  max_new_tokens: 16
+  image_aspect_ratio: pad
+metric_list:
+  - metric: websrc_squad_f1
+    aggregation: !function utils.websrc_aggregate_results
+    higher_is_better: true
+metadata:
+  - version: 0.0
\ No newline at end of file
diff --git a/example_eval.yaml b/miscs/example_eval.yaml
similarity index 100%
rename from example_eval.yaml
rename to miscs/example_eval.yaml
diff --git a/llava_repr_requirements.txt b/miscs/llava_repr_requirements.txt
similarity index 100%
rename from llava_repr_requirements.txt
rename to miscs/llava_repr_requirements.txt
diff --git a/miscs/repr_scripts.sh b/miscs/repr_scripts.sh
index 27fccbaf..cc9a8ae7 100755
--- a/miscs/repr_scripts.sh
+++ b/miscs/repr_scripts.sh
@@ -11,4 +11,4 @@ pip install -r llava_repr_requirements.txt
 
 # Run and exactly reproduce llava_v1.5 results!
 # mme as an example
-accelerate launch --num_processes=1 -m lmms_eval --model llava   --model_args pretrained="liuhaotian/llava-v1.5-7b,use_flash_attention_2=False"   --tasks mme  --batch_size 1 --log_samples --log_samples_sufix reproduce --output_path ./logs/
\ No newline at end of file
+accelerate launch --num_processes=1 -m lmms_eval --model llava   --model_args pretrained="liuhaotian/llava-v1.5-7b,use_flash_attention_2=False,device_map=auto"   --tasks mme  --batch_size 1 --log_samples --log_samples_suffix reproduce --output_path ./logs/
\ No newline at end of file
diff --git a/miscs/tinyllava_repr_requirements.txt b/miscs/tinyllava_repr_requirements.txt
new file mode 100644
index 00000000..b6ab1cb1
--- /dev/null
+++ b/miscs/tinyllava_repr_requirements.txt
@@ -0,0 +1,39 @@
+accelerate==0.27.2
+datasets==2.16.1
+deepspeed==0.14.0
+einops==0.6.1
+einops-exts==0.0.4
+evaluate==0.4.1
+hf_transfer==0.1.6
+Jinja2==3.1.3
+numpy==1.26.4
+openai==1.13.3
+openpyxl
+packaging==23.2
+pandas==2.2.1
+peft==0.10.0
+Pillow==10.2.0
+protobuf==4.25.3
+pycocoevalcap==1.2
+pycocotools==2.0.7
+pytablewriter==1.2.0
+pytest==8.0.2
+python_Levenshtein==0.25.0
+pytz==2024.1
+PyYAML==6.0.1
+Requests==2.31.0
+sacrebleu==2.4.0
+scikit_learn==1.2.2
+sentencepiece==0.1.99
+setuptools==68.2.2
+sglang==0.1.12
+shortuuid==1.0.12
+sqlitedict==2.1.0
+tenacity==8.2.3
+tiktoken
+# torch==2.0.1
+# torchvision==0.15.2
+tokenizers==0.15.1
+timm==0.6.13
+tqdm==4.66.2
+transformers==4.39.3
diff --git a/miscs/tinyllava_repr_scripts.sh b/miscs/tinyllava_repr_scripts.sh
new file mode 100755
index 00000000..902ff0d4
--- /dev/null
+++ b/miscs/tinyllava_repr_scripts.sh
@@ -0,0 +1,25 @@
+# install lmms_eval without building dependencies
+cd lmms_eval;
+pip install --no-deps -U -e .
+
+# install TinyLLaVA without building dependencies
+cd ..
+git clone https://github.com/TinyLLaVA/TinyLLaVA_Factory
+cd TinyLLaVA_Factory
+pip install --no-deps -U -e .
+
+# install all the requirements that require for reproduce llava results
+pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu118
+pip install -r tinyllava_repr_requirements.txt
+
+# Run and reproduce tinyllava best results!
+accelerate launch \
+    --num_processes=1 \
+    -m lmms_eval \
+    --model tinyllava \
+    --model_args pretrained=tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B,conv_mode=phi \
+    --tasks vqav2,gqa,scienceqa_img,textvqa,mmvet,pope,mme,mmmu_val \
+    --batch_size 1 \
+    --log_samples \
+    --log_samples_suffix tinyllava-phi2-siglip-3.1b \
+    --output_path ./logs/
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 35089933..5f3b92fb 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "lmms_eval"
-version = "0.1.1"
+version = "0.2.0"
 authors = [
     { name = "LMMMs-Lab Evaluation Team", email = "lmms_eval@outlook.com" },
 ]
diff --git a/tools/live_bench/__init__.py b/tools/live_bench/__init__.py
deleted file mode 100644
index 6ff6a72c..00000000
--- a/tools/live_bench/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from lmms_eval.live_bench.data_generator import LiveBench
diff --git a/tools/live_bench/api/live_bench.py b/tools/live_bench/api/live_bench.py
deleted file mode 100644
index 047a66ef..00000000
--- a/tools/live_bench/api/live_bench.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from lmms_eval.live_bench.websites import load_websites, load_websites_from_file
-from lmms_eval.live_bench import LiveBench
-
-
-def generate_live_bench(*, force_clear=False, screen_shoter="single_screen", qa_generator="gpt4v", scorer="gpt4v", checker="gemini", driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}):
-    website = load_websites()
-    dataset = LiveBench(force_clear=force_clear)
-    dataset.capture(websites=website, screen_shoter=screen_shoter, qa_generator=qa_generator, scorer=scorer, checker=checker, driver_kwargs=driver_kwargs, shoter_kwargs=shoter_kwargs, generator_kwargs=generator_kwargs)
-    dataset.upload()
-
-
-def generate_live_bench_from_path(path, *, force_clear=False, qa_generator="gpt4v", scorer="gpt4v", checker="gemini", driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}):
-    website = load_websites_from_file(path)
-    dataset = LiveBench(force_clear=force_clear)
-    dataset.capture(websites=website, screen_shoter="human", qa_generator=qa_generator, scorer=scorer, checker=checker, driver_kwargs=driver_kwargs, shoter_kwargs=shoter_kwargs, generator_kwargs=generator_kwargs)
-    dataset.upload()
-
-
-if __name__ == "__main__":
-    generate_live_bench()
diff --git a/tools/live_bench/data_generator/__init__.py b/tools/live_bench/data_generator/__init__.py
deleted file mode 100644
index e8b8850b..00000000
--- a/tools/live_bench/data_generator/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from lmms_eval.live_bench.data_generator.qa_generator import get_generator, get_random_generator
-from lmms_eval.live_bench.data_generator.live_bench_data import LiveBenchData
-from lmms_eval.live_bench.data_generator.live_bench import LiveBench
-from lmms_eval.live_bench.data_generator.response import Response
diff --git a/tools/live_bench/data_generator/check_prompt.md b/tools/live_bench/data_generator/check_prompt.md
deleted file mode 100644
index 08c73476..00000000
--- a/tools/live_bench/data_generator/check_prompt.md
+++ /dev/null
@@ -1,11 +0,0 @@
-I would like you to act as a quizmaster who designs questions based on a provided image that would challenge adults to think critically. The image in question is a screenshot from the homepage or section of news website. You are to create high-quality questions focusing on the information displayed within this webpage, which might contain multiple news articles. Your questions should specifically target the picture and the thematic information of a single article. Your question should be answerable, and checkable. Please disregard redundant elements of the website such as headers, and focus on the events depicted in the images themselves. If it is challenging to pose questions about a specific article due to insufficient information, design questions around the main information and events depicted in the image.
-
-A well-crafted question about an event should allow respondents to gain deeper insights by observing and analyzing the event, paying attention to the following aspects:
-
-- Basic Understanding
-- Contextual Analysis
-- Deeper Implications
-- Broader Implications
-- Further Insights
-
-Now, you are given a screenshot of the homepage of a news website, with a already generated question and answer. Your task is to refine the question and answer, and refractor them to make the question more answerable, checkable, and challenging. If you don't think the question is good, please provide a new question and answer.
diff --git a/tools/live_bench/data_generator/example/example_output.json b/tools/live_bench/data_generator/example/example_output.json
deleted file mode 100644
index 3cedea00..00000000
--- a/tools/live_bench/data_generator/example/example_output.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-    "Basic Understanding": {
-        "Question": "What is the central issue discussed in the article titled \"Kidnapped by One Side, Maimed by the Other: A Teenager’s Ordeal in Congo’s 30-Year War\" and who is responsible for the information presented?",
-        "Answer": "The central issue discussed is the impact of a new offensive by a Rwanda-backed militia in Congo, leading to significant civilian distress. The article is authored by Gabriele Steinhauser, with photographs by Jonathan Torgovnik for The Wall Street Journal."
-    },
-    "Contextual Analysis": {
-        "Question": "Analyze how the photo accompanying the article about the Congo war might influence the reader's perception of the conflict.",
-        "Answer": "The photograph shows a young individual, possibly the teenager mentioned in the title, in a vulnerable state which personalizes the conflict and evokes empathy. This visual representation could lead readers to feel a deeper connection to the issue, emphasizing the human cost of the conflict over political or strategic aspects."
-    },
-    "Deeper Implications": {
-        "Question": "Considering the ongoing violence described in the \"New Darfur Attacks Feed Concerns Over More Large-Scale Violence\" article, what could be the broader implications for regional stability in Africa?",
-        "Answer": "The surge in violence in North Darfur, as noted, raises concerns about escalating conflicts that could destabilize neighboring regions and lead to wider security challenges across Africa. It might strain international relations and humanitarian resources, potentially leading to international interventions or increased refugee flows."
-    },
-    "Comparative Analysis": {
-        "Question": "Compare and contrast the international reactions to the events in Congo and Darfur, as suggested by the articles. What might explain any differences in these reactions?",
-        "Answer": "The international reaction to Darfur’s violence involves direct warnings from U.S. officials and the U.N., suggesting a proactive stance. In contrast, the Congo situation appears to be treated as part of a longer, ongoing conflict with possibly less immediate international urgency. Differences in reactions could be influenced by geopolitical interests, media attention, and the current strategic priorities of international players."
-    }
-}
diff --git a/tools/live_bench/data_generator/example/example_website.png b/tools/live_bench/data_generator/example/example_website.png
deleted file mode 100644
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z=-1itImY59(>pr4ySp+szIOua?#ezRYAYepQLix_JTY;{{FCj<EOT>@>_U&?6DqTg
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diff --git a/tools/live_bench/data_generator/live_bench.py b/tools/live_bench/data_generator/live_bench.py
deleted file mode 100644
index 4cd9da42..00000000
--- a/tools/live_bench/data_generator/live_bench.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import os
-from typing import List
-from tqdm import tqdm
-from lmms_eval.live_bench.data_generator.live_bench_data import LiveBenchData
-from datasets import Dataset, load_dataset
-from lmms_eval.live_bench.websites import Website
-from lmms_eval.live_bench.driver import load_driver
-from lmms_eval.live_bench.data_generator import get_generator, get_random_generator
-from lmms_eval.live_bench.screen_shoter import get_shoter
-from lmms_eval.live_bench.data_generator.qa_generator import QAGenerator, QAData
-from lmms_eval.live_bench.screen_shoter import ScreenImage, ScreenShoter
-from lmms_eval.live_bench.data_generator.score_getter import get_score_getter, get_random_score_getter
-import datasets
-
-from typing import List
-import logging
-
-logger = logging.getLogger("lmms-eval")
-
-
-def get_qa_data(images: ScreenImage, qa_generator: QAGenerator, test=False) -> List[QAData]:
-    response = qa_generator.generate(images, test=test)
-    qa_data = qa_generator.format_response(response)
-    return qa_data
-
-
-def get_live_bench_data(driver, website: Website, screen_shoter: ScreenShoter, qa_generator: QAGenerator, checker: QAGenerator, test=False, scorer=None) -> List[LiveBenchData]:
-    images = screen_shoter.capture(driver, website)
-    qa_data = get_qa_data(images, qa_generator, test=test)
-    data = []
-    for qa in qa_data:
-        data.append(LiveBenchData(screen=images, question=qa.question, answer=qa.answer, subtask=qa.subtask, data_generator=qa_generator.get_name(), checker=checker, scorer=scorer))
-    return data
-
-
-class LiveBench(object):
-    def __init__(self, path: str = "lmms-lab/LiveBench", *, name="default", split="test", cache_dir=None, remote_path=None, trust_remote_code=True, force_clear=False, **kwargs):
-        self.path = path
-        self.name = name
-        self.split = split
-        self.cache_dir = cache_dir
-        self.dataset_kwargs = kwargs
-        if remote_path is None:
-            self.remote_path = path
-        if force_clear:
-            self.clear()
-        else:
-            try:
-                self.hf_data = load_dataset(self.path, name=name, split=split, cache_dir=cache_dir, trust_remote_code=trust_remote_code, **kwargs)
-            except Exception as e:
-                logger.error(f"Error loading dataset: {e}")
-                self.clear()
-
-    def clear(self):
-        self.hf_data = Dataset.from_dict(
-            {"id": [], "images": [], "website": [], "question": [], "answer": [], "subtask": [], "data_generator": [], "checker": [], "date_time": [], "screen_shoter": [], "screen_size": [], "score": [], "reason": [], "scorer_name": []},
-            features=LiveBenchData.features,
-        )
-
-    def add(self, data: LiveBenchData, id: int = None):
-        if id is None:
-            id = len(self.hf_data)
-        organized_data = data.to_hf_dict()
-        organized_data["id"] = id
-        self.hf_data = self.hf_data.add_item(organized_data)
-
-    def capture(self, websites: List[Website] = None, *, screen_shoter="single_screen", qa_generator=None, checker=None, driver=None, scorer=None, test=False, driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}):
-        can_quit_driver = False
-        if driver is None and screen_shoter != "human":
-            driver = load_driver(**driver_kwargs)
-            can_quit_driver = True
-        screen_shoter = get_shoter(screen_shoter, **shoter_kwargs)
-        if qa_generator is not None:
-            qa_generator = get_generator(qa_generator, **generator_kwargs)
-        else:
-            qa_generator = get_random_generator(**generator_kwargs)
-        if checker is None:
-            checker = get_random_generator(**generator_kwargs)
-        else:
-            checker = get_generator(checker, **generator_kwargs)
-        if scorer is not None and isinstance(scorer, str):
-            scorer = get_score_getter(scorer)
-        elif scorer is None:
-            scorer = get_random_score_getter()
-        for website in tqdm(websites, desc="Capturing websites"):
-            try:
-                data = get_live_bench_data(driver, website, screen_shoter, qa_generator, checker, test=test, scorer=scorer)
-                for d in data:
-                    self.add(d)
-            except Exception as e:
-                logger.error(f"Error capturing website: {e}")
-                logger.error(f"Website: {website.get_info()}")
-                continue
-        if can_quit_driver:
-            driver.quit()
-
-    def upload(self, **kwargs):
-        self.hf_data.push_to_hub(self.remote_path, config_name=self.name, split=self.split, **kwargs)
-
-    def save(self, path: str):
-        self.hf_data.save_to_disk(path)
-        logger.info(f"Data saved to {os.path.abspath(path)}")
diff --git a/tools/live_bench/data_generator/live_bench_data.py b/tools/live_bench/data_generator/live_bench_data.py
deleted file mode 100644
index f2e378c0..00000000
--- a/tools/live_bench/data_generator/live_bench_data.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from lmms_eval.live_bench.screen_shoter.screen import ScreenImage
-import datasets
-
-
-class LiveBenchData(object):
-    features = datasets.Features(
-        {
-            "id": datasets.Value("int32"),
-            "images": datasets.Sequence(datasets.Image()),
-            "website": datasets.Value("string"),
-            "question": datasets.Value("string"),
-            "answer": datasets.Value("string"),
-            "subtask": datasets.Value("string"),
-            "data_generator": datasets.Value("string"),
-            "checker": datasets.Value("string"),
-            "date_time": datasets.Value("string"),
-            "screen_shoter": datasets.Value("string"),
-            "screen_size": datasets.Value("string"),
-            "score": datasets.Value("int32"),
-            "reason": datasets.Value("string"),
-            "scorer_name": datasets.Value("string"),
-        }
-    )
-
-    def __init__(self, *, screen: ScreenImage, question: str, answer: str, subtask: str, data_generator: str, score: int = None, reason: str = None, checker=None, scorer_name=None, scorer=None):
-        self.screen = screen
-        self.question = question
-        self.answer = answer
-        self.subtask = subtask
-        self.data_generator = data_generator
-        self.checker = None
-        if checker is not None:
-            response = checker.check(screen, question, answer, subtask)
-            if response.success:
-                formatted_response = checker.format_checked_response(response)
-                if formatted_response.question and formatted_response.answer:
-                    self.question = formatted_response.question
-                    self.answer = formatted_response.answer
-                    if formatted_response.subtask:
-                        self.subtask = formatted_response.subtask
-                    else:
-                        self.subtask = subtask
-                    self.checker = checker.get_name()
-        if score is not None:
-            self.score = score
-            self.reason = reason
-            self.scorer_name = scorer_name
-        else:
-            score = scorer.get_score(question, answer, screen.images)
-            self.score = score.score
-            self.reason = score.reason
-            self.scorer_name = scorer.get_name()
-
-    def to_dict(self):
-        images = self.screen.images
-        website = self.screen.website.get_info()
-        question = self.question
-        answer = self.answer
-        subtask = self.subtask
-        data_generator = self.data_generator
-        date_time = self.screen.capture_datetime
-        screen_shoter = self.screen.shoter
-        screen_size = self.screen.screen_size
-        return {
-            "images": images,
-            "website": website,
-            "question": question,
-            "answer": answer,
-            "subtask": subtask,
-            "data_generator": data_generator,
-            "checker": self.checker,
-            "date_time": date_time,
-            "screen_shoter": screen_shoter,
-            "screen_size": screen_size,
-            "score": self.score,
-            "reason": self.reason,
-            "scorer_name": self.scorer_name,
-        }
-
-    def to_hf_dict(self):
-        return self.features.encode_example(self.to_dict())
-
-    def to_output_dict(self):
-        return {
-            "screen": self.screen.to_output_dict(),
-            "question": self.question,
-            "answer": self.answer,
-            "subtask": self.subtask,
-            "data_generator": self.data_generator,
-            "checker": self.checker,
-            "score": self.score,
-            "reason": self.reason,
-            "scorer_name": self.scorer_name,
-        }
diff --git a/tools/live_bench/data_generator/prompt.md b/tools/live_bench/data_generator/prompt.md
deleted file mode 100644
index 0b6c5c3f..00000000
--- a/tools/live_bench/data_generator/prompt.md
+++ /dev/null
@@ -1,13 +0,0 @@
-I would like you to act as a quizmaster who designs questions based on a provided image that would challenge adults to think critically. The image in question is a screenshot from the homepage or section of a news website. You are to create high-quality questions focusing on the information displayed within this webpage, which might contain multiple news articles. Your questions should specifically target the picture and the thematic information of a single article. Your question should be answerable, and checkable. Please disregard redundant elements of the website such as headers, and focus on the events depicted in the images themselves. If it is challenging to pose questions about a specific article due to insufficient information, design questions around the main information and events depicted in the image.
-
-A well-crafted question about an event should allow respondents to gain deeper insights by observing and analyzing the event, paying attention to the following aspects:
-
-- Basic Understanding
-- Contextual Analysis
-- Deeper Implications
-- Broader Implications
-- Further Insights
-
-Consider designing a multi-round Q&A process, progressively deepening the understanding of the event’s essence. Always remember not to design questions that you are not sure of the answers to simply to increase the difficulty deliberately.
-
-Here are few examples about the task and the expected output format.
diff --git a/tools/live_bench/data_generator/qa_generator.py b/tools/live_bench/data_generator/qa_generator.py
deleted file mode 100644
index 107172f6..00000000
--- a/tools/live_bench/data_generator/qa_generator.py
+++ /dev/null
@@ -1,337 +0,0 @@
-import io
-import re
-import os
-import openai
-import base64
-import json
-import random
-import logging
-import pathlib
-import textwrap
-import google.generativeai as genai
-from time import sleep
-from PIL import Image
-from typing import List
-from abc import ABC, abstractmethod
-from lmms_eval.live_bench.data_generator.response import Response
-from lmms_eval.live_bench.screen_shoter import ScreenImage
-from lmms_eval.live_bench.data_generator.utils.gpt4v import format_gpt4v_images, gpt4v_generate_response
-from lmms_eval.live_bench.data_generator.utils.gemini import gemini_generate_response
-
-logger = logging.getLogger("lmms-eval")
-
-
-class QAData(object):
-    def __init__(self, question: str = None, answer: str = None, subtask: str = None):
-        self.question = question
-        self.answer = answer
-        self.subtask = subtask
-
-    def to_dict(self):
-        return {"question": self.question, "answer": self.answer}
-
-
-class QAGenerator(ABC):
-    def __init__(self, prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md")):
-        self.prompt_file = prompt_file
-        self.prompt = self._load_prompt()
-
-    def _load_prompt(self):
-        with open(self.prompt_file, "r") as f:
-            return f.read()
-
-    def __call__(self, images: ScreenImage, *args, **kwargs):
-        return self.generate(images, *args, **kwargs)
-
-    def generate(self, images: ScreenImage, *, test=False, **kwargs) -> Response:
-        if test:
-            return Response(success=True, content="This is a test response.", full_log={})
-        return self._generate(images, **kwargs)
-
-    def check(self, images: ScreenImage, question, answer, subtask, *, test=False, **kwargs) -> Response:
-        if test:
-            return Response(success=True, content="This is a test response.", full_log={})
-        return self._check(images, question, answer, subtask, **kwargs)
-
-    @abstractmethod
-    def _generate(self, images: ScreenImage, **kwargs) -> Response:
-        raise NotImplementedError("_generate not implemented")
-
-    @abstractmethod
-    def _check(self, images: ScreenImage, question, answer, subtask, **kwargs) -> Response:
-        raise NotImplementedError("_check not implemented")
-
-    def format_response(self, response: Response) -> QAData:
-        if response.success:
-            qa_data = self._format_response(response)
-            if qa_data is None:
-                return []
-            else:
-                return qa_data
-        else:
-            return []
-
-    @abstractmethod
-    def _format_response(self, response: Response) -> str:
-        raise NotImplementedError("format_response not implemented")
-
-    @abstractmethod
-    def format_checked_response(self, response: Response) -> QAData:
-        raise NotImplementedError("format_checked_response not implemented")
-
-    def get_name(self) -> str:
-        raise NotImplementedError("get_name not implemented")
-
-
-class GeneratorRegistry:
-    def __init__(self):
-        self.generators = {}
-
-    def register_generator(self, name):
-        def decorator(cls):
-            self.generators[name] = cls
-            cls.get_name = lambda self: name
-            return cls
-
-        return decorator
-
-    def get_generator(self, name) -> QAGenerator:
-        return self.generators[name]
-
-    def get_random_generator(self) -> QAGenerator:
-        return random.choice(list(self.generators.values()))
-
-
-generator_registry = GeneratorRegistry()
-
-
-def register_generator(name):
-    return generator_registry.register_generator(name)
-
-
-def get_generator(name, *args, **kwargs) -> QAGenerator:
-    return generator_registry.get_generator(name)(*args, **kwargs)
-
-
-def get_random_generator(*args, **kwargs) -> QAGenerator:
-    return generator_registry.get_random_generator()(*args, **kwargs)
-
-
-@register_generator("gpt4v")
-class GPT4Generator(QAGenerator):
-    def __init__(
-        self,
-        prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md"),
-        model="gpt-4-turbo",
-        example_path=os.path.join(os.path.dirname(__file__), "example"),
-        check_prompt=os.path.join(os.path.dirname(__file__), "check_prompt.md"),
-    ):
-        super().__init__(prompt_file)
-        API_KEY = os.getenv("OPENAI_API_KEY")
-        if not API_KEY:
-            raise ValueError("OPENAI_API_KEY environment variable not set.")
-        self.api_key = API_KEY
-        self.client = openai.OpenAI(api_key=self.api_key)
-        self.model = model
-        if os.path.exists(example_path):
-            self.example_path = example_path
-        else:
-            self.example_path = None
-        if os.path.exists(check_prompt):
-            with open(check_prompt, "r") as f:
-                self.check_prompt = f.read()
-        else:
-            self.check_prompt = check_prompt
-
-    def format_messages(self, images: List[Image.Image], example_image: Image.Image, example_output: str):
-        example = [
-            format_gpt4v_images(example_image),
-            {
-                "type": "text",
-                "text": example_output,
-            },
-        ]
-        content = example + [format_gpt4v_images(image) for image in images]
-        content.append(
-            {
-                "type": "text",
-                "text": "Please generate high-quality questions focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.",
-            },
-        )
-        messages = [
-            {
-                "role": "system",
-                "content": self.prompt,
-            },
-            {
-                "role": "user",
-                "content": content,
-            },
-        ]
-        return messages
-
-    def _generate(self, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs):
-        if self.example_path:
-            example_image_path = os.path.join(self.example_path, "example_website.png")
-            example_output_path = os.path.join(self.example_path, "example_output.json")
-            example_image = Image.open(example_image_path)
-            with open(example_output_path, "r") as f:
-                example_output = f.read()
-
-        messages = self.format_messages(images.images, example_image, example_output)
-
-        return gpt4v_generate_response(self.client, self.model, messages, max_tokens, max_try_times, **kwargs)
-
-    def get_check_prompt(self, question: str, answer: str, subtask, images: List[Image.Image]):
-        messages = [
-            {
-                "role": "system",
-                "content": self.check_prompt,
-            }
-        ]
-        content = []
-        for img in images:
-            content.append(format_gpt4v_images(img))
-        content.append(
-            {
-                "type": "text",
-                "text": f"Question: {question}\nQuestioner's Answer: {answer}\nSubtask: {subtask}",
-            },
-        )
-        content.append(
-            {
-                "type": "text",
-                "text": "Please rephrase or rewrite the high-quality question focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.",
-            },
-        )
-        messages.append(
-            {
-                "role": "user",
-                "content": content,
-            }
-        )
-        return messages
-
-    def _check(self, images: ScreenImage, question, answer, subtask, *, max_tokens=4096, max_try_times=5, **kwargs):
-        messages = self.get_check_prompt(question, answer, subtask, images.images)
-        return gpt4v_generate_response(self.client, self.model, messages, max_tokens, max_try_times, **kwargs)
-
-    def format_checked_response(self, response: Response):
-        data = json.loads(response.content)
-        question = data.get("question", None)
-        answer = data.get("answer", None)
-        subtask = data.get("subtask", None)
-        return QAData(question=question, answer=answer, subtask=subtask)
-
-    def _format_response(self, response: Response) -> List[QAData]:
-        try:
-            qa_data = []
-            content = json.loads(response.content)
-            for subtask, message in content.items():
-                subtask = subtask.lower()
-                message_lower = {k.lower(): v for k, v in message.items()}
-                try:
-                    question = message_lower["question"]
-                    answer = message_lower["answer"]
-                    qa_data.append(QAData(question=question, answer=answer, subtask=subtask))
-                except KeyError as e:
-                    logger.error(f"Failed to parse response: {message}")
-                    logger.error(f"Error: {e}")
-            return qa_data
-        except Exception as e:
-            logger.error(f"Failed to format response: {e}")
-            return []
-
-
-@register_generator("gemini")
-class GeminiGenerator(QAGenerator):
-    def __init__(
-        self,
-        prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md"),
-        model="gemini-pro-vision",
-        example_path=os.path.join(os.path.dirname(__file__), "example"),
-        check_prompt=os.path.join(os.path.dirname(__file__), "check_prompt.md"),
-    ):
-        super().__init__(prompt_file)
-        GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-        if not GOOGLE_API_KEY:
-            raise ValueError("GOOGLE_API_KEY environment variable not set.")
-        genai.configure(api_key=GOOGLE_API_KEY)
-
-        self.api_key = GOOGLE_API_KEY
-        self.model = model
-        self.client = genai.GenerativeModel(model)
-        if os.path.exists(example_path):
-            self.example_path = example_path
-        else:
-            self.example_path = None
-        if os.path.exists(check_prompt):
-            with open(check_prompt, "r") as f:
-                self.check_prompt = f.read()
-        else:
-            self.check_prompt = check_prompt
-
-    def format_messages(self, images: List[Image.Image], example_image: Image.Image, example_output: str):
-        content = [self.prompt, "\n", "Example Image:", example_image, "\n", "Example Output:", example_output]
-        content.extend(images)
-        content.append("Please generate high-quality questions focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.")
-        return content
-
-    def _generate(self, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs):
-        if self.example_path:
-            example_image_path = os.path.join(self.example_path, "example_website.png")
-            example_output_path = os.path.join(self.example_path, "example_output.json")
-            example_image = Image.open(example_image_path)
-            with open(example_output_path, "r") as f:
-                example_output = f.read()
-
-        messages = self.format_messages(images.images, example_image, example_output)
-
-        return gemini_generate_response(self.client, messages, max_tokens, max_try_times, **kwargs)
-
-    def get_check_prompt(self, question: str, answer: str, subtask, images: List[Image.Image]):
-        content = [self.check_prompt] + images
-        content.append(f"Question: {question}\nQuestioner's Answer: {answer}\nSubtask: {subtask}")
-        content.append("Your response should be strictly in the below format:\n\nQuestion: <question>\nAnswer: <answer>\nSubtask: <subtask>")
-        return content
-
-    def _check(self, images: ScreenImage, question, answer, subtask, *, max_tokens=4096, max_try_times=5, **kwargs):
-        messages = self.get_check_prompt(question, answer, subtask, images.images)
-        return gemini_generate_response(self.client, messages, max_tokens, max_try_times, **kwargs)
-
-    def format_checked_response(self, response: Response):
-        # Extract the question, answer, and subtask from the normalized content
-        question_match = re.search(r"question:\s*(.*?)\nAnswer:", response.content, re.IGNORECASE | re.DOTALL)
-        answer_match = re.search(r"answer:\s*(.*?)\n(Subtask:|$)", response.content, re.IGNORECASE | re.DOTALL)
-        subtask_match = re.search(r"subtask:\s*(.*)", response.content, re.IGNORECASE)
-
-        question = answer = subtask = None
-
-        if question_match:
-            # Extract the matched groups
-            question = question_match.group(1).strip()
-        if answer_match:
-            answer = answer_match.group(1).strip()
-        if subtask_match:
-            subtask = subtask_match.group(1).strip()
-
-        return QAData(question=question, answer=answer, subtask=subtask)
-
-    def _format_response(self, response: Response) -> List[QAData]:
-        try:
-            qa_data = []
-            content = json.loads(response.content)
-            for subtask, message in content.items():
-                subtask = subtask.lower()
-                message_lower = {k.lower(): v for k, v in message.items()}
-                try:
-                    question = message_lower["question"]
-                    answer = message_lower["answer"]
-                    qa_data.append(QAData(question=question, answer=answer, subtask=subtask))
-                except KeyError as e:
-                    logger.error(f"Failed to parse response: {message}")
-                    logger.error(f"Error: {e}")
-            return qa_data
-        except Exception as e:
-            logger.error(f"Failed to format response: {e}")
-            return []
diff --git a/tools/live_bench/data_generator/response.py b/tools/live_bench/data_generator/response.py
deleted file mode 100644
index 5106b70a..00000000
--- a/tools/live_bench/data_generator/response.py
+++ /dev/null
@@ -1,5 +0,0 @@
-class Response(object):
-    def __init__(self, success: bool, content: str, full_log: dict):
-        self.success = success
-        self.content = content
-        self.full_log = full_log
diff --git a/tools/live_bench/data_generator/score_getter.py b/tools/live_bench/data_generator/score_getter.py
deleted file mode 100644
index 990fc306..00000000
--- a/tools/live_bench/data_generator/score_getter.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import os
-import json
-import random
-import openai
-import anthropic
-from abc import ABC, abstractmethod
-from typing import List
-from PIL import Image
-from lmms_eval.live_bench.screen_shoter import ScreenImage
-from lmms_eval.live_bench.data_generator.qa_generator import Response
-from lmms_eval.live_bench.data_generator.utils.gpt4v import format_gpt4v_images, gpt4v_generate_response
-from lmms_eval.live_bench.data_generator.utils.claude import format_claude_images, claude_generate_response
-
-
-class Score(object):
-    def __init__(self, score: int, reason: str):
-        self.score = score
-        self.reason = reason
-
-
-class ScoreGetter(ABC):
-    def get_name(self):
-        return self.name
-
-    @abstractmethod
-    def get_score(self, question: str, answer: str, images: ScreenImage):
-        raise NotImplementedError("get_score not implemented")
-
-    def __call__(self, question: str, answer: str, images: ScreenImage, **kwargs):
-        return self.get_score(question, answer, images, **kwargs)
-
-
-class ScoreGetterRegistry:
-    def __init__(self):
-        self.score_getters = {}
-
-    def register_score_getter(self, name):
-        def decorator(cls):
-            self.score_getters[name] = cls
-            cls.name = name
-            return cls
-
-        return decorator
-
-    def get_score_getter(self, name) -> ScoreGetter:
-        return self.score_getters[name]
-
-    def get_random_score_getter(self) -> ScoreGetter:
-        return random.choice(list(self.score_getters.values()))
-
-
-generator_registry = ScoreGetterRegistry()
-
-
-def register_score_getter(name):
-    return generator_registry.register_score_getter(name)
-
-
-def get_score_getter(name, *args, **kwargs) -> ScoreGetter:
-    return generator_registry.get_score_getter(name)(*args, **kwargs)
-
-
-def get_random_score_getter(*args, **kwargs) -> ScoreGetter:
-    return generator_registry.get_random_score_getter()(*args, **kwargs)
-
-
-@register_score_getter("gpt4v")
-class GPT4VScoreGetter(ScoreGetter):
-    def __init__(self, prompt: str = os.path.join(os.path.dirname(__file__), "score_prompt.md"), model="gpt-4-turbo", example_path=os.path.join(os.path.dirname(__file__), "example")):
-        super().__init__()
-        if os.path.exists(prompt):
-            with open(prompt, "r") as f:
-                self.prompt = f.read()
-        else:
-            self.prompt = prompt
-        API_KEY = os.getenv("OPENAI_API_KEY")
-        if not API_KEY:
-            raise ValueError("OPENAI_API_KEY environment variable not set.")
-        self.api_key = API_KEY
-        self.client = openai.OpenAI(api_key=self.api_key)
-        self.model = model
-        if os.path.exists(example_path) and os.path.isfile(os.path.join(example_path, "example_score_input.md")):
-            with open(example_path, "r") as f:
-                self.example = f.read()
-        else:
-            self.example = None
-
-    def _format_prompt(self, question: str, answer: str, images: List[Image.Image]):
-        prompt = [{"role": "system", "content": self.prompt}]
-        messages = []
-        for image in images:
-            messages.append(format_gpt4v_images(image))
-        messages.append({"type": "text", "text": f"Question: {question}\nQuestioner's Answer: {answer}"})
-        messages.append({"type": "text", "text": 'You should format you answer into json format like this: {"score": 10, "reason": "some reason"}'})
-        prompt.append({"role": "user", "content": messages})
-        return prompt
-
-    def get_score(self, question: str, answer: str, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs) -> Score:
-        prompt = self._format_prompt(question, answer, images)
-        try:
-            response = gpt4v_generate_response(self.client, self.model, prompt, max_tokens, max_try_times, **kwargs)
-            if response.success:
-                content = json.loads(response.content)
-                score = content.get("score", None)
-                reason = content.get("reason", None)
-                return Score(score=score, reason=reason)
-            else:
-                return Score(score=None, reason=response.content)
-        except Exception as e:
-            return Score(score=None, reason=str(e))
-
-
-@register_score_getter("claude")
-class ClaudeScoreGetter(ScoreGetter):
-    def __init__(self, prompt: str = os.path.join(os.path.dirname(__file__), "score_prompt.md"), model="claude-3-opus-20240229", example_path=os.path.join(os.path.dirname(__file__), "example")):
-        super().__init__()
-        if os.path.exists(prompt):
-            with open(prompt, "r") as f:
-                self.prompt = f.read()
-        else:
-            self.prompt = prompt
-        API_KEY = os.getenv("ANTHROPIC_API_KEY")
-        if not API_KEY:
-            raise ValueError("ANTHROPIC_API_KEY environment variable not set.")
-        self.api_key = API_KEY
-        self.client = anthropic.Anthropic(api_key=self.api_key)
-        self.model = model
-        if os.path.exists(example_path) and os.path.isfile(os.path.join(example_path, "example_score_input.md")):
-            with open(example_path, "r") as f:
-                self.example = f.read()
-        else:
-            self.example = None
-
-    def _format_prompt(self, question: str, answer: str, images: List[Image.Image]):
-        # prompt = [{"role": "system", "content": self.prompt}]
-        prompt = []
-        messages = []
-        for image in images:
-            messages.append(format_claude_images(image))
-        messages.append({"type": "text", "text": f"Question: {question}\nQuestioner's Answer: {answer}"})
-        messages.append({"type": "text", "text": 'You should format you answer into json format like this: {"score": 10, "reason": "some reason"}'})
-        prompt.append({"role": "user", "content": messages})
-        return prompt
-
-    def get_score(self, question: str, answer: str, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs) -> Score:
-        prompt = self._format_prompt(question, answer, images)
-        try:
-            response = claude_generate_response(self.client, self.model, prompt, self.prompt, max_tokens, max_try_times, **kwargs)
-            if response.success:
-                content = json.loads(response.content)
-                score = content.get("score", None)
-                reason = content.get("reason", None)
-                return Score(score=score, reason=reason)
-            else:
-                return Score(score=None, reason=response.content)
-        except Exception as e:
-            return Score(score=None, reason=str(e))
diff --git a/tools/live_bench/data_generator/score_prompt.md b/tools/live_bench/data_generator/score_prompt.md
deleted file mode 100644
index 3940863e..00000000
--- a/tools/live_bench/data_generator/score_prompt.md
+++ /dev/null
@@ -1 +0,0 @@
-Based on the multi-round Q&A regarding to the image, please evaluate each question and answer from the multi-round Q&A based on the image for their authenticity (whether the information can be directly obtained from the image or reasonably inferred) and logical coherence. For each Q&A pair, provide a rating from 1 to 10, where 1 indicates very poor and 10 indicates excellent. Additionally, please provide a brief explanation for each rating.
diff --git a/tools/live_bench/data_generator/utils/__init__.py b/tools/live_bench/data_generator/utils/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tools/live_bench/data_generator/utils/claude.py b/tools/live_bench/data_generator/utils/claude.py
deleted file mode 100644
index c33f81db..00000000
--- a/tools/live_bench/data_generator/utils/claude.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from PIL import Image
-import io
-import base64
-from lmms_eval.live_bench.data_generator.response import Response
-import logging
-from time import sleep
-
-logger = logging.getLogger("lmms-eval")
-
-
-def format_claude_images(image: Image.Image):
-    buffered = io.BytesIO()
-    image.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-    return {
-        "type": "image",
-        "source": {
-            "type": "base64",
-            "media_type": "image/png",
-            "data": img_str,
-        },
-    }
-
-
-def claude_generate_response(client, model, messages, system, max_tokens: int, max_try_times, **kwargs):
-    messages.append({"role": "assistant", "content": "{"})
-
-    def _generate():
-        return client.messages.create(model=model, messages=messages, max_tokens=max_tokens, system=system, **kwargs)
-
-    for times in range(max_try_times):
-        try:
-            response = _generate()
-            return Response(success=True, content="{" + response.content[0].text, full_log={"input": messages, "output": response})
-        except Exception as e:
-            logger.error(f"Failed to generate response: {e}")
-            if times < max_try_times - 1:
-                logger.info(f"Retrying... ({times+1}/{max_try_times})")
-                sleep(3)
-            else:
-                logger.error("Failed to generate response after retrying.")
-                return Response(success=False, content=str(e), full_log={"input": messages, "output": None})
diff --git a/tools/live_bench/data_generator/utils/gemini.py b/tools/live_bench/data_generator/utils/gemini.py
deleted file mode 100644
index 2e425826..00000000
--- a/tools/live_bench/data_generator/utils/gemini.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import google.generativeai as genai
-from time import sleep
-from lmms_eval.live_bench.data_generator.response import Response
-import logging
-
-logger = logging.getLogger("lmms-eval")
-
-
-def gemini_generate_response(client: genai.GenerativeModel, messages, max_tokens: int, max_try_times, **kwargs):
-    generation_config = genai.GenerationConfig(max_output_tokens=max_tokens)
-
-    def _generate():
-        return client.generate_content(messages, generation_config=generation_config, **kwargs)
-
-    for times in range(max_try_times):
-        try:
-            response = _generate()
-            return Response(success=True, content=response.text, full_log={"input": messages, "output": response})
-        except Exception as e:
-            logger.error(f"Failed to generate response: {e}")
-            if times < max_try_times - 1:
-                logger.info(f"Retrying... ({times+1}/{max_try_times})")
-                sleep(3)
-            else:
-                logger.error("Failed to generate response after retrying.")
-                return Response(success=False, content=str(e), full_log={"input": messages, "output": None})
diff --git a/tools/live_bench/data_generator/utils/gpt4v.py b/tools/live_bench/data_generator/utils/gpt4v.py
deleted file mode 100644
index 4b4c1cd8..00000000
--- a/tools/live_bench/data_generator/utils/gpt4v.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from PIL import Image
-import io
-import base64
-from lmms_eval.live_bench.data_generator.response import Response
-import logging
-from time import sleep
-
-logger = logging.getLogger("lmms-eval")
-
-
-def format_gpt4v_images(image: Image.Image):
-    buffered = io.BytesIO()
-    image.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-    return {
-        "type": "image_url",
-        "image_url": {
-            "url": f"data:image/png;base64,{img_str}",
-        },
-    }
-
-
-def gpt4v_generate_response(client, model, messages, max_tokens: int, max_try_times, **kwargs):
-    def _generate():
-        return client.chat.completions.create(model=model, messages=messages, max_tokens=max_tokens, response_format={"type": "json_object"}, **kwargs)
-
-    for times in range(max_try_times):
-        try:
-            response = _generate()
-            return Response(success=True, content=response.choices[0].message.content, full_log={"input": messages, "output": response})
-        except Exception as e:
-            logger.error(f"Failed to generate response: {e}")
-            if times < max_try_times - 1:
-                logger.info(f"Retrying... ({times+1}/{max_try_times})")
-                sleep(3)
-            else:
-                logger.error("Failed to generate response after retrying.")
-                return Response(success=False, content=str(e), full_log={"input": messages, "output": None})
diff --git a/tools/live_bench/driver/.gitignore b/tools/live_bench/driver/.gitignore
deleted file mode 100644
index 0ef18421..00000000
--- a/tools/live_bench/driver/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-extensions/
diff --git a/tools/live_bench/driver/__init__.py b/tools/live_bench/driver/__init__.py
deleted file mode 100644
index a1565d0b..00000000
--- a/tools/live_bench/driver/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from lmms_eval.live_bench.driver.load_driver import load_driver
diff --git a/tools/live_bench/driver/load_driver.py b/tools/live_bench/driver/load_driver.py
deleted file mode 100644
index e8b1702b..00000000
--- a/tools/live_bench/driver/load_driver.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import os
-import zipfile
-import requests
-from selenium import webdriver
-from webdriver_manager.chrome import ChromeDriverManager
-from selenium.webdriver.chrome.options import Options
-
-import undetected_chromedriver as uc
-
-
-def load_driver(
-    window_size="auto", headless=True, driver="undetected_chromedriver", adblock=True, adblock_version="6.0.2-mv3", extension_cache_dir=os.path.join(os.path.dirname(__file__), "extensions"), *, service=None, additional_options=None
-):
-    options = Options()
-    if headless:
-        options.add_argument("--headless")
-    if adblock:
-        try:
-            adblock_url = f"https://code.getadblock.com/releases/adblockchrome-{adblock_version}.zip"
-            adblock_path = os.path.join(extension_cache_dir, f"adblockchrome-{adblock_version}")
-            if not os.path.isdir(adblock_path):
-                os.makedirs(os.path.join(adblock_path, ".."), exist_ok=True)
-                # Download the adblock zip file
-                response = requests.get(adblock_url)
-                with open(f"{adblock_path}.zip", "wb") as file:
-                    file.write(response.content)
-                # Unzip the downloaded file
-                with zipfile.ZipFile(f"{adblock_path}.zip", "r") as zip_ref:
-                    zip_ref.extractall(adblock_path)
-                # Remove the zip file after extraction
-                os.remove(f"{adblock_path}.zip")
-            options.add_argument(f"--load-extension={os.path.abspath(adblock_path)}")
-        except Exception as e:
-            print(f"Error loading adblock extension: {e}")
-    if driver == "undetected_chromedriver":
-        driver = uc.Chrome(headless=headless, options=options)
-        if window_size != "auto":
-            driver.set_window_size(*window_size)
-        return driver
-    elif driver == "chrome":
-        options = Options()
-        if additional_options is not None:
-            for option in additional_options:
-                options.add_argument(option)
-        if service is None:
-            service = ChromeDriverManager().install()
-        service = webdriver.chrome.service.Service(service)
-        driver = webdriver.Chrome(service=service, options=options)
-        if window_size != "auto":
-            driver.set_window_size(*window_size)
-        return driver
diff --git a/tools/live_bench/example.ipynb b/tools/live_bench/example.ipynb
deleted file mode 100644
index 01f64890..00000000
--- a/tools/live_bench/example.ipynb
+++ /dev/null
@@ -1,103 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from random import sample\n",
-    "\n",
-    "from lmms_eval.live_bench.websites.website import DefaultWebsite\n",
-    "from lmms_eval.live_bench.websites import load_websites\n",
-    "\n",
-    "# website = load_websites()\n",
-    "# website = sample(website, 1)\n",
-    "# website[0].url\n",
-    "website = [DefaultWebsite(url=\"https://www.bbc.com/\")]  # , DefaultWebsite(url=\"https://www.bbc.com/sport\"), DefaultWebsite(url=\"https://www.bbc.com/business\")]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from lmms_eval.live_bench import LiveBench"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset = LiveBench(force_clear=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset.capture(websites=website, driver_kwargs={\"headless\": True}, screen_shoter=\"single_screen\", shoter_kwargs={\"screen_size\": (1024, 1024)}, qa_generator=\"gpt4v\", scorer=\"gpt4v\", checker=\"gemini\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset.hf_data[0][\"images\"][0].save(\"img.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset.upload()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "lmms-eval",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/tools/live_bench/screen_shoter/__init__.py b/tools/live_bench/screen_shoter/__init__.py
deleted file mode 100644
index 6e596c7d..00000000
--- a/tools/live_bench/screen_shoter/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from lmms_eval.live_bench.screen_shoter.screen_shoter import ScreenShoter, register_shoter, get_shoter
-from lmms_eval.live_bench.screen_shoter.screen import ScreenImage
diff --git a/tools/live_bench/screen_shoter/screen.py b/tools/live_bench/screen_shoter/screen.py
deleted file mode 100644
index e8610ce4..00000000
--- a/tools/live_bench/screen_shoter/screen.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import io
-import base64
-
-from PIL import Image
-from typing import List, Tuple
-
-from lmms_eval.live_bench.websites import Website
-
-
-def image_to_base64(image: Image.Image) -> str:
-    buffered = io.BytesIO()
-    image.save(buffered, format="PNG")
-    return base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-
-class ScreenImage(object):
-    def __init__(self, images: List[Image.Image], website: Website, shoter: str, screen_size: Tuple[int, int], capture_datetime: str):
-        self.images = images
-        self.website = website
-        self.shoter = shoter
-        self.screen_size = screen_size
-        self.capture_datetime = capture_datetime
-
-    def to_dict(self):
-        return {"images": self.images, "website": self.website.get_info(), "shoter": self.shoter, "screen_size": self.screen_size, "capture_datetime": self.capture_datetime}
-
-    def to_output_dict(self):
-        output = self.to_dict()
-        output["images"] = [image_to_base64(image) for image in self.images]
-        return output
diff --git a/tools/live_bench/screen_shoter/screen_shoter.py b/tools/live_bench/screen_shoter/screen_shoter.py
deleted file mode 100644
index 45a4c9be..00000000
--- a/tools/live_bench/screen_shoter/screen_shoter.py
+++ /dev/null
@@ -1,141 +0,0 @@
-from selenium import webdriver
-from PIL import Image
-from lmms_eval.live_bench.websites import Website
-from lmms_eval.live_bench.screen_shoter.screen import ScreenImage
-from typing import List
-from abc import ABC, abstractmethod
-from datetime import datetime
-from PIL import Image
-import os
-import io
-import logging
-
-logger = logging.getLogger("lmms-eval")
-
-
-class ScreenShoter(ABC):
-    def __init__(self, screen_size=(1024, 1024)):
-        self.screen_size = screen_size
-
-    def capture(self, driver: webdriver.Chrome, website: Website) -> ScreenImage:
-        if driver is not None:
-            website.visit(driver)
-            if self.screen_size != "auto":
-                driver.set_window_size(self.screen_size[0], self.screen_size[1])
-            else:
-                driver.set_window_size(1024, 1024)
-                page_width = driver.execute_script("return document.body.scrollWidth")
-                driver.set_window_size(page_width, 1024)
-        # print("Screen size:", driver.get_window_size())
-        images = self.get_screenshot(driver)
-        return ScreenImage(images, website, self.get_name(), self.screen_size, datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
-
-    def __call__(self, driver: webdriver.Chrome, website: Website) -> List[Image.Image]:
-        return self.capture(driver, website)
-
-    def get_name(self) -> str:
-        raise NotImplementedError("get_name not implemented")
-
-    @abstractmethod
-    def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]:
-        pass
-
-
-class ScreenShoterRegistry:
-    def __init__(self):
-        self.shoters = {}
-
-    def register_shoter(self, name):
-        def decorator(cls):
-            self.shoters[name] = cls
-            cls.get_name = lambda self: name
-            return cls
-
-        return decorator
-
-    def get_shoter(self, name) -> ScreenShoter:
-        return self.shoters[name]
-
-
-shoter_registry = ScreenShoterRegistry()
-
-
-def register_shoter(name):
-    return shoter_registry.register_shoter(name)
-
-
-def get_shoter(name, *args, **kwargs) -> ScreenShoter:
-    return shoter_registry.get_shoter(name)(*args, **kwargs)
-
-
-@register_shoter("human")
-class HumanScreenShoter(ScreenShoter):
-    def __init__(self, screen_size=None):
-        super().__init__(screen_size)
-
-    def capture(self, driver: webdriver.Chrome, website: Website) -> ScreenImage:
-        path = website.get_path()
-        images = []
-
-        def get_image(path):
-            try:
-                with open(path, "rb") as f:
-                    image_data = f.read()
-                image = Image.open(io.BytesIO(image_data))
-                images.append(image)
-            except Exception as e:
-                logger.error(f"Error loading image {path}: {e}")
-
-        if os.path.isdir(path):
-            for root, dirs, files in os.walk(path):
-                for file_name in files:
-                    get_image(os.path.join(root, file_name))
-        else:
-            try:
-                get_image(path)
-            except Exception as e:
-                logger.error(f"Error loading image {path}: {e}")
-        if not images:
-            raise ValueError(f"No images found in {path}")
-        return ScreenImage(images, website, self.get_name(), self.screen_size, datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
-
-    def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]:
-        return []
-
-
-@register_shoter("single_screen")
-class SingleScreenShoter(ScreenShoter):
-    def __init__(self, screen_size=(1024, 1024)):
-        super().__init__(screen_size)
-
-    def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]:
-        screenshot = driver.get_screenshot_as_png()
-        return [Image.open(io.BytesIO(screenshot))]
-
-
-@register_shoter("rolling_screen")
-class RollingScreenShoter(ScreenShoter):
-    def __init__(self, screen_size=(1024, 1024)):
-        super().__init__(screen_size)
-
-    def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]:
-        screenshots = []
-        # Scroll to the top of the page before taking the first screenshot
-        driver.execute_script("window.scrollTo(0, 0)")
-        # Get the total height of the web page
-        total_height = driver.execute_script("return document.body.parentNode.scrollHeight")
-        # Get the viewport height
-        viewport_height = driver.execute_script("return window.innerHeight")
-        # Initialize the current scroll position
-        current_scroll_position = 0
-
-        # Scroll through the page and take screenshots
-        while current_scroll_position < total_height:
-            # Take screenshot and append to the list
-            screenshot = driver.get_screenshot_as_png()
-            screenshots.append(Image.open(io.BytesIO(screenshot)))
-            # Scroll down by the viewport height
-            current_scroll_position += viewport_height
-            driver.execute_script(f"window.scrollTo(0, {current_scroll_position})")
-
-        return screenshots
diff --git a/tools/live_bench/websites/__init__.py b/tools/live_bench/websites/__init__.py
deleted file mode 100644
index 721a374f..00000000
--- a/tools/live_bench/websites/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from lmms_eval.live_bench.websites.load_website import load_websites, load_websites_from_file
-from lmms_eval.live_bench.websites.website import Website
diff --git a/tools/live_bench/websites/load_website.py b/tools/live_bench/websites/load_website.py
deleted file mode 100644
index 56b96357..00000000
--- a/tools/live_bench/websites/load_website.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import yaml
-import os
-from random import sample
-from lmms_eval.live_bench.websites.website import Website, DefaultWebsite, HumanScreenShotWebsite
-
-
-def get_website(website_dict):
-    if "website_class" not in website_dict:
-        website_class = DefaultWebsite
-    else:
-        website_class = website_dict["website_class"]
-    url = website_dict["url"]
-    if "args" in website_dict:
-        return website_class(url, **website_dict["args"])
-    else:
-        return website_class(url)
-
-
-def load_websites(num_sample: int = -1):
-    website_list_path = os.path.join(os.path.dirname(__file__), "website_list.yaml")
-    with open(website_list_path, "r") as f:
-        website_list = yaml.full_load(f)["websites"]
-    if num_sample > 0:
-        website_list = sample(website_list, num_sample)
-    return [get_website(website_dict) for website_dict in website_list]
-
-
-def load_websites_from_file(file_path):
-    names = os.listdir(file_path)
-    websites = []
-    for name in names:
-        path = os.path.join(file_path, name)
-        websites.append(HumanScreenShotWebsite(path=path, name=name))
-    return websites
diff --git a/tools/live_bench/websites/website.py b/tools/live_bench/websites/website.py
deleted file mode 100644
index 327bad37..00000000
--- a/tools/live_bench/websites/website.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import time
-import os
-
-from webdriver_manager.core.driver import Driver
-from abc import ABC, abstractmethod
-
-
-class Website(ABC):
-    def __init__(self, url=None, name=None, path=None):
-        self.url = url
-        self.name = name
-        self.path = path
-        assert self.url is not None or self.path is not None, "Either url or path must be provided"
-
-    def get_path(self):
-        if self.url:
-            return self.url
-        else:
-            return self.path
-
-    def visit(self, driver: Driver):
-        self.pre_visit(driver)
-        driver.get(self.url)
-        self.post_visit(driver)
-
-    def get_info(self):
-        info = {}
-        if self.url:
-            info["url"] = self.url
-        if self.name:
-            info["name"] = self.name
-        return info
-
-    @abstractmethod
-    def pre_visit(self, driver: Driver):
-        raise NotImplementedError("pre_action not implemented")
-
-    @abstractmethod
-    def post_visit(self, driver: Driver):
-        raise NotImplementedError("post_action not implemented")
-
-
-class DefaultWebsite(Website):
-    def __init__(self, url, name=None):
-        super().__init__(url, name)
-
-    def pre_visit(self, driver: Driver):
-        pass
-
-    def post_visit(self, driver: Driver):
-        time.sleep(5)  # Wait for 5 seconds to allow adblock to finish
-
-
-class HumanScreenShotWebsite(Website):
-    def __init__(self, name=None, path=None):
-        super().__init__(name=name, path=path)
-
-    def pre_visit(self, driver: Driver):
-        pass
-
-    def post_visit(self, driver: Driver):
-        pass
diff --git a/tools/live_bench/websites/website_list.yaml b/tools/live_bench/websites/website_list.yaml
deleted file mode 100644
index 4c7b348a..00000000
--- a/tools/live_bench/websites/website_list.yaml
+++ /dev/null
@@ -1,77 +0,0 @@
-websites:
-- url: https://www.bbc.com/
-  # can add below line to specify the class to use for this website
-  # website_class: !constructor website.DefaultWebsite
-  # can add args tag to specify the arguments to pass to the class constructor
-  # args:
-  #  arg1: value1
-  #  arg2: value2
-- url: https://www.bbc.com/news
-- url: https://www.bbc.com/sport
-- url: https://www.bbc.com/business
-- url: https://www.bbc.com/innovation
-- url: https://www.bbc.com/culture
-- url: https://www.bbc.com/travel
-- url: https://www.bbc.com/future-planet
-# - url: https://edition.cnn.com/
-# - url: https://edition.cnn.com/politics
-# - url: https://edition.cnn.com/entertainment
-# - url: https://edition.cnn.com/style
-# - url: https://www.bloomberg.com/economics
-# - url: https://www.bloomberg.com/industries
-# - url: https://www.bloomberg.com/technology
-# - url: https://www.bloomberg.com/politics
-# - url: https://www.bloomberg.com/opinion
-# - url: https://www.wsj.com/
-# - url: https://www.wsj.com/world/africa?mod=nav_top_subsection
-# - url: https://www.wsj.com/world/americas?mod=nav_top_subsection
-# - url: https://www.wsj.com/world/asia?mod=nav_top_subsection
-# - url: https://www.wsj.com/world/china?mod=nav_top_subsection
-# - url: https://www.wsj.com/world/europe?mod=nav_top_subsection
-# - url: https://www.wsj.com/world/middle-east?mod=nav_top_subsection
-# - url: https://www.wsj.com/world/india?mod=nav_top_subsection
-# - url: https://www.wsj.com/world/oceania?mod=nav_top_subsection
-# - url: https://www.wsj.com/world/russia?mod=nav_top_subsection
-# - url: https://www.wsj.com/world/uk?mod=nav_top_subsection
-# - url: https://www.wsj.com/science?mod=nav_top_subsection
-# - url: https://www.wsj.com/science/archaeology?mod=nav_top_subsection
-# - url: https://www.wsj.com/science/biology?mod=nav_top_subsection
-# - url: https://www.wsj.com/science/environment?mod=nav_top_subsection
-# - url: https://www.wsj.com/science/physics?mod=nav_top_subsection
-# - url: https://www.wsj.com/science/space-astronomy?mod=nav_top_subsection
-# - url: https://www.wsj.com/economy/central-banking?mod=nav_top_subsection
-# - url: https://www.wsj.com/economy/consumers?mod=nav_top_subsection
-# - url: https://www.wsj.com/economy/housing?mod=nav_top_subsection
-# - url: https://www.wsj.com/economy/jobs?mod=nav_top_subsection
-# - url: https://www.wsj.com/economy/trade?mod=nav_top_subsection
-# - url: https://www.wsj.com/economy/global
-# - url: https://www.wsj.com/tech/ai?mod=nav_top_subsection
-# - url: https://www.wsj.com/tech/biotech
-# - url: https://www.wsj.com/tech/cybersecurity?mod=nav_top_subsection
-# - url: https://www.wsj.com/tech/personal-tech?mod=nav_top_subsection
-# - url: https://www.reuters.com/
-# - url: https://www.reuters.com/business/aerospace-defense/
-# - url: https://www.reuters.com/business/autos-transportation/
-# - url: https://www.reuters.com/business/davos/
-# - url: https://www.reuters.com/business/energy/
-# - url: https://www.reuters.com/business/environment/
-# - url: https://www.reuters.com/business/finance/
-# - url: https://www.reuters.com/business/healthcare-pharmaceuticals/
-# - url: https://www.reuters.com/business/media-telecom/
-# - url: https://www.reuters.com/business/retail-consumer/
-# - url: https://www.reuters.com/business/future-of-health/
-# - url: https://www.reuters.com/business/future-of-money/
-# - url: https://www.reuters.com/business/take-five/
-# - url: https://www.reuters.com/business/world-at-work/
-# - url: https://www.reuters.com/breakingviews/
-# - url: https://www.reuters.com/technology/
-# - url: https://www.reuters.com/technology/cybersecurity/
-# - url: https://www.reuters.com/technology/space/
-# - url: https://www.reuters.com/technology/disrupted/
-# - url: https://www.reuters.com/technology/reuters-momentum/
-# - url: https://www.reuters.com/investigations/
-# - url: https://a16z.com/news-content/#latest
-# - url: https://news.ycombinator.com/
-# - url: https://www.reddit.com/?rdt=48006
-# - url: https://news.crunchbase.com/
-- url: https://www.cctv.com/

From 2c8c63a273513eba19ba712ffb0888d75f201a2a Mon Sep 17 00:00:00 2001
From: kcz358 <kaichenzhang358@outlook.com>
Date: Wed, 19 Jun 2024 11:08:25 +0000
Subject: [PATCH 06/32] Add back llava onevision

---
 lmms_eval/models/__init__.py        |   1 +
 lmms_eval/models/llava_onevision.py | 484 ++++++++++++++++++++++++++++
 2 files changed, 485 insertions(+)
 create mode 100755 lmms_eval/models/llava_onevision.py

diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py
index ec354cf3..9b19447d 100755
--- a/lmms_eval/models/__init__.py
+++ b/lmms_eval/models/__init__.py
@@ -29,6 +29,7 @@
     "mplug_owl_video": "mplug_Owl",
     "phi3v": "Phi3v",
     "tinyllava": "TinyLlava",
+    "llava_onevision": "Llava_OneVision",
 }
 
 for model_name, model_class in AVAILABLE_MODELS.items():
diff --git a/lmms_eval/models/llava_onevision.py b/lmms_eval/models/llava_onevision.py
new file mode 100755
index 00000000..a755b0c9
--- /dev/null
+++ b/lmms_eval/models/llava_onevision.py
@@ -0,0 +1,484 @@
+from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
+from accelerate.state import AcceleratorState
+from transformers import AutoConfig
+
+import math
+import torch
+
+torch.backends.cuda.matmul.allow_tf32 = True
+
+from tqdm import tqdm
+from datetime import timedelta
+from decord import VideoReader, cpu
+import numpy as np
+
+import copy
+import PIL
+from typing import List, Optional, Union, Tuple
+from packaging import version
+import warnings
+import logging
+
+warnings.filterwarnings("ignore")
+
+eval_logger = logging.getLogger("lmms-eval")
+
+from lmms_eval import utils
+from lmms_eval.api.instance import Instance
+from lmms_eval.api.model import lmms
+from lmms_eval.api.registry import register_model
+from lmms_eval.models.model_utils.load_video import read_video_pyav
+
+try:
+    from llava.model.builder import load_pretrained_model
+    from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token, KeywordsStoppingCriteria
+    from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+    from llava.conversation import conv_templates, SeparatorStyle
+
+except Exception as e:
+    eval_logger.debug("LLaVA is not installed. Please install LLaVA to use this model.\nError: %s" % e)
+
+try:
+    from llavavid.model.language_model.llava_qwen import LlavaQwenConfig
+    from llavavid.model.language_model.llava_llama import LlavaConfig
+
+    AutoConfig.register("llava_qwen", LlavaQwenConfig)
+    AutoConfig.register("llava_llama", LlavaConfig)
+except Exception as e:
+    eval_logger.debug("")
+# inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5
+# if is_flash_attn_2_available:
+#     best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating
+
+if version.parse(torch.__version__) >= version.parse("2.1.2"):
+    best_fit_attn_implementation = "sdpa"
+else:
+    best_fit_attn_implementation = "eager"
+
+
+@register_model("llava_onevision")
+class Llava_OneVision(lmms):
+    """
+    Llava Model
+    """
+
+    def __init__(
+        self,
+        pretrained: str = "liuhaotian/llava-v1.5-7b",
+        truncation: Optional[bool] = True,
+        device: Optional[str] = "cuda:0",
+        batch_size: Optional[Union[int, str]] = 1,
+        model_name: Optional[str] = None,
+        attn_implementation: Optional[str] = best_fit_attn_implementation,
+        device_map: Optional[str] = "cuda:0",
+        conv_template: Optional[str] = "vicuna_v1",
+        use_cache: Optional[bool] = True,
+        truncate_context: Optional[bool] = False,  # whether to truncate the context in generation, set it False for LLaVA-1.6
+        customized_config: Optional[str] = None,  # ends in json
+        max_frames_num: Optional[int] = 32,
+        mm_spatial_pool_stride: Optional[int] = 2,
+        mm_spatial_pool_mode: Optional[str] = "average",
+        token_strategy: Optional[str] = "single",  # could be "single" or "multiple", "multiple" denotes adding multiple <image> tokens for each frame
+        video_decode_backend: str = "pyav",
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        # Do not use kwargs for now
+        assert kwargs == {}, f"Unexpected kwargs: {kwargs}"
+
+        accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52))
+        accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs])
+        if accelerator.num_processes > 1:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            self._device = torch.device(device)
+            self.device_map = device_map
+        else:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+
+        llava_model_args = {
+            "multimodal": True,
+        }
+        if customized_config is not None:
+            llava_model_args["customized_config"] = customized_config
+        if attn_implementation is not None:
+            llava_model_args["attn_implementation"] = attn_implementation
+        if "use_flash_attention_2" in kwargs:
+            llava_model_args["use_flash_attention_2"] = kwargs["use_flash_attention_2"]
+        model_name = model_name if model_name is not None else get_model_name_from_path(pretrained)
+
+        self.pretrained = pretrained
+        self.token_strategy = token_strategy
+        self.max_frames_num = max_frames_num
+        self.mm_spatial_pool_stride = mm_spatial_pool_stride
+        self.mm_spatial_pool_mode = mm_spatial_pool_mode
+        self.video_decode_backend = video_decode_backend
+
+        overwrite_config = {}
+        overwrite_config["mm_spatial_pool_stride"] = self.mm_spatial_pool_stride
+        overwrite_config["mm_spatial_pool_mode"] = self.mm_spatial_pool_mode
+        cfg_pretrained = AutoConfig.from_pretrained(self.pretrained)
+
+        if cfg_pretrained.architectures[0] == "LlavaLlamaForCausalLM":  # Ugly code, only used in  vicuna that needs ROPE
+            if "224" in cfg_pretrained.mm_vision_tower:
+                least_token_number = self.max_frames_num * (16 // self.mm_spatial_pool_stride) ** 2 + 1000
+            else:
+                least_token_number = self.max_frames_num * (24 // self.mm_spatial_pool_stride) ** 2 + 1000
+
+            scaling_factor = math.ceil(least_token_number / 4096)
+            if scaling_factor >= 2:
+                overwrite_config["rope_scaling"] = {"factor": float(scaling_factor), "type": "linear"}
+                overwrite_config["max_sequence_length"] = 4096 * scaling_factor
+                overwrite_config["tokenizer_model_max_length"] = 4096 * scaling_factor
+
+        llava_model_args["overwrite_config"] = overwrite_config
+        try:
+            # Try to load the model with the multimodal argument
+            self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args)
+        except TypeError:
+            # for older versions of LLaVA that don't have multimodal argument
+            llava_model_args.pop("multimodal", None)
+            self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args)
+
+        self._config = self._model.config
+        self.model.eval()
+        self.model.tie_weights()
+        self.truncation = truncation
+        self.batch_size_per_gpu = int(batch_size)
+        self.conv_template = conv_template
+        self.use_cache = use_cache
+        self.truncate_context = truncate_context
+        assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue."
+
+        if accelerator.num_processes > 1:
+            assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
+            # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
+            # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works
+            # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work.
+            if accelerator.distributed_type == DistributedType.DEEPSPEED:
+                kwargs = {
+                    "train_micro_batch_size_per_gpu": self.batch_size_per_gpu,
+                    "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes,
+                }
+                AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs)
+                eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0")
+
+            if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED:
+                self._model = accelerator.prepare(self.model)
+            else:
+                self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
+            self.accelerator = accelerator
+            if self.accelerator.is_local_main_process:
+                eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
+            self._rank = self.accelerator.local_process_index
+            self._world_size = self.accelerator.num_processes
+
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism")
+            self._rank = 0
+            self._word_size = 1
+
+        else:
+            eval_logger.info(f"Using single device: {self._device}")
+            self.model.to(self._device)
+            self._rank = 0
+            self._world_size = 1
+
+    @property
+    def config(self):
+        # return the associated transformers.AutoConfig for the given pretrained model.
+        return self._config
+
+    @property
+    def tokenizer(self):
+        return self._tokenizer
+
+    @property
+    def model(self):
+        # returns the model, unwrapping it if using Accelerate
+        if hasattr(self, "accelerator"):
+            return self.accelerator.unwrap_model(self._model)
+        else:
+            return self._model
+
+    @property
+    def eot_token_id(self):
+        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+        return self.tokenizer.eos_token_id
+
+    @property
+    def max_length(self):
+        return self._max_length
+
+    def pad_sequence(self, input_ids, batch_first, padding_value):
+        if self.tokenizer.padding_side == "left":
+            input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids]
+        input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value)
+        if self.tokenizer.padding_side == "left":
+            input_ids = torch.flip(input_ids, [1])
+        return input_ids
+
+    @property
+    def batch_size(self):
+        return self.batch_size_per_gpu
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def rank(self):
+        return self._rank
+
+    @property
+    def world_size(self):
+        return self._world_size
+
+    def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]:
+        """ """
+        add_special_tokens = False if add_special_tokens is None else add_special_tokens
+        encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
+        # left-truncate the encoded context to be at most `left_truncate_len` tokens long
+        if left_truncate_len:
+            encoding = encoding[-left_truncate_len:]
+        return encoding
+
+    def tok_decode(self, tokens):
+        try:
+            return self.tokenizer.decode(tokens)
+        except:
+            return self.tokenizer.decode([tokens])
+
+    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        # TODO
+        res = []
+        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
+
+        for contexts, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
+            # encode, pad, and truncate contexts for this batch
+            if type(doc_to_target) == str:
+                continuation = doc_to_target
+            else:
+                continuation = doc_to_target(self.task_dict[task][split][doc_id])
+            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
+            visuals = self.flatten(visuals)
+            image_sizes = [[visual.size[0], visual.size[1]] for visual in visuals]
+            if visuals:
+                image = process_images(visuals, self._image_processor, self._config)
+                if type(image) is list:
+                    image = [_image.to(dtype=torch.float16, device=self.device) for _image in image]
+                else:
+                    image = image.to(dtype=torch.float16, device=self.device)
+            else:
+                image = None
+
+            prompts_input = contexts[0] if isinstance(contexts, list) else contexts
+
+            if image is not None and len(image) != 0 and DEFAULT_IMAGE_TOKEN not in prompts_input:
+                """
+                Three senarios:
+                1. No image, and there for, no image token should be added.
+                2. image token is already specified in the context, so we don't need to add it.
+                3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line.
+                """
+                image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals)
+                image_tokens = " ".join(image_tokens)
+                prompts_input = image_tokens + "\n" + (contexts[0] if isinstance(contexts, list) else contexts)
+
+            # This is much safer for llama3, as we now have some object type in it
+            if "llama_3" in self.conv_template:
+                conv = copy.deepcopy(conv_templates[self.conv_template])
+            else:
+                conv = conv_templates[self.conv_template].copy()
+
+            conv.append_message(conv.roles[0], prompts_input)
+            conv.append_message(conv.roles[1], None)
+            prompt = conv.get_prompt()
+            pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
+            contxt_id = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device)
+            # Add the answer of the second role
+            conv.messages[1][1] = continuation
+
+            prompt = conv.get_prompt()
+            input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device)
+            labels = input_ids.clone()
+            # Context part no need to calculate for loss
+            labels[0, : contxt_id.shape[1]] = -100
+            with torch.inference_mode():
+                outputs = self.model(input_ids=input_ids, labels=labels, images=image, use_cache=True, image_sizes=image_sizes)
+            loss = outputs["loss"]
+            # loss = torch.exp(loss)
+            logits = outputs["logits"]
+            greedy_tokens = logits.argmax(dim=-1)
+            cont_toks = input_ids[:, contxt_id.shape[1] :]  # [1, seq]
+            greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : input_ids.shape[1]]  # [1, seq]
+            max_equal = (greedy_tokens == cont_toks).all()
+            res.append((float(loss.item()), bool(max_equal)))
+            pbar.update(1)
+
+        pbar.close()
+        return res
+
+    def flatten(self, input):
+        new_list = []
+        for i in input:
+            for j in i:
+                new_list.append(j)
+        return new_list
+
+    def load_video(self, video_path, max_frames_num):
+        if type(video_path) == str:
+            vr = VideoReader(video_path, ctx=cpu(0))
+        else:
+            vr = VideoReader(video_path[0], ctx=cpu(0))
+        total_frame_num = len(vr)
+        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int)
+        frame_idx = uniform_sampled_frames.tolist()
+        spare_frames = vr.get_batch(frame_idx).asnumpy()
+        return spare_frames  # (frames, height, width, channels)
+
+    def generate_until(self, requests: List[Instance]) -> List[str]:
+        res = []
+
+        def _collate(x):
+            # the negative sign on len(toks) sorts descending - this has a few advantages:
+            # - time estimates will always be over not underestimates, which is more useful for planning
+            # - to know the size of a batch when going through the list, you know the first one is always the batch
+            #   padded context length. this is useful to simplify the batching logic and more importantly to make
+            #   automatic adaptive batches much much easier to implement
+            # - any OOMs will happen right away rather than near the end
+            toks = self.tok_encode(x[0])
+            return -len(toks), x[0]
+
+        # we group requests by their generation_kwargs,
+        # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling
+        # in the same batch.
+        re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True)
+        chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None)
+        num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1
+        pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding")
+        for chunk in chunks:
+            batched_contexts, all_gen_kwargs, batched_doc_to_visual, batched_doc_id, batched_task, batched_split = zip(*chunk)
+            task = batched_task[0]
+            split = batched_split[0]
+            batched_visuals = [batched_doc_to_visual[0](self.task_dict[task][split][ids]) for ids in batched_doc_id]  # [B, N]
+            assert len(batched_visuals) == 1
+
+            # we assume all gen kwargs in the batch are the same
+            # this is safe to assume because the `grouper` object ensures it.
+            gen_kwargs = all_gen_kwargs[0]
+            if "until" in gen_kwargs:
+                gen_kwargs.pop("until")
+
+            question_input = []
+
+            for visual, context in zip(batched_visuals, batched_contexts):
+                if "image_aspect_ratio" in gen_kwargs.keys() and "image_aspect_ratio" not in self._config.__dict__:
+                    # here we should pop it out of gen_kwargs so that it doesn't get passed to the model for next step of generation
+                    self._config.image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio")
+                    eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}")
+
+                # encode, pad, and truncate contexts for this batch
+                if type(visual[0]) == PIL.Image.Image:  # For image task
+                    image_tensor = process_images(visual, self._image_processor, self._config)
+                    if type(image_tensor) is list:
+                        image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor]
+                    else:
+                        image_tensor = image_tensor.to(dtype=torch.float16, device=self.device)
+
+                    task_type = "image"
+
+                elif type(visual[0]) == str:  # For video task
+                    image_tensor = []
+                    try:
+                        if self.video_decode_backend == "decord":
+                            frames = self.load_video(visual, self.max_frames_num)
+                        elif self.video_decode_backend == "pyav":
+                            frames = read_video_pyav(visual[0], num_frm=self.max_frames_num)
+                        frames = self._image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].half().cuda()
+                        image_tensor.append(frames)
+                    except Exception as e:
+                        eval_logger.error(f"Error {e} in loading video")
+                        image_tensor = None
+
+                    task_type = "video"
+
+                if image_tensor is not None and len(image_tensor) != 0 and DEFAULT_IMAGE_TOKEN not in context:
+                    """
+                    Three senarios:
+                    1. No image, and there for, no image token should be added.
+                    2. image token is already specified in the context, so we don't need to add it.
+                    3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line.
+                    4. For video tasks, we could add a <image> token or multiple <image> tokens for each frame in the context. This depends on the training strategy and should balance in test to decide which is better
+                    """
+                    if task_type == "image":
+                        image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visual) if isinstance(visual, list) else [DEFAULT_IMAGE_TOKEN]
+                    elif task_type == "video":
+                        image_tokens = [DEFAULT_IMAGE_TOKEN] * len(frames) if self.token_strategy == "multiple" else [DEFAULT_IMAGE_TOKEN]
+
+                    image_tokens = " ".join(image_tokens)
+                    question = image_tokens + "\n" + context
+                else:
+                    question = context
+
+                # This is much safer for llama3, as we now have some object type in it
+                if "llama_3" in self.conv_template:
+                    conv = copy.deepcopy(conv_templates[self.conv_template])
+                else:
+                    conv = conv_templates[self.conv_template].copy()
+                conv.append_message(conv.roles[0], question)
+                conv.append_message(conv.roles[1], None)
+                prompt_question = conv.get_prompt()
+                question_input.append(prompt_question)
+
+            # preconfigure gen_kwargs with defaults
+            if "max_new_tokens" not in gen_kwargs:
+                gen_kwargs["max_new_tokens"] = 1024
+            if "temperature" not in gen_kwargs:
+                gen_kwargs["temperature"] = 0
+            if "do_sample" not in gen_kwargs:
+                gen_kwargs["do_sample"] = False
+            if "top_p" not in gen_kwargs:
+                gen_kwargs["top_p"] = None
+            if "num_beams" not in gen_kwargs:
+                gen_kwargs["num_beams"] = 1
+
+            input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input]
+            pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
+            input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device)
+            attention_masks = input_ids.ne(pad_token_ids).to(self.device)
+
+            if task_type == "image":
+                gen_kwargs["image_sizes"] = [batched_visuals[idx][0].size for idx in range(len(batched_visuals))]
+            elif task_type == "video":
+                stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+                keywords = [stop_str]
+                stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids)
+                gen_kwargs["modalities"] = ["video"]
+                gen_kwargs["stopping_criteria"] = [stopping_criteria]
+                self._config.mm_spatial_pool_stride = self.mm_spatial_pool_stride
+                self._config.mm_spatial_pool_mode = self.mm_spatial_pool_mode
+
+            # These steps are not in LLaVA's original code, but are necessary for generation to work
+            # TODO: attention to this major generation step...
+            if "image_aspect_ratio" in gen_kwargs.keys():
+                gen_kwargs.pop("image_aspect_ratio") 
+            try:
+                with torch.inference_mode():
+                    cont = self.model.generate(input_ids, attention_mask=attention_masks, pad_token_id=pad_token_ids, images=image_tensor, use_cache=self.use_cache, **gen_kwargs)
+
+                text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True)
+            except Exception as e:
+                raise e
+
+            text_outputs = [response.strip() for response in text_outputs]
+            res.extend(text_outputs)
+            self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs)
+            pbar.update(1)
+            # reorder this group of results back to original unsorted form
+        res = re_ords.get_original(res)
+
+        pbar.close()
+        return res

From e6055890b8433591a3d468c935a6fc09ef4e7f7f Mon Sep 17 00:00:00 2001
From: choiszt <ls2001927@sohu.com>
Date: Thu, 20 Jun 2024 15:14:21 +0800
Subject: [PATCH 07/32] update ablation for videomme datasets

---
 lmms_eval/tasks/videomme/utils.py             | 103 ++++++++++++++++--
 lmms_eval/tasks/videomme/videomme.yaml        |   0
 .../tasks/videomme/videomme_w_subtitle.yaml   |  44 ++++++++
 3 files changed, 135 insertions(+), 12 deletions(-)
 mode change 100755 => 100644 lmms_eval/tasks/videomme/utils.py
 mode change 100755 => 100644 lmms_eval/tasks/videomme/videomme.yaml
 create mode 100644 lmms_eval/tasks/videomme/videomme_w_subtitle.yaml

diff --git a/lmms_eval/tasks/videomme/utils.py b/lmms_eval/tasks/videomme/utils.py
old mode 100755
new mode 100644
index 4cd26715..ca821601
--- a/lmms_eval/tasks/videomme/utils.py
+++ b/lmms_eval/tasks/videomme/utils.py
@@ -10,6 +10,8 @@
 import sys
 from typing import List, Dict, Optional, Union
 import re
+import cv2
+import numpy as np
 
 eval_logger = logging.getLogger("lmms-eval")
 
@@ -80,17 +82,55 @@
 # cache_dir = os.path.join(hf_home, cache_dir)
 # base_cache_dir = config["dataset_kwargs"]["cache_dir"]
 base_cache_dir = os.path.expanduser(hf_home)
-
+with open(Path(__file__).parent / "videomme.yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"]
+
+
+def parse_subtitle_time(time_str):
+    h, m, s_ms = time_str.split(':')
+    s, ms = s_ms.split(',')
+    return int(h) * 3600 + int(m) * 60 + int(s) + int(ms) / 1000
+
+def load_subtitles(subtitle_path):
+    subtitles = {}
+    with open(subtitle_path, 'r', encoding='utf-8') as file:
+        content = file.read().split('\n\n')
+        for section in content:
+            if section.strip():
+                lines = section.split('\n')
+                if len(lines) >= 3:
+                    time_range = lines[1].split(' --> ')
+                    start_time = parse_subtitle_time(time_range[0])
+                    end_time = parse_subtitle_time(time_range[1])
+                    text = ' '.join(line for line in lines[2:])
+                    subtitles[(start_time, end_time)] = text
+    return subtitles
+
+def convert_time_to_frame(time_in_seconds, fps):
+    return int(time_in_seconds * fps)
+
+def extract_subtitles(video_path, subtitle_path):
+    video = cv2.VideoCapture(video_path)
+    fps = video.get(cv2.CAP_PROP_FPS)
+    total_frame=int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    subtitles = load_subtitles(subtitle_path)
+    
+    subtitle_frames = []
+    for (start_time, end_time), text in subtitles.items():
+        start_frame = convert_time_to_frame(start_time, fps)
+        end_frame = convert_time_to_frame(end_time, fps)
+        subtitle_frames.append((start_frame, end_frame, text))
+
+    return subtitle_frames,total_frame
 
 def videomme_doc_to_visual(doc):
-    with open(Path(__file__).parent / "videomme.yaml", "r") as f:
-        raw_data = f.readlines()
-        safe_data = []
-        for i, line in enumerate(raw_data):
-            # remove function definition since yaml load cannot handle it
-            if "!function" not in line:
-                safe_data.append(line)
-    cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"]
+
     cache_dir = os.path.join(base_cache_dir, cache_name)
     video_path = doc["videoID"] + ".mp4"
     video_path = os.path.join(cache_dir, video_path)
@@ -106,7 +146,6 @@ def videomme_doc_to_visual(doc):
 
 
 def videomme_doc_to_text(doc, model_specific_prompt_kwargs=None):
-
     option_prompt="Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option."
     question = doc["question"]
     option = str(doc["options"])
@@ -126,11 +165,51 @@ def videomme_doc_to_text(doc, model_specific_prompt_kwargs=None):
 # The best answer is:
 
 def videomme_doc_to_text_subtitle(doc, model_specific_prompt_kwargs=None):
+    cache_dir = os.path.join(base_cache_dir, cache_name)
+    video_path = doc["videoID"] + ".mp4"
+    subtitle_path=os.path.join(cache_dir,"subtitle",doc["videoID"]+".srt")
+    video_path = os.path.join(cache_dir, video_path)
+    if os.path.exists(subtitle_path): #Denote have subtitle
+        subtitle=open(subtitle_path).readlines()
+    else:
+        subtitle=""
     subtitles_prompt="This video's subtitles are listed below: \n"
-    if doc["Subtitle"]=="":
+    if subtitle=="":
         subtitle="No subtitles available"
     else:
-        subtitle=doc["Subtitle"]
+        if "gemini_api_flag" in model_specific_prompt_kwargs: #specific for gemini_api
+            if model_specific_prompt_kwargs['gemini_api_flag']=="full subtitle":
+                textlist=[]
+                for ele in subtitle:
+                    pattern = r'<font color="white" size=".72c">(.*?)</font>'
+                    matches = re.findall(pattern, ele)
+                    if matches:
+                        textlist.append(matches[0])
+                subtitle_text="\n".join(textlist)
+        else:
+            if "frame_num" in model_specific_prompt_kwargs:
+                frame_num=model_specific_prompt_kwargs['frame_num']
+                subtitle_by_frame,total_frame=extract_subtitles(video_path,subtitle_path)
+                uniform_sampled_frames = np.linspace(0, total_frame - 1, frame_num, dtype=int).tolist()
+                
+                subtitle_by_frame_idx=[]
+                for frame_idx in uniform_sampled_frames:
+                    for idx,title in enumerate(subtitle_by_frame):
+                        if frame_idx<title[1] and frame_idx>=title[0]:
+                            subtitle_by_frame_idx.append(idx)
+                subtitle_by_frame_idx=list(set(subtitle_by_frame_idx))
+
+                textlist=[]
+                for idx in subtitle_by_frame_idx:
+                    pattern = r'<font color="white" size=".72c">(.*?)</font>'
+                    raw_text=re.findall(pattern, subtitle_by_frame[idx][2])
+                    try:
+                        textlist.append(raw_text[0])
+                    except:
+                        continue
+                subtitle_text="\n".join(textlist)
+        subtitle=subtitle_text
+
     option_prompt="Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option."
     question = doc["question"]
     option = str(doc["options"])
diff --git a/lmms_eval/tasks/videomme/videomme.yaml b/lmms_eval/tasks/videomme/videomme.yaml
old mode 100755
new mode 100644
diff --git a/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml
new file mode 100644
index 00000000..ac34cdf4
--- /dev/null
+++ b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml
@@ -0,0 +1,44 @@
+dataset_path: lmms-lab/Video-MME
+dataset_kwargs:
+  token: True
+  cache_dir: videomme
+  video: True
+  # From_YouTube: True
+task: videomme_w_subtitle
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.videomme_doc_to_visual
+doc_to_text: !function utils.videomme_doc_to_text_subtitle
+doc_to_target: "answer"
+generation_kwargs:
+  max_new_tokens: 16
+  temperature: 0
+  top_p: 1.0
+  num_beams: 1
+  do_sample: false
+# The return value of process_results will be used by metrics
+process_results: !function utils.videomme_process_results
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: videomme_percetion_score
+    aggregation: !function utils.videomme_aggregate_results
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    frame_num: 32
+  gemini_api:
+    gemini_api_flag: "full subtitle"
+  # gpt4v:
+  #   pre_prompt: ""
+  #   post_prompt: 
+  # # qwen_vl:  
+  # #   pre_prompt: ""
+  # #   post_prompt: " Answer:"
+  # # otterhd:
+  # #   pre_prompt: ""
+  # #   post_prompt: " Answer:"
+  # xcomposer2_4khd:
+  #   pre_prompt: "[UNUSED_TOKEN_146]user\n"
+  #   post_prompt: " Answer this question with A, B, C, or D.[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
+metadata:
+  - version: 0.0

From 1d282ca66e52c65f3cae95c5a25023b487cd52ac Mon Sep 17 00:00:00 2001
From: Shuai Liu <76559926+choiszt@users.noreply.github.com>
Date: Thu, 20 Jun 2024 20:38:39 +0800
Subject: [PATCH 08/32] Delete lmms_eval/tasks/videomme/videomme_subtitle.yaml

---
 .../tasks/videomme/videomme_subtitle.yaml     | 43 -------------------
 1 file changed, 43 deletions(-)
 delete mode 100644 lmms_eval/tasks/videomme/videomme_subtitle.yaml

diff --git a/lmms_eval/tasks/videomme/videomme_subtitle.yaml b/lmms_eval/tasks/videomme/videomme_subtitle.yaml
deleted file mode 100644
index 04125766..00000000
--- a/lmms_eval/tasks/videomme/videomme_subtitle.yaml
+++ /dev/null
@@ -1,43 +0,0 @@
-dataset_path: lmms-lab/Video-MME
-dataset_kwargs:
-  token: True
-  cache_dir: videomme
-  video: True
-  # From_YouTube: True
-task: videomme_subtitle
-test_split: test
-output_type: generate_until
-doc_to_visual: !function utils.videomme_doc_to_visual
-doc_to_text: !function utils.videomme_doc_to_text_subtitle
-doc_to_target: "answer"
-generation_kwargs:
-  max_new_tokens: 16
-  temperature: 0
-  top_p: 1.0
-  num_beams: 1
-  do_sample: false
-# The return value of process_results will be used by metrics
-process_results: !function utils.videomme_process_results
-# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
-metric_list:
-  - metric: videomme_percetion_score
-    aggregation: !function utils.videomme_aggregate_results
-    higher_is_better: true
-model_specific_prompt_kwargs:
-  default:
-    pre_prompt: ""
-    post_prompt: ""
-  # gpt4v:
-  #   pre_prompt: ""
-  #   post_prompt: 
-  # # qwen_vl:  
-  # #   pre_prompt: ""
-  # #   post_prompt: " Answer:"
-  # # otterhd:
-  # #   pre_prompt: ""
-  # #   post_prompt: " Answer:"
-  # xcomposer2_4khd:
-  #   pre_prompt: "[UNUSED_TOKEN_146]user\n"
-  #   post_prompt: " Answer this question with A, B, C, or D.[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
-metadata:
-  - version: 0.0

From 63532aafc208ec62d0cdbcf8fdf56582cef68eeb Mon Sep 17 00:00:00 2001
From: Bo Li <drluodian@gmail.com>
Date: Sun, 23 Jun 2024 06:24:02 +0000
Subject: [PATCH 09/32] Squashed commit of the following:
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

commit 8f9d620fcd9d0a0742ee6bcf51ea63bd6b088a36
Author: Li Bo <drluodian@gmail.com>
Date:   Sun Jun 23 14:02:25 2024 +0800

    Update pyproject.toml

commit 6341b7c15ce9fb28eb06b067ddb299d6cf2e16c3
Merge: fce85f1b 903b042b
Author: Li Bo <drluodian@gmail.com>
Date:   Sun Jun 23 14:02:02 2024 +0800

    Merge pull request #125 from EvolvingLMMs-Lab/dev/interleave

    [Model] aligned llava-interleave model results on video tasks

commit 903b042be016016d4ebeecb07701f3076a2d323c
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Sat Jun 22 12:07:13 2024 +0000

    Remove unnecessary lines for video llava

commit d78ec86407b729a964906a8c2e50704b4bc74d06
Merge: ebe7217a fce85f1b
Author: Li Bo <drluodian@gmail.com>
Date:   Sat Jun 22 13:57:31 2024 +0800

    Merge branch 'main' into dev/interleave

commit ebe7217a486c1e754e42c2cbdb834e09fbbcc9b0
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Sat Jun 22 02:57:08 2024 +0000

    Delete unnecessary lines

commit 120c474b056f9177c74e1fd9691d59e2f234b785
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Fri Jun 21 08:38:41 2024 +0000

    Revise model registry for llava_hf and longva

commit 7d6201f921088afd3f52a35076e3c6fcc9aa518c
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Fri Jun 21 08:38:24 2024 +0000

    Add longva

commit 12f480699c71a12a24d4349d9b0681933201a3a6
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Fri Jun 21 08:35:39 2024 +0000

    Remove unnecessary lines since use batched visuals now in llava

commit 12cea76f1f0f14b1fd1007c9d39a9b0557368637
Author: Bo Li <drluodian@gmail.com>
Date:   Thu Jun 20 18:15:32 2024 +0000

    chore: Add loguru for logging in lmms_eval package

commit 8ef24740dd48a11c97eb627f2fff4aca107fef0d
Author: Bo Li <drluodian@gmail.com>
Date:   Thu Jun 20 12:11:03 2024 +0000

    chore: Remove unused models from lmms_eval package

commit af38885fc2e066f5ea44388f33e07176f836fe28
Author: Bo Li <drluodian@gmail.com>
Date:   Thu Jun 20 12:07:09 2024 +0000

    chore: Handle ImportError when importing models

    Handle the ImportError exception when importing models in the lmms_eval package. This change adds a try-except block to catch the ImportError and print an error message indicating the failed import. This will help with troubleshooting and identifying any issues with the model imports.

commit fce85f1b03ff7043b29dee787c5d17a08dd2687a
Merge: dbe63293 d94f83cb
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 20 20:02:12 2024 +0800

    Merge pull request #120 from EvolvingLMMs-Lab/pufanyi/hf_dataset_docs

    Add docs for datasets upload to HF

commit dbe63293245a5141fdfd80bda7657c304f6bd32f
Author: choiszt <ls2001927@sohu.com>
Date:   Thu Jun 20 15:14:21 2024 +0800

    update ablation for videomme datasets

commit d94f83cb3f08b61a2c75cc4326e58792100605b3
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 20 13:30:59 2024 +0800

    Update README.md

commit cab8159ff35db330536c0b6dfb4b0a3b24142209
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 20 13:30:29 2024 +0800

    Update README.md

commit 45876652a877a8006b828f32f5cc4660629f9190
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu Jun 20 03:55:30 2024 +0000

    Add llava_hf back to registry

commit 3463651b8c54d36cd94169e3d376f5ed225a195a
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu Jun 20 03:54:33 2024 +0000

    Remove handling non-visual loop in llava

commit cb0d3f49b72790b081f981e0e6147131542f7f68
Author: Fanyi Pu <FPU001@e.ntu.edu.sg>
Date:   Thu Jun 20 02:11:18 2024 +0800

    update readme

commit 813877bfe5ac590cdbe92dd74d18f83a2091f748
Author: Fanyi Pu <FPU001@e.ntu.edu.sg>
Date:   Wed Jun 19 15:37:52 2024 +0800

    to sh script

commit a14684b8557d5894976448a5c559ed7a66a6cf16
Author: Fanyi Pu <FPU001@e.ntu.edu.sg>
Date:   Wed Jun 19 15:37:04 2024 +0800

    lint

commit d0f8851d42ba31f5da2a7a65e91499db45174dbc
Author: Fanyi Pu <FPU001@e.ntu.edu.sg>
Date:   Wed Jun 19 15:36:48 2024 +0800

    small fix

commit 63748e9718f287ad433afc90e340b5e17a89c1ed
Author: Fanyi Pu <FPU001@e.ntu.edu.sg>
Date:   Wed Jun 19 15:36:43 2024 +0800

    small fix

commit 7f1159a1fe04cfb783dc31d4fbdef3bda0ce19e4
Author: Fanyi Pu <FPU001@e.ntu.edu.sg>
Date:   Wed Jun 19 15:35:05 2024 +0800

    update preparation

commit 19f9bd621c76a483ff98f8c7eb78f64753da683a
Author: Fanyi Pu <FPU001@e.ntu.edu.sg>
Date:   Wed Jun 19 15:23:24 2024 +0800

    docs

commit ce6f889ba02d819979c7922f6336cf4f1f718f65
Author: Fanyi Pu <FPU001@e.ntu.edu.sg>
Date:   Wed Jun 19 15:04:16 2024 +0800

    tutorial

commit f513c520c2a3dad26d2b2ca5c4ed4db05a493c73
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 19 06:51:19 2024 +0000

    chore: Update dependencies to fix potential risks and improve compatibility

commit efb529552c5e4ba039a4cba8e9aa5cb7ba65bf90
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Wed Jun 19 10:25:58 2024 +0800

    Release llava-wilder

commit 742651fc9daf97e2f57831ed6e6e7ee7ead7d555
Author: Fanyi Pu <FPU001@e.ntu.edu.sg>
Date:   Wed Jun 19 07:44:26 2024 +0800

    feat: Add support for auto downloading tar format videos

commit 511b6259828212fcba954cdeb8cf90d6e5daabf8
Merge: 22a4958e 050b2c37
Author: Bo Li <drluodian@gmail.com>
Date:   Tue Jun 18 17:01:03 2024 +0000

    Merge branch 'main' of https://github.com/EvolvingLMMs-Lab/lmms-eval

commit 050b2c370017e9b97475dd6cf01fd051b5ca5c86
Merge: 74facb41 ef306512
Author: Li Bo <drluodian@gmail.com>
Date:   Tue Jun 18 13:13:38 2024 +0800

    Merge pull request #114 from zjysteven/add-tinyllava

    add tinyllava

commit ef306512e5135f76dffa383f600b8733015836e8
Author: Jingyang Zhang <jingyang.zhang@duke.edu>
Date:   Mon Jun 17 17:57:02 2024 -0400

    fix typo

commit 9bab67732a4238097725deddf867fb1946ffee40
Merge: dbfb2387 74facb41
Author: Jingyang Zhang <jingyang.zhang@duke.edu>
Date:   Sun Jun 16 10:56:05 2024 -0400

    Merge branch 'EvolvingLMMs-Lab:main' into add-tinyllava

commit 74facb41a826691dfce4458cf1d8659b34fc5bf5
Merge: 8ba192f9 d5df72de
Author: Li Bo <drluodian@gmail.com>
Date:   Sun Jun 16 17:59:19 2024 +0800

    Merge pull request #118 from teowu/main

    Fix the potential risk by PR #117

commit d5df72de2d03108d6b365818ecc3551ac9aa6302
Merge: 5bf59ed2 8ba192f9
Author: Teo (Timothy) Wu Haoning <38696372+teowu@users.noreply.github.com>
Date:   Sun Jun 16 15:32:13 2024 +0800

    Merge branch 'EvolvingLMMs-Lab:main' into main

commit 5bf59ed250da98a408a94e214a73caa400cba842
Author: teowu <realtimothyhwu@gmail.com>
Date:   Sun Jun 16 07:27:28 2024 +0000

    fix #117, allow auto download with tar format videos

commit 98b3955cb808e36303c030aea78eb037d1ec59ce
Merge: a056f118 be9dada8
Author: teowu <realtimothyhwu@gmail.com>
Date:   Sun Jun 16 07:25:07 2024 +0000

    Merge branch 'main' of https://github.com/teowu/lmms-eval into main

commit a056f118704eccec86ce32ab86981ce4bc1e1deb
Author: teowu <realtimothyhwu@gmail.com>
Date:   Sun Jun 16 07:23:54 2024 +0000

    fix #117, allow auto download with tar format videos

commit 8ba192f94edf5d99598983445d5faa4f8807c49f
Merge: 7cc28907 be9dada8
Author: Li Bo <drluodian@gmail.com>
Date:   Sat Jun 15 17:30:59 2024 +0800

    Merge pull request #117 from teowu/main

    LongVideoBench for LMMs-Eval

commit be9dada8b4189c53c08e1674ab273242cf2f80a0
Merge: 62ea8ceb 7cc28907
Author: Teo (Timothy) Wu Haoning <38696372+teowu@users.noreply.github.com>
Date:   Sat Jun 15 16:39:20 2024 +0800

    Merge pull request #1 from EvolvingLMMs-Lab/main

    Merge pull request #113 from teowu/main

commit 62ea8ceb223ef2b51ebab2bcd50d5cf339c35cfe
Author: teowu <realtimothyhwu@gmail.com>
Date:   Sat Jun 15 08:30:11 2024 +0000

    LongVideoBench support: image LMMs (idefics2, phi3) and video LMMs (LLaVA-Next-Video-34B)

commit 7cc28907edbb4eb58ee1398772a48110ea35dd96
Merge: 4bc7224d ea14cd4b
Author: Li Bo <drluodian@gmail.com>
Date:   Sat Jun 15 14:10:22 2024 +0800

    Merge pull request #113 from teowu/main

    Q-Bench, Q-Bench2, A-Bench

commit dbfb23873979f789477f4797ee2d6071e0fd921e
Author: Jingyang <jingyang.zhang@duke.edu>
Date:   Fri Jun 14 16:20:42 2024 -0400

    add tinyllava

commit ea14cd4b361f4c95b3665cbdb95bc51754090eb5
Author: teowu <realtimothyhwu@gmail.com>
Date:   Fri Jun 14 15:01:52 2024 +0000

    Add qbench, qbench2, abench; fix phi3v as its current implementation does not support multi-image

commit 4bc7224dcd27fe8b288bfc3fed4d7a9da9635658
Merge: 2797987f bf14cb85
Author: Li Bo <drluodian@gmail.com>
Date:   Fri Jun 14 02:14:43 2024 +0800

    Merge pull request #111 from XinrunDu/main

    add II-Bench

commit bf14cb8527b2b7ac438a36567a875168bc02d294
Author: XinrunDu <duxinrun2000@gmail.com>
Date:   Thu Jun 13 09:37:02 2024 +0000

    fix dataset_path

commit 6248113f4e11a0ac396d31fa1b032a142fea8cb4
Author: XinrunDu <duxinrun2000@gmail.com>
Date:   Thu Jun 13 09:32:06 2024 +0000

    add II-Bench

commit 2797987f5b88b87bd172714b678a75a1d8051826
Merge: 63d82f1f 66d4bb2d
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 11:14:47 2024 +0800

    Merge pull request #109 from EvolvingLMMs-Lab/pufanyi/update_version

    [Small Update] Update the version of LMMs-Eval

commit 66d4bb2d9c9afbbdea40196d4ad80e214d0b14b6
Author: Fanyi Pu <FPU001@e.ntu.edu.sg>
Date:   Thu Jun 13 11:13:00 2024 +0800

    update version

commit 63d82f1ff11eb430d91a15d6788a1f0b4d596850
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 11:04:32 2024 +0800

    Update README.md

commit 44a33799671cb668f55366d5e5a4ddb051a3a1b4
Merge: 5ed00356 0ce46d08
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 04:00:12 2024 +0800

    Merge pull request #105 from tianyu-z/main

    Include VCR

commit 0ce46d088e473d12d63de44f17c67dceab25658c
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Wed Jun 12 15:56:34 2024 -0400

    update README.md

commit 46a88d8b0199ed44d2ff459fb372f2e006960cea
Merge: 47b13b9b 5ed00356
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Wed Jun 12 15:50:26 2024 -0400

    merged readme.md

commit 47b13b9b320d36ac53b3622557e31239f7c22621
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Wed Jun 12 15:30:52 2024 -0400

    update aggregation function for vcr_wiki

commit 5ed00356676cf5d0ff056cf27d1b519b8e303ff7
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 03:21:42 2024 +0800

    Update README.md

commit ed8806839db5988ced672bd162b7b046edb4863a
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 03:13:59 2024 +0800

    Update README.md

commit fea3806026932a6e2bd6e538bcc413e33abdf245
Merge: d99a24ab 05dc8e85
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 13 03:11:49 2024 +0800

    Merge pull request #108 from EvolvingLMMs-Lab/internal_main_dev

    [Upgrade to v0.2] Embracing Video Evaluations with LMMs-Eval

commit 05dc8e853eab7c6bc782a1e2662d2efe7422f767
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:56:04 2024 +0000

    chore: Update lmms-eval to support video evaluations for LLaVA models

commit cbeee20bc4ffb510a2b23d96cdaf4077be7c2a9e
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:50:30 2024 +0000

    chore: Update lmms-eval to support video evaluations for LLaVA models

commit f00d5498b69dd4f7e54c907ac906abc7c128f000
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:46:33 2024 +0000

    Update image alignment in README.md

commit 34156335db74cef9e3f0915d7172fd6b22456c15
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:43:16 2024 +0000

    Update llava conv_template in lmms_eval/models/llava.py

commit 50575a950736bc8fc1e191310314cbb5fdff5720
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:39:03 2024 +0000

    chore: Update lmms-eval to support video evaluations for LLaVA models

commit c9b2252fb8a15dd04252af5e6b4613855afd6ada
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:33:48 2024 +0000

    Bump version to 0.2.0.dev0

commit 465bd4205e8097e9c037b24a3ed08dd6a7694efa
Merge: e43bd840 d99a24ab
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 15:04:25 2024 +0000

    Merge branch 'main' of https://github.com/EvolvingLMMs-Lab/lmms-eval into internal_main_dev

commit e43bd840b63eb499856e36d9d2ba45c924abcead
Author: Bo Li <drluodian@gmail.com>
Date:   Wed Jun 12 14:54:06 2024 +0000

    chore: Remove unnecessary files and code related to live_bench and sft_eval tasks

commit d99a24abd06df10d07e5a4d0ad5030613f92f2e7
Merge: 374590be a66003be
Author: Li Bo <drluodian@gmail.com>
Date:   Wed Jun 12 19:45:57 2024 +0800

    Merge pull request #107 from AtsuMiyai/new_task/upd_update

    update gpt-3.5-turbo version

commit a66003befe4175824a1be6ed59f5f5b88c15f792
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed Jun 12 17:05:17 2024 +0900

    update gpt-3.5-turbo version

commit ee91f272985f32eeb9cd6faa41afdd8eb49cac30
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed Jun 12 16:50:53 2024 +0900

    update gpt-3.5-turbo version

commit 326b9694fc77398592b8caf3ba0bc2e2bb903813
Author: tianyu-z <zhangtianyupro@gmail.com>
Date:   Mon Jun 10 20:07:40 2024 -0400

    include std and confidence interval

commit cd050d4a721d01a2ace0cd030cf7f8dc67eb8c4d
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Mon Jun 10 18:49:47 2024 -0400

    update vcr_wiki tasks in README.md

commit 205721e0aad76dde30255e56149bbed121883356
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Mon Jun 10 18:43:15 2024 -0400

    update vcr_wiki tasks

commit db8e718b502469e8536ee359c5559de87635ffc7
Author: tianyu-z <zhangtianyupro@gmail.com>
Date:   Mon Jun 10 16:13:58 2024 -0400

    include the try-except logic for spacy

commit 427dabb790118f538b64e4e5bf6a7aab9689b3d9
Author: Suyuchen <suyuchen.wang@umontreal.ca>
Date:   Mon Jun 10 15:51:05 2024 -0400

    add crossed_text to vcr_wiki output

commit 043b483eb55f7be4fea75c9bc0b9b03d251b109b
Author: tianyu-z <zhangtianyupro@gmail.com>
Date:   Mon Jun 10 15:47:00 2024 -0400

    switch logic

commit e1f04db8f58dd10591fde335ea13f74cda7c79bd
Author: tianyu-z <zhangtianyupro@gmail.com>
Date:   Mon Jun 10 02:38:21 2024 -0400

    modify the form of VCR

commit 96e8d9867c9549ab7490f4b12cfeb6a06238e0aa
Author: tianyu-z <zhangtianyupro@gmail.com>
Date:   Mon Jun 10 00:10:30 2024 -0400

    init include vcr

commit 374590be62f988a76cf6704cfe394cd8ae7d4cb6
Merge: 504685e2 cb3b9ce7
Author: Kaichen Zhang - NTU <kaichenzhang358@outlook.com>
Date:   Fri Jun 7 20:25:48 2024 +0800

    Merge pull request #101 from Gumpest/main

    Update conbench in README

commit 504685e20b17659b913cf46f3012c16bf429e09d
Author: Li Bo <drluodian@gmail.com>
Date:   Thu Jun 6 15:42:15 2024 +0800

    Update README.md

commit cb3b9ce71411da862ff01342a9122a3c656ffbd1
Merge: c9793b38 67b64ea4
Author: Yuan Zhang <56063339+Gumpest@users.noreply.github.com>
Date:   Thu Jun 6 11:22:24 2024 +0800

    Merge branch 'EvolvingLMMs-Lab:main' into main

commit c9793b3883714f254a700230b7bee781d6110e73
Author: Yuan Zhang <gump_well_done@163.com>
Date:   Thu Jun 6 11:21:05 2024 +0800

    update README

commit 67b64ea44a5a39d96c7a196a8a8345a7486bd912
Merge: 8ee7848a 5fd68451
Author: Li Bo <drluodian@gmail.com>
Date:   Wed Jun 5 23:12:58 2024 +0800

    Merge pull request #100 from Gumpest/main

    add Conbench

commit 5fd684515c55ef643726c1b6c720c7cbd2183ba1
Author: Yuan Zhang <gump_well_done@163.com>
Date:   Wed Jun 5 21:52:31 2024 +0800

    add conbench

commit 8ee7848aaa6383aa1f919c3f21199c81db3fff89
Merge: 747e1978 6fefaf7c
Author: Li Bo <drluodian@gmail.com>
Date:   Tue Jun 4 17:09:33 2024 +0800

    Merge pull request #95 from AtsuMiyai/new_task/upd

    add MM-UPD

commit 747e19782996065cdce7157ee8c5e15beb5b6c59
Merge: 4854a34d 05843072
Author: Li Bo <drluodian@gmail.com>
Date:   Tue Jun 4 17:09:04 2024 +0800

    Merge pull request #97 from CaraJ7/update

    Add MathVerse in README.md

commit 6fefaf7cea504e35583ee7217449da290295a7a4
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Tue Jun 4 17:36:39 2024 +0900

    update utils.py for leaderboard submission

commit 5f4fe360def1c48ea0cb1da6409d192784882308
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Sun Jun 2 23:28:27 2024 +0900

    slightly change query_prompt for the reproduction

commit 05843072d608b970bcada1cd0db65a3c80864060
Author: CaraJ7 <1350074492@qq.com>
Date:   Sun Jun 2 17:05:28 2024 +0800

    Add MathVerse in README.md

commit 0581ab3cfb362e2024988b46fbbb00324f1233c9
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Fri May 31 16:09:45 2024 +0900

    merge model_specific_prompt_kwargs and dataset_name into each task yaml

commit 4854a34d4d37efb5e201f2691ecdb054590cf20b
Author: Pu Fanyi <FPU001@e.ntu.edu.sg>
Date:   Sat May 4 19:23:39 2024 +0800

    Group MMMU images into one image (#83)

    * update

    * update font

    * Add matplotlib.font_manager import in utils.py

    * Refactor font handling in add_order_label function in utils.py

    * group mmmu

    ---------

    Co-authored-by: Li Bo <drluodian@gmail.com>

commit d224794c49520f4d28a31862cf977198cd6cbc5e
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 15:15:59 2024 +0900

    add upd

commit 453e7936424220f02b99517059ca71babfbe5f5a
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 15:03:30 2024 +0900

    add upd

commit 909edd6769ddcf8a546be4fdd129416687516878
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 12:52:21 2024 +0900

    add upd

commit 7c1ac9706cafc4801fa4da181d2f610b7838c7b8
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 12:50:32 2024 +0900

    add upd

commit 811301c5280ddd74986645086f026ab730c8848c
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 12:46:58 2024 +0900

    add upd

commit 71401bafd1d515f704f86ab4817a758542bc4672
Author: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Date:   Wed May 29 12:41:21 2024 +0900

    add upd

commit 24dc435908d921e9f1a5706e3141b12e5d838d18
Author: Bo Li <drluodian@gmail.com>
Date:   Mon May 27 10:17:32 2024 +0000

    fix compatibility issue of older version llava

commit 616edf43731415b35f0f5e97748ed2e017a2891d
Author: Bo Li <drluodian@gmail.com>
Date:   Mon May 27 09:32:26 2024 +0000

    [Fix] import issues of multilingual llava and olympiadbench

commit 4c5a99e21a63fb0ee1c7d15546d18066e1d9894b
Merge: 45c05b2b b05c3e22
Author: Li Bo <drluodian@gmail.com>
Date:   Mon May 27 14:19:53 2024 +0800

    Merge pull request #87 from vfragoso/vifragos/phi3v

    Adding microsoft/Phi-3-vision-128k-instruct model.

commit b05c3e222fabd308dd7af4e04c1c6a0812962fe6
Author: Victor Fragoso <victor.fragoso@microsoft.com>
Date:   Fri May 24 16:36:37 2024 +0000

    Adding documentation of Phi3v class.

commit c2008971308ce8168d57c24d00b725832f099244
Author: Victor Fragoso <victor.fragoso@microsoft.com>
Date:   Fri May 24 16:25:02 2024 +0000

    Adding prompt arguments for Phi3v on MathVista-TestMini

commit 7f9fb6bcc6cd24a7b8011b8753d0ea98cc2451fd
Author: Victor Fragoso <victor.fragoso@microsoft.com>
Date:   Fri May 24 13:24:16 2024 +0000

    Adding Phi3v model.

commit 45c05b2b2bece76e06849a52a0d034f9c0ac2367
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 23 03:47:36 2024 +0000

    Set printing info for llava_hf to debug level

commit 53f013ed8278776551ca992562253387cc9968d2
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 23 03:41:39 2024 +0000

    Fix pope random name in pope full

commit 22520a95f13334b75eee0cf0387151067a6bf516
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 23 03:41:14 2024 +0000

    Add separated pope tasks by category

commit d1eefb1565014b47287ffa6b350229062f8f602f
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 9 08:36:02 2024 +0000

    Update gitignore

commit b2b4dbd2dc13432c79208db35abf7f55c97f1790
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Mon May 20 07:45:11 2024 +0000

    Comment out Spice in caption task so that don't need to download stanford nlp model

commit 662f05ce4c62a46a83f819d3a5925a9bd20059b5
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Mon May 20 03:13:13 2024 +0000

    Comment out parse result in xcomposer

commit 09329322916bfbb604d72ddaf50441a0947f8805
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 16 03:55:39 2024 +0000

    Fix instructblip qformer size mismatch and multi-images problem

commit 557a6a3b15e07e506bc05e2cc76ff6a2f8c93964
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 16 03:11:41 2024 +0000

    Remove redundant code in fuyu

commit 6aeb5504e74ed1980b53700d8e4d4dcf7d1b38fc
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 16 01:45:24 2024 +0000

    Fix idefics2 llava in the wild bugs

commit aea80e6a71f716951353e1e5d68380243396b4d6
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Wed May 15 11:07:35 2024 +0000

    Better task list_with_num

commit 3c12a080d66b9c38f615b961befca7c30f82fa39
Author: Li Bo <drluodian@gmail.com>
Date:   Sat May 18 02:35:52 2024 +0800

    Update LICENSE

commit 82317a635a4978b32e095a06cc295d0ae23661c2
Author: Li Bo <drluodian@gmail.com>
Date:   Sat May 18 02:29:09 2024 +0800

    Update LICENSE

commit a8bba1cdb51061a0d27bf9a98cca1505b5c58ea5
Author: Li Bo <drluodian@gmail.com>
Date:   Sat May 18 02:28:03 2024 +0800

    Create LICENSE

commit caa5893b5fd2c1d32c72b97f371ccd9a8d9ec3a0
Merge: c0944486 423b0060
Author: Li Bo <drluodian@gmail.com>
Date:   Mon May 13 11:45:26 2024 +0800

    Merge pull request #73 from EvolvingLMMs-Lab/kc/qwen_vl_api

    [Feat] Add qwen vl api

commit c09444860362a136f17641f8b2a1f91c2bbc3715
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Sat May 11 06:11:19 2024 +0000

    Fix llava_hf image tokens number issue

commit 64f07e497f53e5bcbe9e8fb5830cc7a1daaf7ff1
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Thu May 9 02:04:10 2024 +0000

    Fix endless warning for llava_hf generation

commit 8aaa828108da8514dd9cd23a9d6d83a8b67f2d65
Author: Bo Li <drluodian@gmail.com>
Date:   Thu May 2 06:13:56 2024 +0000

    Add model_name parameter to Llava constructor

commit 7847dc4d8efe60605102414bb071b1da9851228e
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Tue May 7 03:15:59 2024 +0000

    Parse result for llava_hf 1.6

commit 3e56b4f92db39a2ce92903b0c43a34f1d14d59ec
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Tue May 7 03:09:56 2024 +0000

    Fix llava_hf generation for 1.6

commit fa3ff92b07ea5aaa633a2039818c310744f84d07
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Mon May 6 08:32:57 2024 +0000

    Fix llava conv template for llama3

commit 423b00606aa77fd6b324c19e3d480b73ab852db6
Author: kcz358 <kaichenzhang358@outlook.com>
Date:   Sun May 5 07:54:52 2024 +0000

    Add qwen vl api

commit b7fd7a9f7aa3c0e1e50374047dfffc46a7462b90
Merge: 986139a9 c5a130b6
Author: Li Bo <drluodian@gmail.com>
Date:   Sun May 5 13:19:48 2024 +0800

    Merge pull request #59 from EvolvingLMMs-Lab/add_idefics2

    add idefics2

commit 986139a9a31154679bdea029b09639f84712db27
Merge: b46239ca 8d3526c0
Author: Li Bo <drluodian@gmail.com>
Date:   Fri May 3 01:18:18 2024 +0800

    Merge pull request #36 from cocoshe/main

    [Fix] repr llava doc

commit b46239cabab7b545ec99d9eae6c851e531b18374
Merge: bc69a744 373265f2
Author: Li Bo <drluodian@gmail.com>
Date:   Fri May 3 01:17:34 2024 +0800

    Merge pull request #56 from gagan3012/main

    Multilingual LLava bench

commit bc69a744d2cffeb06eba62e843bcc7869e27613a
Merge: eef3aeb6 626e8a91
Author: Li Bo <drluodian@gmail.com>
Date:   Fri May 3 01:12:14 2024 +0800

    Merge pull request #70 from hunterheiden/hsh/new_task/WebSRC

    Bugfix: WebSRC should be token-level F1 NOT character-level

commit 626e8a91a4af2dd5dd774fc130cc2f4d74b2bc37
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Thu May 2 09:31:03 2024 -0400

    Bugfix: WebSRC should be token-level F1 NOT character-level

commit eef3aeb6ab589bb1d5045af5b5c1984a69402d19
Merge: c4e9dd9f 9bca4413
Author: Li Bo <drluodian@gmail.com>
Date:   Thu May 2 14:38:17 2024 +0800

    Merge pull request #69 from hunterheiden/hsh/new_task/WebSRC

    [New Task] WebSRC (multimodal Q&A on web screenshots)

commit 9bca441376325173128e5c50087f068e519c48da
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Wed May 1 11:07:29 2024 -0400

    Add code to enable compilation of submission for WebSRC test split

commit 7687495b1ed552eeba088cb9ad5aaf1170e7fff9
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Wed May 1 10:47:32 2024 -0400

    Draft and validate websrc eval on dev split

commit 4eebd3e5d7ab3b8c3116eea57318db72d2ce32bb
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Wed May 1 10:46:54 2024 -0400

    Update main README with new task names

commit 35fe80b67656114a8824eb59574089663bdc4c9a
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Wed May 1 10:46:20 2024 -0400

    Draft README for WebSRC

commit 955bd0635cc6c14a96ad869f1002e6dbefdc5071
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Tue Apr 30 10:16:21 2024 -0400

    Init webSRC

commit c4e9dd9f6e40e8586587c4a75987aa109a37f14b
Merge: d8a3a99f 319afccb
Author: Li Bo <drluodian@gmail.com>
Date:   Fri Apr 26 14:37:22 2024 +0800

    Merge pull request #63 from hunterheiden/hsh/new_task/screenspot

    New Task: ScreenSpot - Grounding (REC) and instruction generation (REG) on screens

commit 319afccbe713ddf40a8a6fa28501e64c0ad34725
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Thu Apr 25 11:44:34 2024 -0400

    slight update

commit 2f3811ca1bbad6a441016b05fde09a571900fca8
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Thu Apr 25 11:41:04 2024 -0400

    Add README file specific to ScreenSpot

commit 28962cbe83631ec5d6481aaea4907a7c96fec848
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Wed Apr 24 11:52:33 2024 -0400

    Update README to reflect new tasks

commit e457cfb4f2d6869e8367d6d5b03ad25ee4acc363
Author: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Date:   Tue Apr 23 18:33:16 2024 -0400

    Create ScreenSpot on clean branch

commit d8a3a99ff6142fe101fa3c188cc7f29593c44345
Merge: 3dcd0158 ed171293
Author: Li Bo <drluodian@gmail.com>
Date:   Tue Apr 23 10:34:03 2024 +0800

    Merge pull request #61 from tupini07/patch-1

    Fix typo in Qwen-VL that was causing "reference before assignment"

commit ed171293d1e82075c5c6a847fc91ecbfd45cf89f
Author: Andrea Tupini <tupini07@gmail.com>
Date:   Mon Apr 22 14:56:41 2024 -0600

    refactor query construction for clarity

commit cd874201c46f32a2903ddffae85f9db73e14adfd
Author: Andrea Tupini <tupini07@gmail.com>
Date:   Mon Apr 22 14:54:29 2024 -0600

    convert contexts to list if necessary and remove unnecessary construction of `questions`

commit 85573674e90c8d505312ba18c5102e0051255078
Author: Andrea Tupini <tupini07@gmail.com>
Date:   Mon Apr 22 14:47:33 2024 -0600

    Fix typo in qwen_vl that was causing "reference before assignment"

commit 3dcd01582b719555bcf8eb25d91cc5e42abd2c5f
Merge: 95df9fee 743673a1
Author: Li Bo <drluodian@gmail.com>
Date:   Sat Apr 20 22:03:16 2024 +0800

    Merge pull request #60 from CaraJ7/main

    Add MathVerse

commit 743673a1419b6e729e18c96f148745cc739d4c71
Merge: c1a54721 95df9fee
Author: CaraJ7 <1350074492@qq.com>
Date:   Sat Apr 20 21:49:02 2024 +0800

    Merge branch 'main' of https://github.com/EvolvingLMMs-Lab/lmms-eval

commit c1a5472135c3b84061b64d997ab50dda0412ba4f
Author: CaraJ7 <1350074492@qq.com>
Date:   Sat Apr 20 21:45:34 2024 +0800

    Add MathVerse

commit 373265f24e7a89cbd49ab724a2e388cc0930be78
Author: Gagan Bhatia <49101362+gagan3012@users.noreply.github.com>
Date:   Fri Apr 12 17:21:39 2024 -0700

    Add files via upload

commit d8530514a5ef9378d2adeaceb228b60ec25a6718
Author: Gagan Bhatia <49101362+gagan3012@users.noreply.github.com>
Date:   Fri Apr 12 17:19:49 2024 -0700

    Create README.md

commit 22a4958e993463edff352ac033014f9a485706cc
Author: Bo Li <bo.li01@bytedance.com>
Date:   Thu Apr 4 17:12:43 2024 +0000

    [WIP] adding mmbench dev evaluation (#75)

    * WIP

    * Update GPT evaluation model name and sys prompt

    * 🛠️ Scale accuracy to percentage

    The accuracy value is now multiplied by 100 in the aggregation function to represent it as a percentage. Regarding the evaluation process, `math` module importation and refactoring reduce progress log verbosity by logging every 100 evaluations instead of 10. It prevents potential logging overflow. Handling of NaN values is added to ensure 'default_value' is set in case of missing data, avoiding errors in split, category, and l2-category assignments. Finally, reporting of categorical and l2-categorical accuracies is streamlined through a new `calculate_hit_rates` function, improving code readability and maintenance.

    Issue refs: #1427, #1533

    * Update GPT evaluation model name and API configuration

    * Refactor MMBench_Evaluator class to handle missing columns

    * Add print statements for detailed results in MMBench-CN(CC), MMBench-CN(Dev), and MMBench-EN(Dev) evaluations

    * Refactor MMBench-CN and MMBench-EN evaluation functions

    * 🔄 Refactor result processing and logging logic

    - Simplified the result processing functions across different utility modules (`cc_utils.py`, `cn_utils.py`, `en_utils.py`) to unify the handling of multiple-choice options. Now, all options ("A" to "E") are dynamically added to the result data, and default to "nan" if not provided in the document.
    - Removed redundant keys directly from the process results dict creation to avoid clutter and align with the new dynamic addition of options.
    - In `mmbench_evals.py`, removed the unnecessary check for all splits being 'dev' and streamlined the evaluation loop by eliminating the progress bar (tqdm) for a cleaner log output.
    - Commented-out code and verbose logging during evaluation, which may have interfered with performance, has been removed for a more efficient and less intrusive logging experience.

    This cleanup reduces redundancy in the codebase and improves evaluation performance.

    Refs #2045

    ---------

    Co-authored-by: Bo Li <bo.li01@bytedance.com>
    (cherry picked from commit a19278c2ea6ddcbca64d3cc7f4efec7fe5775121)

commit 8d3526c0869f0ad7747ff6bb02441140792b461c
Author: cocoshe <1228759711@qq.com>
Date:   Thu Mar 28 13:38:36 2024 +0800

    fix doc
---
 docs/README.md                                |   3 +-
 lmms_eval/__main__.py                         |  29 +-
 lmms_eval/api/metrics.py                      |   5 +-
 lmms_eval/api/model.py                        |   4 +-
 lmms_eval/api/registry.py                     |   3 +-
 lmms_eval/api/task.py                         |   4 +-
 lmms_eval/evaluator.py                        |   6 +-
 lmms_eval/logging_utils.py                    |   7 +-
 lmms_eval/models/__init__.py                  |  14 +-
 lmms_eval/models/batch_gpt4.py                |   7 +-
 lmms_eval/models/claude.py                    |  10 +-
 lmms_eval/models/from_log.py                  |   3 +-
 lmms_eval/models/fuyu.py                      |   5 +-
 lmms_eval/models/gemini_api.py                |   4 +-
 lmms_eval/models/gpt4v.py                     |   4 +-
 lmms_eval/models/idefics2.py                  |   4 +-
 lmms_eval/models/instructblip.py              |   5 +-
 lmms_eval/models/internvl.py                  |   3 +-
 lmms_eval/models/llama_vid.py                 |   3 +-
 lmms_eval/models/llava.py                     |  16 +-
 lmms_eval/models/llava_hf.py                  |   4 +-
 lmms_eval/models/llava_sglang.py              |   4 +-
 lmms_eval/models/llava_vid.py                 |  33 +-
 .../models/{llava_onevision.py => longva.py}  |  45 +--
 lmms_eval/models/minicpm_v.py                 |   4 +-
 lmms_eval/models/model_utils/load_video.py    |   1 -
 .../model_utils/qwen/qwen_generate_utils.py   |   3 +-
 lmms_eval/models/mplug_owl_video.py           |   7 +-
 .../configuration_mplug_owl.py                |   4 +-
 .../mplug_owl_video/modeling_mplug_owl.py     |   9 +-
 .../mplug_owl_video/tokenization_mplug_owl.py |   4 +-
 lmms_eval/models/phi3v.py                     |   3 +-
 lmms_eval/models/qwen_vl.py                   |   4 +-
 lmms_eval/models/qwen_vl_api.py               |   4 +-
 lmms_eval/models/reka.py                      |   9 +-
 lmms_eval/models/tinyllava.py                 |   7 +-
 lmms_eval/models/video_chatgpt.py             |  11 +-
 lmms_eval/models/video_chatgpt/utils.py       | 117 -------
 lmms_eval/models/video_llava.py               |  15 +-
 lmms_eval/models/xcomposer2_4KHD.py           |   4 +-
 lmms_eval/models/xcomposer2_4khd.py           |   3 +-
 lmms_eval/tasks/__init__.py                   |  10 +-
 lmms_eval/tasks/activitynetqa/utils.py        |   5 +-
 lmms_eval/tasks/cmmmu/utils.py                |   4 +-
 lmms_eval/tasks/coco_cap/utils.py             |   3 +-
 lmms_eval/tasks/conbench/utils.py             |   3 +-
 lmms_eval/tasks/cvrr/utils.py                 |   5 +-
 lmms_eval/tasks/docvqa/utils.py               |   4 +-
 lmms_eval/tasks/egoschema/utils.py            |   4 +-
 lmms_eval/tasks/ferret/utils.py               |   5 +-
 lmms_eval/tasks/flickr30k/utils.py            |   3 +-
 .../tasks/hallusion_bench/evaluate_hb.py      |   4 +-
 lmms_eval/tasks/hallusion_bench/utils.py      |   4 +-
 lmms_eval/tasks/ii_bench/utils.py             |   5 +-
 lmms_eval/tasks/infovqa/utils.py              |   5 +-
 .../tasks/internal_eval/d170_cn_utils.py      |   4 +-
 .../tasks/internal_eval/d170_en_utils.py      |   4 +-
 .../tasks/internal_eval/dc100_en_utils.py     |   4 +-
 .../tasks/internal_eval/dc200_cn_utils.py     |   4 +-
 lmms_eval/tasks/llava-bench-coco/utils.py     |   5 +-
 lmms_eval/tasks/llava-in-the-wild/utils.py    |   5 +-
 .../tasks/llava_wilder/llava_wilder_full.yaml |  14 -
 .../llava_wilder/llava_wilder_medium.yaml     |  14 -
 .../llava_wilder/llava_wilder_small.yaml      |   5 +-
 lmms_eval/tasks/llava_wilder/utils.py         |  61 +---
 lmms_eval/tasks/longvideobench/utils.py       |   2 -
 lmms_eval/tasks/mathverse/mathverse_evals.py  |   4 +-
 lmms_eval/tasks/mathverse/utils.py            |   3 +-
 lmms_eval/tasks/mathvista/mathvista_evals.py  |   4 +-
 lmms_eval/tasks/mathvista/utils.py            |   3 +-
 lmms_eval/tasks/mmbench/cc_utils.py           |   3 +-
 lmms_eval/tasks/mmbench/cn_utils.py           |   3 +-
 lmms_eval/tasks/mmbench/en_utils.py           |   3 +-
 lmms_eval/tasks/mmbench/mmbench_evals.py      |   3 +-
 lmms_eval/tasks/mme/utils.py                  |   3 +-
 lmms_eval/tasks/mmmu/utils.py                 |   9 +-
 lmms_eval/tasks/mmmu/utils_group_img.py       |   6 +-
 lmms_eval/tasks/mmupd/mmupd_evals.py          |   4 +-
 lmms_eval/tasks/mmupd/utils.py                |   3 +-
 lmms_eval/tasks/mmvet/utils.py                |   4 +-
 lmms_eval/tasks/multidocvqa/utils.py          |   6 +-
 .../utils.py                                  |   4 +-
 lmms_eval/tasks/nextqa/utils.py               |   4 +-
 lmms_eval/tasks/nocaps/utils.py               |   3 +-
 lmms_eval/tasks/ocrbench/utils.py             |   4 +-
 lmms_eval/tasks/ok_vqa/utils.py               |   4 +-
 lmms_eval/tasks/olympiadbench/cn_utils.py     |   4 +-
 lmms_eval/tasks/olympiadbench/en_utils.py     |   4 +-
 .../olympiadbench/olympiadbench_evals.py      |   3 +-
 lmms_eval/tasks/perceptiontest/test/utils.py  |   4 +-
 lmms_eval/tasks/perceptiontest/val/utils.py   |   4 +-
 lmms_eval/tasks/qbench/utils.py               |   1 -
 lmms_eval/tasks/refcoco+/utils.py             |   3 +-
 lmms_eval/tasks/refcoco/utils.py              |   3 +-
 lmms_eval/tasks/refcocog/utils.py             |   3 +-
 lmms_eval/tasks/screenspot/utils.py           |   3 +-
 lmms_eval/tasks/screenspot/utils_rec.py       |   4 +-
 lmms_eval/tasks/stvqa/utils.py                |   5 +-
 lmms_eval/tasks/synthdog/donut_evaluator.py   |   4 +-
 lmms_eval/tasks/synthdog/utils.py             |   3 +-
 lmms_eval/tasks/tempcompass/utils.py          |   6 +-
 lmms_eval/tasks/textcaps/utils.py             |   3 +-
 lmms_eval/tasks/textvqa/utils.py              |   4 +-
 lmms_eval/tasks/vatex/utils.py                |   4 +-
 lmms_eval/tasks/vcr_wiki/utils.py             |   4 +-
 .../tasks/video_detail_description/utils.py   |   4 +-
 lmms_eval/tasks/videochatgpt/utils.py         |   4 +-
 lmms_eval/tasks/videomme/utils.py             |  86 +++---
 lmms_eval/tasks/vizwiz_vqa/utils.py           |   4 +-
 lmms_eval/tasks/vqav2/utils.py                |   4 +-
 lmms_eval/tasks/websrc/utils.py               |   6 +-
 lmms_eval/tasks/worldqa/utils.py              |   4 +-
 .../tasks/worldqa/worldqa_mc_evaluator.py     |   3 +-
 lmms_eval/tasks/youcook2/utils.py             |   4 +-
 lmms_eval/utils.py                            |  30 +-
 pyproject.toml                                |   6 +-
 tools/make_image_hf_dataset.ipynb             | 292 ++++++++++++++++++
 ...aset.ipynb => make_video_hf_dataset.ipynb} |  20 --
 118 files changed, 579 insertions(+), 657 deletions(-)
 rename lmms_eval/models/{llava_onevision.py => longva.py} (92%)
 mode change 100755 => 100644
 delete mode 100755 lmms_eval/tasks/llava_wilder/llava_wilder_full.yaml
 delete mode 100644 lmms_eval/tasks/llava_wilder/llava_wilder_medium.yaml
 create mode 100755 tools/make_image_hf_dataset.ipynb
 rename tools/{make_hf_dataset.ipynb => make_video_hf_dataset.ipynb} (93%)

diff --git a/docs/README.md b/docs/README.md
index 020e351b..2c912363 100755
--- a/docs/README.md
+++ b/docs/README.md
@@ -8,4 +8,5 @@ Majority of this documentation is adapted from [lm-eval-harness](https://github.
 
 * To learn about the command line flags, see the [commands](commands.md)
 * To learn how to add a new moddel,  see the [Model Guide](model_guide.md).
-* For a crash course on adding new tasks to the library, see our [Task Guide](task_guide.md).
\ No newline at end of file
+* For a crash course on adding new tasks to the library, see our [Task Guide](task_guide.md).
+* If you need to upload your datasets into correct HF format with viewer supported, please refer to [tools](https://github.com/EvolvingLMMs-Lab/lmms-eval/tree/pufanyi/hf_dataset_docs/tools)
diff --git a/lmms_eval/__main__.py b/lmms_eval/__main__.py
index 2949705f..96be8f06 100755
--- a/lmms_eval/__main__.py
+++ b/lmms_eval/__main__.py
@@ -1,12 +1,10 @@
 import os
 import yaml
 import sys
-import copy
 import json
-import logging
+
 import traceback
 import argparse
-import torch
 import numpy as np
 import datetime
 
@@ -25,10 +23,7 @@
 from lmms_eval.tasks import initialize_tasks, include_path, get_task_dict
 from lmms_eval.api.registry import ALL_TASKS
 from lmms_eval.logging_utils import WandbLogger
-from lmms_eval.utils import PathFormatter
-
-
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def _handle_non_serializable(o):
@@ -166,9 +161,10 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
         print("└───────────────────────────────────────────────────────────────────────────────┘")
         sys.exit(1)
 
-    set_loggers(args)
-    eval_logger = logging.getLogger("lmms-eval")
-    eval_logger.setLevel(getattr(logging, f"{args.verbosity}"))
+    # reset logger
+    eval_logger.remove()
+    eval_logger.add(sys.stdout, colorize=True, level=args.verbosity)
+    eval_logger.add(sys.stderr, level=args.verbosity)
     eval_logger.info(f"Verbosity set to {args.verbosity}")
     os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
@@ -228,11 +224,6 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
 
 
 def cli_evaluate_single(args: Union[argparse.Namespace, None] = None) -> None:
-    eval_logger = logging.getLogger("lmms-eval")
-    eval_logger.setLevel(getattr(logging, f"{args.verbosity}"))
-    eval_logger.info(f"Verbosity set to {args.verbosity}")
-    os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
     initialize_tasks(args.verbosity)
 
     if args.predict_only:
@@ -350,13 +341,5 @@ def print_results(args, results):
         print(evaluator.make_table(results, "groups"))
 
 
-def set_loggers(args):
-    eval_logger = logging.getLogger("lmms-eval")
-    ch = logging.StreamHandler()
-    formatter = PathFormatter("%(asctime)s [%(pathname)s:%(lineno)d] %(levelname)s %(message)s", "%m-%d %H:%M:%S", timezone=args.timezone)
-    ch.setFormatter(formatter)
-    eval_logger.addHandler(ch)
-
-
 if __name__ == "__main__":
     cli_evaluate()
diff --git a/lmms_eval/api/metrics.py b/lmms_eval/api/metrics.py
index c0e5c505..157ed2df 100755
--- a/lmms_eval/api/metrics.py
+++ b/lmms_eval/api/metrics.py
@@ -9,10 +9,7 @@
 import torch
 
 from lmms_eval.api.registry import register_metric, register_aggregation
-
-import logging
-
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 # Register Aggregations First
diff --git a/lmms_eval/api/model.py b/lmms_eval/api/model.py
index 3a2e2d97..e6eeaba6 100755
--- a/lmms_eval/api/model.py
+++ b/lmms_eval/api/model.py
@@ -8,9 +8,9 @@
 from lmms_eval.api.instance import Instance
 from tqdm import tqdm
 from lmms_eval import utils
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+
+from loguru import logger as eval_logger
 
 T = TypeVar("T", bound="lmms")
 
diff --git a/lmms_eval/api/registry.py b/lmms_eval/api/registry.py
index 253341db..6b911997 100755
--- a/lmms_eval/api/registry.py
+++ b/lmms_eval/api/registry.py
@@ -1,10 +1,9 @@
 from lmms_eval.api.model import lmms
 
 from typing import Callable, Dict
-import logging
 import evaluate as hf_evaluate
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 MODEL_REGISTRY = {}
 
diff --git a/lmms_eval/api/task.py b/lmms_eval/api/task.py
index 3e9040e6..5560745c 100755
--- a/lmms_eval/api/task.py
+++ b/lmms_eval/api/task.py
@@ -2,7 +2,7 @@
 import ast
 import itertools
 import json
-import logging
+
 import os
 import random
 import re
@@ -37,7 +37,7 @@
 )
 from lmms_eval.filters import build_filter_ensemble
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 # HuggingfaceM4/NoCaps contains truncated image in test split
 # Include this inside code block to avoid error
diff --git a/lmms_eval/evaluator.py b/lmms_eval/evaluator.py
index 8a4c49d8..0104b01a 100755
--- a/lmms_eval/evaluator.py
+++ b/lmms_eval/evaluator.py
@@ -7,7 +7,7 @@
 from tqdm import tqdm
 
 import torch
-import logging
+
 import numpy as np
 from datasets import Image, Sequence
 
@@ -17,8 +17,6 @@
 import lmms_eval.api.metrics
 import lmms_eval.api.registry
 
-import re
-
 from lmms_eval.utils import (
     positional_deprecated,
     run_task_tests,
@@ -28,7 +26,7 @@
     simple_parse_args_string,
 )
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 @positional_deprecated
diff --git a/lmms_eval/logging_utils.py b/lmms_eval/logging_utils.py
index 6107d21b..4d7e0910 100755
--- a/lmms_eval/logging_utils.py
+++ b/lmms_eval/logging_utils.py
@@ -1,6 +1,6 @@
 # Code mostly from: https://github.com/EleutherAI/lm-evaluation-harness/pull/1339, credit to: https://github.com/ayulockin
 import copy
-import logging
+
 import re
 import os
 import json
@@ -9,13 +9,10 @@
 import numpy as np
 from datetime import datetime
 from typing import Any, Dict, List, Literal, Tuple, Union
-
 from packaging.version import Version
-
 from lmms_eval import utils
 import tenacity
-
-logger = logging.getLogger(__name__)
+from loguru import logger
 
 try:
     import wandb
diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py
index 9b19447d..a7f2fb39 100755
--- a/lmms_eval/models/__init__.py
+++ b/lmms_eval/models/__init__.py
@@ -1,7 +1,8 @@
-import os
-import hf_transfer
+from loguru import logger
+import sys
 
-os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+logger.remove()
+logger.add(sys.stdout, level="WARNING")
 
 AVAILABLE_MODELS = {
     "llava": "Llava",
@@ -22,18 +23,19 @@
     "idefics2": "Idefics2",
     "internvl": "InternVLChat",
     "gemini_api": "GeminiAPI",
-    "gemini_model": "GeminiModel",
     "reka": "Reka",
-    "llava_onevision": "Llava_OneVision",
     "from_log": "FromLog",
     "mplug_owl_video": "mplug_Owl",
     "phi3v": "Phi3v",
     "tinyllava": "TinyLlava",
     "llava_onevision": "Llava_OneVision",
+    "llava_hf": "LlavaHf",
+    "longva": "LongVA",
 }
 
 for model_name, model_class in AVAILABLE_MODELS.items():
     try:
         exec(f"from .{model_name} import {model_class}")
-    except ImportError:
+    except ImportError as e:
+        # logger.warning(f"Failed to import {model_class} from {model_name}: {e}")
         pass
diff --git a/lmms_eval/models/batch_gpt4.py b/lmms_eval/models/batch_gpt4.py
index 54bfa149..8f4c2220 100755
--- a/lmms_eval/models/batch_gpt4.py
+++ b/lmms_eval/models/batch_gpt4.py
@@ -2,7 +2,7 @@
 from copy import deepcopy
 from io import BytesIO
 import base64
-import logging
+
 import os
 import time
 import json
@@ -20,14 +20,13 @@
 from lmms_eval.api.instance import Instance
 from lmms_eval.api.model import lmms
 from lmms_eval.api.registry import register_model
-from lmms_eval import utils
+from loguru import logger as eval_logger
 
 # Conditional imports
 try:
     from decord import VideoReader, cpu
 except ImportError:
-    eval_logger = logging.getLogger("lmms-eval")
-    eval_logger.info("Decord is not installed. Video input will not be supported.")
+    eval_logger.warning("Decord is not installed. Video input will not be supported.")
 
 # Constants and global configurations
 API_TYPE = os.getenv("API_TYPE", "openai")
diff --git a/lmms_eval/models/claude.py b/lmms_eval/models/claude.py
index c629ca06..4c967e88 100644
--- a/lmms_eval/models/claude.py
+++ b/lmms_eval/models/claude.py
@@ -5,28 +5,28 @@
 import json
 from typing import List, Tuple, Union
 from tqdm import tqdm
-import requests as url_requests
 import time
-import logging
 
 from lmms_eval.api.instance import Instance
 from lmms_eval.api.model import lmms
 from lmms_eval.api.registry import register_model
-from lmms_eval import utils
 
 from accelerate import Accelerator, DistributedType
 
 from PIL import Image
 
 NUM_SECONDS_TO_SLEEP = 5
-eval_logger = logging.getLogger("lmms-eval")
+
+from loguru import logger
+
+eval_logger = logger
 
 try:
     import anthropic
     from decord import VideoReader, cpu
     import numpy as np
 except Exception as e:
-    eval_logger.error(f"Error importing claude: {e}")
+    eval_logger.warning(f"Error importing claude: {e}")
 
 API_URL = os.getenv("ANTHROPIC_API_URL", "https://api.anthropic.com/v1/complete")
 API_KEY = os.getenv("ANTHROPIC_API_KEY", "YOUR_API_KEY")
diff --git a/lmms_eval/models/from_log.py b/lmms_eval/models/from_log.py
index 4c573e0f..c774a086 100644
--- a/lmms_eval/models/from_log.py
+++ b/lmms_eval/models/from_log.py
@@ -1,4 +1,3 @@
-import logging
 import json
 import os
 import re
@@ -11,7 +10,7 @@
 from lmms_eval.api.instance import Instance
 from accelerate import Accelerator, DistributedType
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 @register_model("from_log")
diff --git a/lmms_eval/models/fuyu.py b/lmms_eval/models/fuyu.py
index 5960d69e..fa108358 100755
--- a/lmms_eval/models/fuyu.py
+++ b/lmms_eval/models/fuyu.py
@@ -15,11 +15,8 @@
 from tqdm import tqdm
 from accelerate import Accelerator, DistributedType
 from accelerate.state import AcceleratorState
-import logging
 
-import logging
-
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 @register_model("fuyu")
diff --git a/lmms_eval/models/gemini_api.py b/lmms_eval/models/gemini_api.py
index 0b2be05e..4a43c9af 100644
--- a/lmms_eval/models/gemini_api.py
+++ b/lmms_eval/models/gemini_api.py
@@ -1,7 +1,7 @@
 import io
 import os
 import time
-import logging
+
 import json
 
 from PIL import Image
@@ -12,7 +12,7 @@
 from lmms_eval.api.instance import Instance
 from accelerate import Accelerator, DistributedType
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 try:
     import google.generativeai as genai
diff --git a/lmms_eval/models/gpt4v.py b/lmms_eval/models/gpt4v.py
index d28c1b00..729e73f7 100755
--- a/lmms_eval/models/gpt4v.py
+++ b/lmms_eval/models/gpt4v.py
@@ -7,7 +7,7 @@
 from tqdm import tqdm
 import requests as url_requests
 import time
-import logging
+
 
 from lmms_eval.api.instance import Instance
 from lmms_eval.api.model import lmms
@@ -26,7 +26,7 @@
 
 API_TYPE = os.getenv("API_TYPE", "openai")
 NUM_SECONDS_TO_SLEEP = 30
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 if API_TYPE == "openai":
     API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
diff --git a/lmms_eval/models/idefics2.py b/lmms_eval/models/idefics2.py
index 608d694b..090cdee5 100644
--- a/lmms_eval/models/idefics2.py
+++ b/lmms_eval/models/idefics2.py
@@ -1,5 +1,5 @@
 import torch
-import logging
+
 from tqdm import tqdm
 from lmms_eval import utils
 from lmms_eval.api.instance import Instance
@@ -14,7 +14,7 @@
 
 warnings.filterwarnings("ignore")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 DEFAULT_IMAGE_TOKEN = "<image>"
 try:
diff --git a/lmms_eval/models/instructblip.py b/lmms_eval/models/instructblip.py
index 3ca068ed..c33ab122 100755
--- a/lmms_eval/models/instructblip.py
+++ b/lmms_eval/models/instructblip.py
@@ -1,5 +1,5 @@
 import torch
-import logging
+
 import copy
 from tqdm import tqdm
 from lmms_eval import utils
@@ -20,8 +20,7 @@
 
 warnings.filterwarnings("ignore")
 
-eval_logger = logging.getLogger("lmms-eval")
-transformers.logging.set_verbosity_error()
+from loguru import logger as eval_logger
 
 
 @register_model("instructblip")
diff --git a/lmms_eval/models/internvl.py b/lmms_eval/models/internvl.py
index d808081a..6238d7fc 100644
--- a/lmms_eval/models/internvl.py
+++ b/lmms_eval/models/internvl.py
@@ -1,4 +1,3 @@
-import logging
 import os
 from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
 from accelerate.state import AcceleratorState
@@ -26,7 +25,7 @@
 import sys
 
 sys.path.append(os.path.join(str(wd), "InternVL", "internvl_chat"))
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 if not hasattr(eval_logger, "internvl_warning_logged"):
     eval_logger.internvl_warning_logged = False
diff --git a/lmms_eval/models/llama_vid.py b/lmms_eval/models/llama_vid.py
index 69627fe8..e62b4ef7 100644
--- a/lmms_eval/models/llama_vid.py
+++ b/lmms_eval/models/llama_vid.py
@@ -1,4 +1,3 @@
-import logging
 import os
 from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
 from accelerate.state import AcceleratorState
@@ -22,7 +21,7 @@
 
 import subprocess
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 try:
     from llamavid.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py
index b49cf55b..6de4c8f8 100755
--- a/lmms_eval/models/llava.py
+++ b/lmms_eval/models/llava.py
@@ -2,7 +2,7 @@
 
 torch.backends.cuda.matmul.allow_tf32 = True
 
-import logging
+
 import copy
 from tqdm import tqdm
 from datetime import timedelta
@@ -21,7 +21,7 @@
 
 warnings.filterwarnings("ignore")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 try:
     from llava.model.builder import load_pretrained_model
@@ -358,18 +358,6 @@ def _collate(x):
                 prompt_question = conv.get_prompt()
                 question_input.append(prompt_question)
 
-            # The above for loop has bugs. When there is no visuals, e.g. pure text,
-            # there will be no for loop execute resulting in an empty question_input (because no visuals)
-            # Scenario 1 won't even be execute
-            if len(flattened_visuals) == 0:
-                for context in contexts:
-                    question = context
-                    conv = conv_templates[self.conv_template].copy()
-                    conv.append_message(conv.roles[0], question)
-                    conv.append_message(conv.roles[1], None)
-                    prompt_question = conv.get_prompt()
-                    question_input.append(prompt_question)
-
             # input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device)
             # preconfigure gen_kwargs with defaults
             gen_kwargs["image_sizes"] = [flattened_visuals[idx].size for idx in range(len(flattened_visuals))]
diff --git a/lmms_eval/models/llava_hf.py b/lmms_eval/models/llava_hf.py
index de0fb9ec..547daa29 100644
--- a/lmms_eval/models/llava_hf.py
+++ b/lmms_eval/models/llava_hf.py
@@ -1,5 +1,5 @@
 import torch
-import logging
+
 from tqdm import tqdm
 from lmms_eval import utils
 from lmms_eval.api.instance import Instance
@@ -14,7 +14,7 @@
 
 warnings.filterwarnings("ignore")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 DEFAULT_IMAGE_TOKEN = "<image>"
 
diff --git a/lmms_eval/models/llava_sglang.py b/lmms_eval/models/llava_sglang.py
index 47c67288..d473e4c1 100644
--- a/lmms_eval/models/llava_sglang.py
+++ b/lmms_eval/models/llava_sglang.py
@@ -3,7 +3,7 @@
 
 torch.backends.cuda.matmul.allow_tf32 = True
 
-import logging
+
 from tqdm import tqdm
 from datetime import timedelta
 
@@ -19,7 +19,7 @@
 from concurrent.futures import ThreadPoolExecutor, as_completed
 import tempfile
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 try:
     import sglang as sgl
diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py
index cbbe7863..675c396e 100755
--- a/lmms_eval/models/llava_vid.py
+++ b/lmms_eval/models/llava_vid.py
@@ -1,4 +1,3 @@
-import logging
 from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
 from accelerate.state import AcceleratorState
 from typing import List, Optional, Union, Tuple
@@ -16,31 +15,22 @@
 from lmms_eval.api.registry import register_model
 from lmms_eval.models.model_utils.load_video import read_video_pyav
 
-eval_logger = logging.getLogger("lmms-eval")
-import sys
+from loguru import logger as eval_logger
 
-sys.path.append("llava-video")
 try:
-    from llavavid.model.language_model.llava_llama import LlavaConfig
-
-    # from llavavid.model.language_model.llava_qwen import LlavaQwenConfig
     from llavavid.model.builder import load_pretrained_model
     from llavavid.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
-    from llavavid.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+    from llavavid.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
     from llavavid.conversation import conv_templates, SeparatorStyle
-
-    # AutoConfig.register("llava_qwen", LlavaQwenConfig)
-    AutoConfig.register("llava_llama", LlavaConfig)
-
+    from llavavid.mm_utils import tokenizer_image_token_qwen_merge, preprocess_qwen, preprocess_llama3
 except ImportError:
     eval_logger.debug("LLaVA-Video is not installed. Please install LLaVA-Video to use this model.")
 
-try:
-    from llavavid.model.language_model.llava_qwen import LlavaQwenConfig
+from llavavid.model.language_model.llava_qwen import LlavaQwenConfig
+from llavavid.model.language_model.llava_llama import LlavaConfig
 
-    AutoConfig.register("llava_qwen", LlavaQwenConfig)
-except:
-    eval_logger.debug("")
+AutoConfig.register("llava_qwen", LlavaQwenConfig)
+AutoConfig.register("llava_llama", LlavaConfig)
 
 
 @register_model("llavavid")
@@ -361,7 +351,7 @@ def generate_until(self, requests) -> List[str]:
             if self.model.config.mm_use_im_start_end:
                 qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + qs
             else:
-                qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
+                qs = DEFAULT_IMAGE_TOKEN * len(videos) + "\n" + qs
 
             # This is much safer for llama3, as we now have some object type in it
             if "llama_3" in self.conv_template:
@@ -379,11 +369,6 @@ def generate_until(self, requests) -> List[str]:
                 pad_token_ids = 0  # lmms-lab/llama3-llava-8b is trained on this pad token id. You may need to customize this for other models.
             attention_masks = input_ids.ne(pad_token_ids).long().cuda()
 
-            # input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input]
-            # pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
-            # input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device)
-            # attention_masks = input_ids.ne(pad_token_ids).to(self.device)
-
             stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
             keywords = [stop_str]
             stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids)
@@ -393,7 +378,7 @@ def generate_until(self, requests) -> List[str]:
             if "max_new_tokens" not in gen_kwargs:
                 gen_kwargs["max_new_tokens"] = 1024
             if "temperature" not in gen_kwargs:
-                gen_kwargs["temperature"] = 0.2
+                gen_kwargs["temperature"] = 0
             if "top_p" not in gen_kwargs:
                 gen_kwargs["top_p"] = None
             if "num_beams" not in gen_kwargs:
diff --git a/lmms_eval/models/llava_onevision.py b/lmms_eval/models/longva.py
old mode 100755
new mode 100644
similarity index 92%
rename from lmms_eval/models/llava_onevision.py
rename to lmms_eval/models/longva.py
index a755b0c9..c5bf6861
--- a/lmms_eval/models/llava_onevision.py
+++ b/lmms_eval/models/longva.py
@@ -30,22 +30,14 @@
 from lmms_eval.models.model_utils.load_video import read_video_pyav
 
 try:
-    from llava.model.builder import load_pretrained_model
-    from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token, KeywordsStoppingCriteria
-    from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
-    from llava.conversation import conv_templates, SeparatorStyle
+    from longva.model.builder import load_pretrained_model
+    from longva.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token, KeywordsStoppingCriteria
+    from longva.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+    from longva.conversation import conv_templates, SeparatorStyle
 
 except Exception as e:
-    eval_logger.debug("LLaVA is not installed. Please install LLaVA to use this model.\nError: %s" % e)
+    eval_logger.debug("longva is not installed. Please install longva to use this model.\nError: %s" % e)
 
-try:
-    from llavavid.model.language_model.llava_qwen import LlavaQwenConfig
-    from llavavid.model.language_model.llava_llama import LlavaConfig
-
-    AutoConfig.register("llava_qwen", LlavaQwenConfig)
-    AutoConfig.register("llava_llama", LlavaConfig)
-except Exception as e:
-    eval_logger.debug("")
 # inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5
 # if is_flash_attn_2_available:
 #     best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating
@@ -56,15 +48,11 @@
     best_fit_attn_implementation = "eager"
 
 
-@register_model("llava_onevision")
-class Llava_OneVision(lmms):
-    """
-    Llava Model
-    """
-
+@register_model("longva")
+class LongVA(lmms):
     def __init__(
         self,
-        pretrained: str = "liuhaotian/llava-v1.5-7b",
+        pretrained: str = "lmms-lab/LongVA-7B",
         truncation: Optional[bool] = True,
         device: Optional[str] = "cuda:0",
         batch_size: Optional[Union[int, str]] = 1,
@@ -121,18 +109,6 @@ def __init__(
         overwrite_config["mm_spatial_pool_mode"] = self.mm_spatial_pool_mode
         cfg_pretrained = AutoConfig.from_pretrained(self.pretrained)
 
-        if cfg_pretrained.architectures[0] == "LlavaLlamaForCausalLM":  # Ugly code, only used in  vicuna that needs ROPE
-            if "224" in cfg_pretrained.mm_vision_tower:
-                least_token_number = self.max_frames_num * (16 // self.mm_spatial_pool_stride) ** 2 + 1000
-            else:
-                least_token_number = self.max_frames_num * (24 // self.mm_spatial_pool_stride) ** 2 + 1000
-
-            scaling_factor = math.ceil(least_token_number / 4096)
-            if scaling_factor >= 2:
-                overwrite_config["rope_scaling"] = {"factor": float(scaling_factor), "type": "linear"}
-                overwrite_config["max_sequence_length"] = 4096 * scaling_factor
-                overwrite_config["tokenizer_model_max_length"] = 4096 * scaling_factor
-
         llava_model_args["overwrite_config"] = overwrite_config
         try:
             # Try to load the model with the multimodal argument
@@ -364,6 +340,7 @@ def _collate(x):
             task = batched_task[0]
             split = batched_split[0]
             batched_visuals = [batched_doc_to_visual[0](self.task_dict[task][split][ids]) for ids in batched_doc_id]  # [B, N]
+            flattened_visuals = self.flatten(batched_visuals)  # [B*N]
             assert len(batched_visuals) == 1
 
             # we assume all gen kwargs in the batch are the same
@@ -451,7 +428,7 @@ def _collate(x):
             attention_masks = input_ids.ne(pad_token_ids).to(self.device)
 
             if task_type == "image":
-                gen_kwargs["image_sizes"] = [batched_visuals[idx][0].size for idx in range(len(batched_visuals))]
+                gen_kwargs["image_sizes"] = [flattened_visuals[idx].size for idx in range(len(flattened_visuals))]
             elif task_type == "video":
                 stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
                 keywords = [stop_str]
@@ -464,7 +441,7 @@ def _collate(x):
             # These steps are not in LLaVA's original code, but are necessary for generation to work
             # TODO: attention to this major generation step...
             if "image_aspect_ratio" in gen_kwargs.keys():
-                gen_kwargs.pop("image_aspect_ratio") 
+                gen_kwargs.pop("image_aspect_ratio")
             try:
                 with torch.inference_mode():
                     cont = self.model.generate(input_ids, attention_mask=attention_masks, pad_token_id=pad_token_ids, images=image_tensor, use_cache=self.use_cache, **gen_kwargs)
diff --git a/lmms_eval/models/minicpm_v.py b/lmms_eval/models/minicpm_v.py
index e951a3df..9a6e0215 100755
--- a/lmms_eval/models/minicpm_v.py
+++ b/lmms_eval/models/minicpm_v.py
@@ -1,5 +1,5 @@
 import torch
-import logging
+
 from tqdm import tqdm
 from lmms_eval import utils
 from lmms_eval.api.instance import Instance
@@ -15,7 +15,7 @@
 
 warnings.filterwarnings("ignore")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 @register_model("minicpm_v")
diff --git a/lmms_eval/models/model_utils/load_video.py b/lmms_eval/models/model_utils/load_video.py
index 789039e7..25be5fa4 100644
--- a/lmms_eval/models/model_utils/load_video.py
+++ b/lmms_eval/models/model_utils/load_video.py
@@ -29,7 +29,6 @@ def record_video_length_packet(container):
 
 
 def read_video_pyav(video_path, num_frm=8):
-    
     if "webm" not in video_path and "mkv" not in video_path:
         # For mp4, we try loading with stream first
         try:
diff --git a/lmms_eval/models/model_utils/qwen/qwen_generate_utils.py b/lmms_eval/models/model_utils/qwen/qwen_generate_utils.py
index 7e7dd97c..3717665a 100755
--- a/lmms_eval/models/model_utils/qwen/qwen_generate_utils.py
+++ b/lmms_eval/models/model_utils/qwen/qwen_generate_utils.py
@@ -15,10 +15,9 @@
 import torch
 import torch.nn.functional as F
 from transformers import PreTrainedTokenizer
-from transformers import logging
 from transformers.generation import LogitsProcessor
 
-logger = logging.get_logger(__name__)
+from loguru import logger
 
 # Types.
 HistoryType = List[Tuple[str, str]]
diff --git a/lmms_eval/models/mplug_owl_video.py b/lmms_eval/models/mplug_owl_video.py
index bfc52d23..1055f4dd 100644
--- a/lmms_eval/models/mplug_owl_video.py
+++ b/lmms_eval/models/mplug_owl_video.py
@@ -1,4 +1,3 @@
-import logging
 from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
 from accelerate.state import AcceleratorState
 from typing import List, Optional, Union, Tuple
@@ -7,17 +6,17 @@
 from tqdm import tqdm
 from datetime import timedelta
 
-from lmms_eval import utils
 from lmms_eval.api.instance import Instance
 from lmms_eval.api.model import lmms
 from lmms_eval.api.registry import register_model
-from lmms_eval.utils import stop_sequences_criteria
 
 from lmms_eval.models.mplug_owl_video.modeling_mplug_owl import MplugOwlForConditionalGeneration
 from lmms_eval.models.mplug_owl_video.processing_mplug_owl import MplugOwlImageProcessor, MplugOwlProcessor
 
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger
+
+eval_logger = logger
 
 
 @register_model("mplug_owl_video")
diff --git a/lmms_eval/models/mplug_owl_video/configuration_mplug_owl.py b/lmms_eval/models/mplug_owl_video/configuration_mplug_owl.py
index 6b5d458d..ce5b777b 100644
--- a/lmms_eval/models/mplug_owl_video/configuration_mplug_owl.py
+++ b/lmms_eval/models/mplug_owl_video/configuration_mplug_owl.py
@@ -19,11 +19,9 @@
 
 from transformers.configuration_utils import PretrainedConfig
 from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
-from transformers.utils import logging
 from transformers.models.auto import CONFIG_MAPPING
 
-
-logger = logging.get_logger(__name__)
+from loguru import logger
 
 MPLUG_OWL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "MAGAer13/mplug-owl-llama-7b": "https://huggingface.co/MAGAer13/mplug-owl-llama-7b/resolve/main/config.json",
diff --git a/lmms_eval/models/mplug_owl_video/modeling_mplug_owl.py b/lmms_eval/models/mplug_owl_video/modeling_mplug_owl.py
index 6c5b7592..07bf9f0c 100644
--- a/lmms_eval/models/mplug_owl_video/modeling_mplug_owl.py
+++ b/lmms_eval/models/mplug_owl_video/modeling_mplug_owl.py
@@ -14,9 +14,9 @@
 # limitations under the License.
 """ PyTorch MplugOwl model. """
 
-import logging
 import math
 from typing import Any, Optional, Tuple, Union
+from loguru import logger
 
 try:
     from flash_attn.flash_attn_interface import flash_attn_unpadded_func
@@ -24,7 +24,8 @@
     flash_attn_func = flash_attn_unpadded_func
 except:
     flash_attn_func = None
-    print("Error importing flash_attn in mplug_owl. Please install flash-attn first.")
+    logger.warning("Error importing flash_attn in mplug_owl. Please install flash-attn first.")
+
 import math
 from dataclasses import dataclass
 from typing import Any, Optional, Tuple, Union
@@ -41,15 +42,11 @@
     ModelOutput,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
-    logging,
     replace_return_docstrings,
 )
 from transformers.models.auto import AutoModelForCausalLM
 from .configuration_mplug_owl import MplugOwlConfig, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig
 
-
-logger = logging.get_logger(__name__)
-
 _CHECKPOINT_FOR_DOC = "MAGAer13/mplug-owl-llama-7b"
 _CONFIG_FOR_DOC = "MplugOwlConfig"
 
diff --git a/lmms_eval/models/mplug_owl_video/tokenization_mplug_owl.py b/lmms_eval/models/mplug_owl_video/tokenization_mplug_owl.py
index 22384b44..bd7f42f1 100644
--- a/lmms_eval/models/mplug_owl_video/tokenization_mplug_owl.py
+++ b/lmms_eval/models/mplug_owl_video/tokenization_mplug_owl.py
@@ -14,11 +14,9 @@
 # limitations under the License.
 """Tokenization classes for MplugOwl."""
 
-from transformers.utils import logging
 from transformers.models.llama.tokenization_llama import LlamaTokenizer
 
-
-logger = logging.get_logger(__name__)
+from loguru import logger
 
 VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
 
diff --git a/lmms_eval/models/phi3v.py b/lmms_eval/models/phi3v.py
index faa59ef5..ab1e838d 100644
--- a/lmms_eval/models/phi3v.py
+++ b/lmms_eval/models/phi3v.py
@@ -1,5 +1,4 @@
 import torch
-import logging
 
 from accelerate import Accelerator, DistributedType
 from lmms_eval import utils
@@ -11,7 +10,7 @@
 from transformers import AutoProcessor
 from typing import List, Optional, Tuple, Union
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 @register_model("phi3v")
diff --git a/lmms_eval/models/qwen_vl.py b/lmms_eval/models/qwen_vl.py
index e55ad7c9..dff301d3 100755
--- a/lmms_eval/models/qwen_vl.py
+++ b/lmms_eval/models/qwen_vl.py
@@ -1,5 +1,5 @@
 import torch
-import logging
+
 from tqdm import tqdm
 from lmms_eval import utils
 from lmms_eval.api.instance import Instance
@@ -16,7 +16,7 @@
 warnings.simplefilter("ignore", category=DeprecationWarning)
 warnings.filterwarnings("ignore")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 
diff --git a/lmms_eval/models/qwen_vl_api.py b/lmms_eval/models/qwen_vl_api.py
index af7f2713..8a0ccc65 100644
--- a/lmms_eval/models/qwen_vl_api.py
+++ b/lmms_eval/models/qwen_vl_api.py
@@ -6,7 +6,7 @@
 from tqdm import tqdm
 import requests as url_requests
 import time
-import logging
+
 
 from lmms_eval.api.instance import Instance
 from lmms_eval.api.model import lmms
@@ -16,7 +16,7 @@
 from PIL import Image
 
 NUM_SECONDS_TO_SLEEP = 5
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 try:
     import dashscope
diff --git a/lmms_eval/models/reka.py b/lmms_eval/models/reka.py
index d5e85d5d..bc461cad 100644
--- a/lmms_eval/models/reka.py
+++ b/lmms_eval/models/reka.py
@@ -8,7 +8,7 @@
 from tqdm import tqdm
 import requests as url_requests
 import time
-import logging
+
 import json
 
 from lmms_eval.api.instance import Instance
@@ -17,14 +17,17 @@
 from accelerate import Accelerator, DistributedType
 
 NUM_SECONDS_TO_SLEEP = 30
-eval_logger = logging.getLogger("lmms-eval")
+
+from loguru import logger
+
+eval_logger = logger
 
 try:
     from reka.client import Reka as RekaClient
     from reka import ChatMessage
     from decord import VideoReader, cpu
 except Exception as e:
-    eval_logger.error(f"Error importing reka: {e}")
+    eval_logger.warning(f"Error importing reka: {e}")
 
 
 @register_model("reka")
diff --git a/lmms_eval/models/tinyllava.py b/lmms_eval/models/tinyllava.py
index e2ca4bdf..1cb6d281 100755
--- a/lmms_eval/models/tinyllava.py
+++ b/lmms_eval/models/tinyllava.py
@@ -2,7 +2,6 @@
 
 torch.backends.cuda.matmul.allow_tf32 = True
 
-import logging
 import copy
 from tqdm import tqdm
 from datetime import timedelta
@@ -21,12 +20,10 @@
 
 warnings.filterwarnings("ignore")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 try:
-    from tinyllava.model import load_pretrained_model
     from tinyllava.data import ImagePreprocess, TextPreprocess
-    from tinyllava.utils.constants import DEFAULT_IMAGE_TOKEN
     from tinyllava.utils.message import Message
 except Exception as e:
     eval_logger.debug("TinyLLaVA_Factory is not installed. Please install TinyLLaVA_Factory to use this model.\nError: %s" % e)
@@ -368,8 +365,6 @@ def _collate(x):
             attention_masks = input_ids.ne(pad_token_ids).to(self.device)
             # These steps are not in LLaVA's original code, but are necessary for generation to work
             # TODO: attention to this major generation step...
-            if "image_aspect_ratio" in gen_kwargs.keys():
-                gen_kwargs.pop("image_aspect_ratio")
             try:
                 cont = self.model.generate(
                     input_ids,
diff --git a/lmms_eval/models/video_chatgpt.py b/lmms_eval/models/video_chatgpt.py
index a724cd98..089a2c5d 100644
--- a/lmms_eval/models/video_chatgpt.py
+++ b/lmms_eval/models/video_chatgpt.py
@@ -1,5 +1,4 @@
 import os
-from lmms_eval import utils
 from lmms_eval.api.instance import Instance
 from lmms_eval.api.model import lmms
 from lmms_eval.api.registry import register_model
@@ -11,21 +10,21 @@
 from PIL import Image
 
 from datetime import timedelta
-import logging
 from typing import List, Tuple, Optional, Union
 from tqdm import tqdm
 
+from loguru import logger
+
+eval_logger = logger
+
 try:
     from lmms_eval.models.video_chatgpt.eval.model_utils import load_video, initialize_model
     from lmms_eval.models.video_chatgpt.inference import video_chatgpt_infer, video_chatgpt_infer_ppl, get_spatio_temporal_features_torch
 except ImportError:
-    eval_logger = logging.getLogger("lmms-eval")
-    eval_logger.info("Failed to import video_chatgpt modules")
+    eval_logger.warning("Failed to import video_chatgpt modules")
 
 from lmms_eval.models.model_utils.load_video import read_video_pyav
 
-eval_logger = logging.getLogger("lmms-eval")
-
 
 @register_model("video_chatgpt")
 class VideoChatGPT(lmms):
diff --git a/lmms_eval/models/video_chatgpt/utils.py b/lmms_eval/models/video_chatgpt/utils.py
index eb6fa78e..1c8b4c3f 100644
--- a/lmms_eval/models/video_chatgpt/utils.py
+++ b/lmms_eval/models/video_chatgpt/utils.py
@@ -1,94 +1,3 @@
-import logging
-import logging.handlers
-import os
-import sys
-
-import requests
-
-from lmms_eval.models.video_chatgpt.constants import LOGDIR
-
-server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
-moderation_msg = "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN."
-
-handler = None
-
-
-def build_logger(logger_name, logger_filename):
-    global handler
-
-    formatter = logging.Formatter(
-        fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-        datefmt="%Y-%m-%d %H:%M:%S",
-    )
-
-    # Set the format of root handlers
-    if not logging.getLogger().handlers:
-        logging.basicConfig(level=logging.INFO)
-    logging.getLogger().handlers[0].setFormatter(formatter)
-
-    # Redirect stdout and stderr to loggers
-    stdout_logger = logging.getLogger("stdout")
-    stdout_logger.setLevel(logging.INFO)
-    sl = StreamToLogger(stdout_logger, logging.INFO)
-    sys.stdout = sl
-
-    stderr_logger = logging.getLogger("stderr")
-    stderr_logger.setLevel(logging.ERROR)
-    sl = StreamToLogger(stderr_logger, logging.ERROR)
-    sys.stderr = sl
-
-    # Get logger
-    logger = logging.getLogger(logger_name)
-    logger.setLevel(logging.INFO)
-
-    # Add a file handler for all loggers
-    if handler is None:
-        os.makedirs(LOGDIR, exist_ok=True)
-        filename = os.path.join(LOGDIR, logger_filename)
-        handler = logging.handlers.TimedRotatingFileHandler(filename, when="D", utc=True)
-        handler.setFormatter(formatter)
-
-        for name, item in logging.root.manager.loggerDict.items():
-            if isinstance(item, logging.Logger):
-                item.addHandler(handler)
-
-    return logger
-
-
-class StreamToLogger(object):
-    """
-    Fake file-like stream object that redirects writes to a logger instance.
-    """
-
-    def __init__(self, logger, log_level=logging.INFO):
-        self.terminal = sys.stdout
-        self.logger = logger
-        self.log_level = log_level
-        self.linebuf = ""
-
-    def __getattr__(self, attr):
-        return getattr(self.terminal, attr)
-
-    def write(self, buf):
-        temp_linebuf = self.linebuf + buf
-        self.linebuf = ""
-        for line in temp_linebuf.splitlines(True):
-            # From the io.TextIOWrapper docs:
-            #   On output, if newline is None, any '\n' characters written
-            #   are translated to the system default line separator.
-            # By default sys.stdout.write() expects '\n' newlines and then
-            # translates them so this is still cross platform.
-            if line[-1] == "\n":
-                self.logger.log(self.log_level, line.rstrip())
-            else:
-                self.linebuf += line
-
-    def flush(self):
-        if self.linebuf != "":
-            self.logger.log(self.log_level, self.linebuf.rstrip())
-        self.linebuf = ""
-
-
 def disable_torch_init():
     """
     Disable the redundant torch default initialization to accelerate model creation.
@@ -97,29 +6,3 @@ def disable_torch_init():
 
     setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
     setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
-
-
-def violates_moderation(text):
-    """
-    Check whether the text violates OpenAI moderation API.
-    """
-    url = "https://api.openai.com/v1/moderations"
-    headers = {"Content-Type": "application/json", "Authorization": "Bearer " + os.environ["OPENAI_API_KEY"]}
-    text = text.replace("\n", "")
-    data = "{" + '"input": ' + f'"{text}"' + "}"
-    data = data.encode("utf-8")
-    try:
-        ret = requests.post(url, headers=headers, data=data, timeout=5)
-        flagged = ret.json()["results"][0]["flagged"]
-    except requests.exceptions.RequestException as e:
-        flagged = False
-    except KeyError as e:
-        flagged = False
-
-    return flagged
-
-
-def pretty_print_semaphore(semaphore):
-    if semaphore is None:
-        return "None"
-    return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})"
diff --git a/lmms_eval/models/video_llava.py b/lmms_eval/models/video_llava.py
index 98acebfe..4c8d7058 100644
--- a/lmms_eval/models/video_llava.py
+++ b/lmms_eval/models/video_llava.py
@@ -1,4 +1,3 @@
-import logging
 from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
 from accelerate.state import AcceleratorState
 from typing import List, Optional, Union, Tuple
@@ -15,17 +14,9 @@
 from lmms_eval.api.registry import register_model
 from lmms_eval.utils import stop_sequences_criteria
 
-eval_logger = logging.getLogger("lmms-eval")
-
-# try:
-#     import torch
-#     from videollava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
-#     from videollava.conversation import conv_templates, SeparatorStyle
-#     from videollava.model.builder import load_pretrained_model
-#     from videollava.utils import disable_torch_init
-#     from videollava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
-# except ImportError:
-#     eval_logger.debug("Video-LLaVA is not installed. Please install Video-LLaVA to use this model.")
+from loguru import logger
+
+eval_logger = logger
 
 from transformers import VideoLlavaProcessor, VideoLlavaForConditionalGeneration
 from lmms_eval.models.model_utils.load_video import read_video_pyav
diff --git a/lmms_eval/models/xcomposer2_4KHD.py b/lmms_eval/models/xcomposer2_4KHD.py
index b43f12e4..6c4f81a7 100644
--- a/lmms_eval/models/xcomposer2_4KHD.py
+++ b/lmms_eval/models/xcomposer2_4KHD.py
@@ -5,7 +5,7 @@
 import numpy as np
 import torchvision.transforms as transforms
 from datetime import timedelta
-import logging
+
 
 from lmms_eval import utils
 from lmms_eval.api.instance import Instance
@@ -22,7 +22,7 @@
 
 pattern = re.compile(r"[A-Z]")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 meta_instruction = """You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔).
 - InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed\
diff --git a/lmms_eval/models/xcomposer2_4khd.py b/lmms_eval/models/xcomposer2_4khd.py
index b43f12e4..e741f637 100644
--- a/lmms_eval/models/xcomposer2_4khd.py
+++ b/lmms_eval/models/xcomposer2_4khd.py
@@ -5,7 +5,6 @@
 import numpy as np
 import torchvision.transforms as transforms
 from datetime import timedelta
-import logging
 
 from lmms_eval import utils
 from lmms_eval.api.instance import Instance
@@ -22,7 +21,7 @@
 
 pattern = re.compile(r"[A-Z]")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 meta_instruction = """You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔).
 - InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed\
diff --git a/lmms_eval/tasks/__init__.py b/lmms_eval/tasks/__init__.py
index 0679eae3..19f7fea2 100755
--- a/lmms_eval/tasks/__init__.py
+++ b/lmms_eval/tasks/__init__.py
@@ -1,4 +1,4 @@
-import os
+import os, sys
 from typing import List, Union, Dict
 
 from lmms_eval import utils
@@ -13,9 +13,9 @@
     ALL_TASKS,
 )
 
-import logging
+from loguru import logger
 
-eval_logger = logging.getLogger("lmms-eval")
+eval_logger = logger
 
 
 def register_configurable_task(config: Dict[str, str]) -> int:
@@ -109,7 +109,9 @@ def include_path(task_dir):
 
 
 def initialize_tasks(verbosity="INFO"):
-    eval_logger.setLevel(getattr(logging, f"{verbosity}"))
+    logger.remove()
+    eval_logger.add(sys.stdout, colorize=True, level=verbosity)
+    eval_logger.add(sys.stderr, level=verbosity)
     task_dir = os.path.dirname(os.path.abspath(__file__)) + "/"
     include_path(task_dir)
 
diff --git a/lmms_eval/tasks/activitynetqa/utils.py b/lmms_eval/tasks/activitynetqa/utils.py
index 7c236e92..0c77ff79 100755
--- a/lmms_eval/tasks/activitynetqa/utils.py
+++ b/lmms_eval/tasks/activitynetqa/utils.py
@@ -5,7 +5,7 @@
 import datetime
 import lmms_eval.tasks._task_utils.file_utils as file_utils
 import json
-import logging
+
 import yaml
 from pathlib import Path
 
@@ -15,7 +15,7 @@
 import time
 import ast
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 with open(Path(__file__).parent / "_default_template_yaml", "r") as f:
     raw_data = f.readlines()
@@ -239,7 +239,6 @@ def activitynetqa_gpt_eval(results, args):
 
 # Factory into different aggregate
 def activitynetqa_aggregate_score(results, args):
-
     yes_count = 0
     no_count = 0
     total_score = 0
diff --git a/lmms_eval/tasks/cmmmu/utils.py b/lmms_eval/tasks/cmmmu/utils.py
index 7f50a80b..a20eaa60 100755
--- a/lmms_eval/tasks/cmmmu/utils.py
+++ b/lmms_eval/tasks/cmmmu/utils.py
@@ -3,11 +3,11 @@
 import random
 import os
 import json
-import logging
+
 from collections import Counter
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 PROMPT = {
     "task_instructions": [
diff --git a/lmms_eval/tasks/coco_cap/utils.py b/lmms_eval/tasks/coco_cap/utils.py
index ab3a736a..edb0e31e 100755
--- a/lmms_eval/tasks/coco_cap/utils.py
+++ b/lmms_eval/tasks/coco_cap/utils.py
@@ -6,9 +6,8 @@
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 dir_name = os.path.dirname(os.path.abspath(__file__))
 
diff --git a/lmms_eval/tasks/conbench/utils.py b/lmms_eval/tasks/conbench/utils.py
index bf7e1090..6e7803b1 100644
--- a/lmms_eval/tasks/conbench/utils.py
+++ b/lmms_eval/tasks/conbench/utils.py
@@ -2,9 +2,8 @@
 import os
 from anls import anls_score
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 dir_name = os.path.dirname(os.path.abspath(__file__))
 
diff --git a/lmms_eval/tasks/cvrr/utils.py b/lmms_eval/tasks/cvrr/utils.py
index eef51ddc..b2eb25c4 100755
--- a/lmms_eval/tasks/cvrr/utils.py
+++ b/lmms_eval/tasks/cvrr/utils.py
@@ -4,7 +4,7 @@
 import datetime
 import lmms_eval.tasks._task_utils.file_utils as file_utils
 import json
-import logging
+
 import yaml
 from pathlib import Path
 
@@ -15,7 +15,7 @@
 import ast
 from tqdm import tqdm
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 with open(Path(__file__).parent / "_default_template_yaml", "r") as f:
     raw_data = f.readlines()
@@ -45,7 +45,6 @@
 # Pass in video path here
 # Can only work correctly with video llm
 def cvrr_doc_to_visual(doc):
-
     # Unzip all the zip files to HF HOME cache dir
     HF_HOME = os.environ["HF_HOME"]
     cache_dir = config["dataset_kwargs"]["cache_dir"]
diff --git a/lmms_eval/tasks/docvqa/utils.py b/lmms_eval/tasks/docvqa/utils.py
index 0a785d23..bb17aa03 100755
--- a/lmms_eval/tasks/docvqa/utils.py
+++ b/lmms_eval/tasks/docvqa/utils.py
@@ -1,10 +1,10 @@
 import json
 import os
-import logging
+
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-logger = logging.getLogger("lmms-eval")
+from loguru import logger
 
 
 def docvqa_doc_to_visual(doc):
diff --git a/lmms_eval/tasks/egoschema/utils.py b/lmms_eval/tasks/egoschema/utils.py
index 7278db34..34d2ab58 100755
--- a/lmms_eval/tasks/egoschema/utils.py
+++ b/lmms_eval/tasks/egoschema/utils.py
@@ -5,7 +5,7 @@
 import datetime
 import lmms_eval.tasks._task_utils.file_utils as file_utils
 import json
-import logging
+
 import yaml
 from pathlib import Path
 
@@ -27,7 +27,7 @@
 cache_dir = os.path.join(HF_HOME, cache_dir)
 cache_dir = os.path.join(cache_dir, "videos")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 # Pass in video path here
diff --git a/lmms_eval/tasks/ferret/utils.py b/lmms_eval/tasks/ferret/utils.py
index 7e86ea47..bb96b4a1 100755
--- a/lmms_eval/tasks/ferret/utils.py
+++ b/lmms_eval/tasks/ferret/utils.py
@@ -1,5 +1,5 @@
 import json
-import logging
+
 import os
 import requests
 import numpy as np
@@ -10,7 +10,8 @@
 from pathlib import Path
 from copy import deepcopy
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
+
 NUM_SECONDS_TO_SLEEP = 0.5
 
 FERRET_W_METRICS = ["gpt_eval_ferret_refer_desc", "gpt_eval_ferret_refer_reason", "gpt_eval_ferret_ground_conv"]
diff --git a/lmms_eval/tasks/flickr30k/utils.py b/lmms_eval/tasks/flickr30k/utils.py
index 8fa1069a..bc470f4d 100755
--- a/lmms_eval/tasks/flickr30k/utils.py
+++ b/lmms_eval/tasks/flickr30k/utils.py
@@ -6,9 +6,8 @@
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 import datetime
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 dir_name = os.path.dirname(os.path.abspath(__file__))
 
diff --git a/lmms_eval/tasks/hallusion_bench/evaluate_hb.py b/lmms_eval/tasks/hallusion_bench/evaluate_hb.py
index 87c65519..f35c67a2 100755
--- a/lmms_eval/tasks/hallusion_bench/evaluate_hb.py
+++ b/lmms_eval/tasks/hallusion_bench/evaluate_hb.py
@@ -1,6 +1,6 @@
 import os
 import json
-import logging
+
 from tqdm import tqdm
 
 from lmms_eval.tasks.hallusion_bench.utils import evaluate_by_chatgpt, check_same_by_chatgpt, assign_correctness, get_eval_all, get_eval_fig, get_eval_pair_all
@@ -11,7 +11,7 @@
 
 metric = ["aAcc", "fAcc", "qAcc"]
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def hb_doc_to_text(doc, model_specific_prompt_kwargs=None):
diff --git a/lmms_eval/tasks/hallusion_bench/utils.py b/lmms_eval/tasks/hallusion_bench/utils.py
index 1d8dfdaf..f0ea9c86 100755
--- a/lmms_eval/tasks/hallusion_bench/utils.py
+++ b/lmms_eval/tasks/hallusion_bench/utils.py
@@ -7,7 +7,7 @@
 import openai
 import threading
 import requests
-import logging
+
 
 API_TYPE = os.getenv("API_TYPE", "openai")
 
@@ -26,7 +26,7 @@
         "Content-Type": "application/json",
     }
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def evaluate_by_chatgpt(data, output_entry, correctness_entry, gpt_model="gpt-4", load_json=False, save_json_path="./hallusion_output.json", retries=3):
diff --git a/lmms_eval/tasks/ii_bench/utils.py b/lmms_eval/tasks/ii_bench/utils.py
index 6d645b29..8b30c12a 100755
--- a/lmms_eval/tasks/ii_bench/utils.py
+++ b/lmms_eval/tasks/ii_bench/utils.py
@@ -1,9 +1,10 @@
 import json
-import logging
 import re
 from collections import Counter
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
+from loguru import logger
+
 PROMPT = """Question: {}
 (A) {}
 (B) {}
@@ -68,4 +69,4 @@ def ii_bench_aggregate_submissions(results, args):
     file = generate_submission_file("ii_bench_test_for_submission.json", args)
     with open(file, "w") as f:
         json.dump(results, f, indent=4)
-    logging.getLogger("lmms-eval").info(f"Results saved to {file}")
+    logger.info(f"Results saved to {file}")
diff --git a/lmms_eval/tasks/infovqa/utils.py b/lmms_eval/tasks/infovqa/utils.py
index a4b1ab5c..9907a6cb 100755
--- a/lmms_eval/tasks/infovqa/utils.py
+++ b/lmms_eval/tasks/infovqa/utils.py
@@ -1,11 +1,10 @@
 import json
 import os
-import logging
 
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-lmms_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def infovqa_doc_to_visual(doc):
@@ -30,4 +29,4 @@ def infovqa_test_aggregate_results(results, args):
     file = generate_submission_file("infovqa_test_for_submission.json", args)
     with open(file, "w") as f:
         json.dump(results, f)
-    lmms_logger.info(f"Results saved to {file}")
+    eval_logger.info(f"Results saved to {file}")
diff --git a/lmms_eval/tasks/internal_eval/d170_cn_utils.py b/lmms_eval/tasks/internal_eval/d170_cn_utils.py
index 060229d7..aeed548c 100755
--- a/lmms_eval/tasks/internal_eval/d170_cn_utils.py
+++ b/lmms_eval/tasks/internal_eval/d170_cn_utils.py
@@ -1,7 +1,7 @@
 import os
 import requests
 import time
-import logging
+
 import yaml
 from pathlib import Path
 import re
@@ -9,7 +9,7 @@
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 with open(Path(__file__).parent / "d170_cn.yaml", "r") as f:
     raw_data = f.readlines()
diff --git a/lmms_eval/tasks/internal_eval/d170_en_utils.py b/lmms_eval/tasks/internal_eval/d170_en_utils.py
index b96017b4..5bf2b9d9 100755
--- a/lmms_eval/tasks/internal_eval/d170_en_utils.py
+++ b/lmms_eval/tasks/internal_eval/d170_en_utils.py
@@ -1,7 +1,7 @@
 import os
 import requests
 import time
-import logging
+
 import yaml
 from pathlib import Path
 import re
@@ -9,7 +9,7 @@
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 with open(Path(__file__).parent / "d170_en.yaml", "r") as f:
     raw_data = f.readlines()
diff --git a/lmms_eval/tasks/internal_eval/dc100_en_utils.py b/lmms_eval/tasks/internal_eval/dc100_en_utils.py
index 5e2b2a33..4fd4d79b 100755
--- a/lmms_eval/tasks/internal_eval/dc100_en_utils.py
+++ b/lmms_eval/tasks/internal_eval/dc100_en_utils.py
@@ -1,7 +1,7 @@
 import base64
 import requests
 import re
-import logging
+
 import time
 import os
 import yaml
@@ -18,7 +18,7 @@ def doc_to_visual(doc):
     return [doc["image"].convert("RGB")]
 
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 # Assuming the config is loaded similarly as in d170_en/utils.py
 with open(Path(__file__).parent / "dc100_en.yaml", "r") as f:
diff --git a/lmms_eval/tasks/internal_eval/dc200_cn_utils.py b/lmms_eval/tasks/internal_eval/dc200_cn_utils.py
index f5d7e87d..3549b22a 100755
--- a/lmms_eval/tasks/internal_eval/dc200_cn_utils.py
+++ b/lmms_eval/tasks/internal_eval/dc200_cn_utils.py
@@ -1,7 +1,7 @@
 import base64
 import requests
 import re
-import logging
+
 import os
 import yaml
 import json
@@ -18,7 +18,7 @@ def doc_to_visual(doc):
     return [doc["image"].convert("RGB")]
 
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 # Assuming the config is loaded similarly as in d170_en/utils.py
 with open(Path(__file__).parent / "dc200_cn.yaml", "r") as f:
diff --git a/lmms_eval/tasks/llava-bench-coco/utils.py b/lmms_eval/tasks/llava-bench-coco/utils.py
index 8858637f..08bf2515 100755
--- a/lmms_eval/tasks/llava-bench-coco/utils.py
+++ b/lmms_eval/tasks/llava-bench-coco/utils.py
@@ -1,5 +1,5 @@
 import json
-import logging
+
 import os
 import requests
 import numpy as np
@@ -10,7 +10,8 @@
 from pathlib import Path
 from copy import deepcopy
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
+
 NUM_SECONDS_TO_SLEEP = 0.5
 
 LLAVA_W_METRICS = ["gpt_eval_llava_conv", "gpt_eval_llava_detail", "gpt_eval_llava_complex"]
diff --git a/lmms_eval/tasks/llava-in-the-wild/utils.py b/lmms_eval/tasks/llava-in-the-wild/utils.py
index ac86ee99..25bf37a3 100755
--- a/lmms_eval/tasks/llava-in-the-wild/utils.py
+++ b/lmms_eval/tasks/llava-in-the-wild/utils.py
@@ -1,5 +1,5 @@
 import json
-import logging
+
 import os
 import requests
 import numpy as np
@@ -10,7 +10,8 @@
 from pathlib import Path
 from copy import deepcopy
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
+
 NUM_SECONDS_TO_SLEEP = 5
 
 LLAVA_W_METRICS = ["gpt_eval_llava_conv", "gpt_eval_llava_detail", "gpt_eval_llava_complex"]
diff --git a/lmms_eval/tasks/llava_wilder/llava_wilder_full.yaml b/lmms_eval/tasks/llava_wilder/llava_wilder_full.yaml
deleted file mode 100755
index 65627d03..00000000
--- a/lmms_eval/tasks/llava_wilder/llava_wilder_full.yaml
+++ /dev/null
@@ -1,14 +0,0 @@
-dataset_path: lmms-lab/llava-wilder
-dataset_name: Full
-dataset_kwargs:
-  token: True
-task: "llava_wilder_full"
-test_split: test
-model_specific_prompt_kwargs:
-  default:
-    pre_prompt: ""
-    post_prompt: ""
-  xcomposer2_4khd:
-    pre_prompt: "[UNUSED_TOKEN_146]user\nQuestion: "
-    post_prompt: "[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
-include: _default_template_wilder_yaml
\ No newline at end of file
diff --git a/lmms_eval/tasks/llava_wilder/llava_wilder_medium.yaml b/lmms_eval/tasks/llava_wilder/llava_wilder_medium.yaml
deleted file mode 100644
index 90f7bcc2..00000000
--- a/lmms_eval/tasks/llava_wilder/llava_wilder_medium.yaml
+++ /dev/null
@@ -1,14 +0,0 @@
-dataset_path: lmms-lab/llava-wilder
-dataset_name: Medium
-dataset_kwargs:
-  token: True
-task: "llava_wilder_medium"
-test_split: test
-model_specific_prompt_kwargs:
-  default:
-    pre_prompt: ""
-    post_prompt: ""
-  xcomposer2_4khd:
-    pre_prompt: "[UNUSED_TOKEN_146]user\nQuestion: "
-    post_prompt: "[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
-include: _default_template_wilder_yaml
\ No newline at end of file
diff --git a/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml b/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml
index 54b18897..1c493673 100644
--- a/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml
+++ b/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml
@@ -1,9 +1,8 @@
-dataset_path: lmms-lab/llava-wilder
-dataset_name: Small
+dataset_path: lmms-lab/llava-bench-wilder
 dataset_kwargs:
   token: True
 task: "llava_wilder_small"
-test_split: train 
+test_split: small
 model_specific_prompt_kwargs:
   default:
     pre_prompt: ""
diff --git a/lmms_eval/tasks/llava_wilder/utils.py b/lmms_eval/tasks/llava_wilder/utils.py
index 15d44438..96a020bc 100644
--- a/lmms_eval/tasks/llava_wilder/utils.py
+++ b/lmms_eval/tasks/llava_wilder/utils.py
@@ -3,7 +3,7 @@
 import os
 from pathlib import Path
 import requests
-import logging
+
 import time
 from copy import deepcopy
 import numpy as np
@@ -11,18 +11,7 @@
 from io import BytesIO
 
 # Set up a logger
-eval_logger = logging.getLogger("lmms-eval")
-
-# Create a static variable to track if the message has been logged
-if not hasattr(eval_logger, "dashcope_warning_logged"):
-    eval_logger.dashcope_warning_logged = False
-
-try:
-    import dashscope
-except ImportError:
-    if not eval_logger.dashcope_warning_logged:
-        eval_logger.debug("Dashcope not found, make sure you install dashscope to use qwen vl")
-        eval_logger.dashcope_warning_logged = True
+from loguru import logger as eval_logger
 
 NUM_SECONDS_TO_SLEEP = 5
 dir_path = os.path.dirname(os.path.realpath(__file__))
@@ -58,14 +47,6 @@
         "Content-Type": "application/json",
     }
 
-elif API_TYPE == "qwen_vl":
-    API_URL = os.getenv("QWEN_ENDPOINT", "https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation")
-    API_KEY = os.getenv("DASHSCOPE_API_KEY", "YOUR_API_KEY")
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json",
-    }
-
 
 def get_chat_response(base64_image, prompt, max_retries=5, wait_time=10):
     headers = {
@@ -114,29 +95,6 @@ def image_to_base64(pil_image):
     return base64.b64encode(buffered.getvalue()).decode("utf-8")
 
 
-def qwen_multimodal_conversation_call(text_content, image_content, retries=5):
-    """Simple single round multimodal conversation call."""
-    messages = [{"role": "user", "content": [{"image": image_content}, {"text": text_content}]}]
-    for attempt in range(retries):
-        try:
-            response_data = dashscope.MultiModalConversation.call(model=GPT_EVAL_MODEL_NAME, messages=messages)
-            # The response status_code is HTTPStatus.OK indicate success,
-            # otherwise indicate request is failed, you can get error code
-            # and message from code and message.
-            content = response_data["output"]["choices"][0]["message"]["content"][0]["text"].strip()
-            if content != "":
-                return content, GPT_EVAL_MODEL_NAME
-            break  # If successful, break out of the loop
-        except Exception as e:
-            eval_logger.info(f"Attempt {attempt + 1} failed with error: {e}")
-            if attempt < retries:  # If we have retries left, sleep and then continue to next attempt
-                time.sleep(NUM_SECONDS_TO_SLEEP)
-            else:  # If this was the last attempt, log and return empty
-                eval_logger.error(f"All {retries} attempts failed. Last error message: {e}")
-                return "", ""
-    return "", ""
-
-
 def parse_score(review):
     try:
         score_pair = review.split("\n")[0]
@@ -162,20 +120,13 @@ def llava_process_results(doc, result):
     """
     try:
         question = doc.get("question", "")
-        ans1 = doc.get("gpt4v_answer", "")
+        ans1 = doc.get("answer", "")
         ans2 = result[0] if result else ""
         content = f"[Question]\n{question}\n\n" + f"[Assistant 1]\n{ans1}\n\n[End of Assistant 1]\n\n" + f"[Assistant 2]\n{ans2}\n\n[End of Assistant 2]\n\n" f"[System]\n{judge_rules}\n\n"
         visuals = llava_doc_to_visual(doc)
-        if API_TYPE == "qwen_vl":
-            file_path = os.path.join(dir_path, f"tmp_{doc['question_id']}.jpg")
-            visuals[0].save(file_path)
-            image_content = "file://" + file_path
-            review, model_name = qwen_multimodal_conversation_call(content, image_content=image_content)
-            os.remove(file_path)
-        elif API_TYPE == "openai":
-            image_path = doc["image"]
-            base64_image = image_to_base64(image_path)
-            review, model_name = get_chat_response(base64_image, content)
+        image_path = doc["image"]
+        base64_image = image_to_base64(image_path)
+        review, model_name = get_chat_response(base64_image, content)
         scores = parse_score(review)
     except Exception as e:
         eval_logger.error(f"Error for Question ID: {doc.get('question_id', 'Unknown')}: {e}")
diff --git a/lmms_eval/tasks/longvideobench/utils.py b/lmms_eval/tasks/longvideobench/utils.py
index d189c8f0..b71e3086 100644
--- a/lmms_eval/tasks/longvideobench/utils.py
+++ b/lmms_eval/tasks/longvideobench/utils.py
@@ -1,5 +1,4 @@
 import json
-import logging
 import re
 from collections import Counter, defaultdict
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
@@ -13,7 +12,6 @@
 from PIL import Image
 import torch
 
-import logging
 from pathlib import Path
 import yaml
 import sys
diff --git a/lmms_eval/tasks/mathverse/mathverse_evals.py b/lmms_eval/tasks/mathverse/mathverse_evals.py
index 71843a2a..5894f6f7 100644
--- a/lmms_eval/tasks/mathverse/mathverse_evals.py
+++ b/lmms_eval/tasks/mathverse/mathverse_evals.py
@@ -1,10 +1,10 @@
 import time
 import requests
-import logging
+
 from tqdm import tqdm
 import pandas as pd
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 DEMO_PROMPT_EXTRACT = """
 I am providing you a response from a model to a math problem, termed 'Model Response'. You should extract the answer from the response as 'Extracted Answer'. Directly output the extracted answer with no explanation.
diff --git a/lmms_eval/tasks/mathverse/utils.py b/lmms_eval/tasks/mathverse/utils.py
index 9ec613a7..de22d93d 100644
--- a/lmms_eval/tasks/mathverse/utils.py
+++ b/lmms_eval/tasks/mathverse/utils.py
@@ -1,11 +1,10 @@
-import logging
 import yaml
 import os
 from pathlib import Path
 import pandas as pd
 import json
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 from lmms_eval.tasks.mathverse.mathverse_evals import MathVerseEvaluator
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
diff --git a/lmms_eval/tasks/mathvista/mathvista_evals.py b/lmms_eval/tasks/mathvista/mathvista_evals.py
index 90d367ec..afc5b1b5 100755
--- a/lmms_eval/tasks/mathvista/mathvista_evals.py
+++ b/lmms_eval/tasks/mathvista/mathvista_evals.py
@@ -2,9 +2,9 @@
 import requests
 import re
 from Levenshtein import distance
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+
+from loguru import logger as eval_logger
 
 # pids: 799, 681, 615
 shot_examples = [
diff --git a/lmms_eval/tasks/mathvista/utils.py b/lmms_eval/tasks/mathvista/utils.py
index 3bbe96a9..4f8ad21b 100755
--- a/lmms_eval/tasks/mathvista/utils.py
+++ b/lmms_eval/tasks/mathvista/utils.py
@@ -1,11 +1,10 @@
-import logging
 import yaml
 import os
 from pathlib import Path
 import pandas as pd
 import json
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 from lmms_eval.tasks.mathvista.mathvista_evals import MathVistaEvaluator
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
diff --git a/lmms_eval/tasks/mmbench/cc_utils.py b/lmms_eval/tasks/mmbench/cc_utils.py
index a5b9326c..8cd6665d 100755
--- a/lmms_eval/tasks/mmbench/cc_utils.py
+++ b/lmms_eval/tasks/mmbench/cc_utils.py
@@ -1,11 +1,10 @@
-import logging
 import yaml
 import os
 from pathlib import Path
 import pandas as pd
 import json
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 from lmms_eval.tasks.mmbench.mmbench_evals import MMBench_Evaluator
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
diff --git a/lmms_eval/tasks/mmbench/cn_utils.py b/lmms_eval/tasks/mmbench/cn_utils.py
index ba076b6f..2a767d7a 100755
--- a/lmms_eval/tasks/mmbench/cn_utils.py
+++ b/lmms_eval/tasks/mmbench/cn_utils.py
@@ -1,4 +1,3 @@
-import logging
 import yaml
 import os
 from pathlib import Path
@@ -6,7 +5,7 @@
 import json
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 from lmms_eval.tasks.mmbench.mmbench_evals import MMBench_Evaluator
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
diff --git a/lmms_eval/tasks/mmbench/en_utils.py b/lmms_eval/tasks/mmbench/en_utils.py
index 60121ec5..f0b5e9f8 100755
--- a/lmms_eval/tasks/mmbench/en_utils.py
+++ b/lmms_eval/tasks/mmbench/en_utils.py
@@ -1,11 +1,10 @@
-import logging
 import yaml
 import os
 from pathlib import Path
 import pandas as pd
 import json
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 from lmms_eval.tasks.mmbench.mmbench_evals import MMBench_Evaluator
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
diff --git a/lmms_eval/tasks/mmbench/mmbench_evals.py b/lmms_eval/tasks/mmbench/mmbench_evals.py
index 7868157e..7f2c246f 100755
--- a/lmms_eval/tasks/mmbench/mmbench_evals.py
+++ b/lmms_eval/tasks/mmbench/mmbench_evals.py
@@ -10,9 +10,8 @@
 import pandas as pd
 from tqdm import tqdm
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 class MMBench_Evaluator:
diff --git a/lmms_eval/tasks/mme/utils.py b/lmms_eval/tasks/mme/utils.py
index 26d5942c..5aa4ae18 100755
--- a/lmms_eval/tasks/mme/utils.py
+++ b/lmms_eval/tasks/mme/utils.py
@@ -4,9 +4,8 @@
 import json
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 dir_name = os.path.dirname(os.path.abspath(__file__))
 
diff --git a/lmms_eval/tasks/mmmu/utils.py b/lmms_eval/tasks/mmmu/utils.py
index 5a1b91f6..83aeed20 100755
--- a/lmms_eval/tasks/mmmu/utils.py
+++ b/lmms_eval/tasks/mmmu/utils.py
@@ -5,11 +5,11 @@
 import numpy as np
 import os
 import json
-import logging
+
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-lmms_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 MULTI_CHOICE_PROMPT = "Answer with the option's letter from the given choices directly."
 OPEN_ENDED_PROMPT = "Answer the question using a single word or phrase."
@@ -86,9 +86,10 @@ def extract_subset_name(input_string):
 
 def mmmu_test_aggregate_results_for_submission(results, args):
     path = generate_submission_file("mmmu_test_for_submission.json", args)
+    results_dict = {list(item.keys())[0]: list(item.values())[0] for item in results}
     with open(path, "w") as f:
-        json.dump(results, f)
-    lmms_logger.info(f"Results saved to {path}.")
+        json.dump(results_dict, f)
+    eval_logger.info(f"Results saved to {path}.")
 
 
 def mmmu_aggregate_results(results):
diff --git a/lmms_eval/tasks/mmmu/utils_group_img.py b/lmms_eval/tasks/mmmu/utils_group_img.py
index 2470d7c7..5de728b6 100644
--- a/lmms_eval/tasks/mmmu/utils_group_img.py
+++ b/lmms_eval/tasks/mmmu/utils_group_img.py
@@ -5,7 +5,7 @@
 import numpy as np
 import os
 import json
-import logging
+
 import matplotlib.font_manager as fm
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
@@ -129,7 +129,7 @@ def process_images(images, size=1008):
         return concat_horizontal
 
 
-lmms_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 MULTI_CHOICE_PROMPT = "Answer with the option's letter from the given choices directly."
 OPEN_ENDED_PROMPT = "Answer the question using a single word or phrase."
@@ -209,7 +209,7 @@ def mmmu_test_aggregate_results_for_submission(results, args):
     path = generate_submission_file("mmmu_test_for_submission.json", args)
     with open(path, "w") as f:
         json.dump(results, f)
-    lmms_logger.info(f"Results saved to {path}.")
+    eval_logger.info(f"Results saved to {path}.")
 
 
 def mmmu_aggregate_results(results):
diff --git a/lmms_eval/tasks/mmupd/mmupd_evals.py b/lmms_eval/tasks/mmupd/mmupd_evals.py
index 53055313..00dde912 100644
--- a/lmms_eval/tasks/mmupd/mmupd_evals.py
+++ b/lmms_eval/tasks/mmupd/mmupd_evals.py
@@ -7,10 +7,10 @@
 import numpy as np
 import pandas as pd
 import pickle
-import logging
+
 import json
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def dump(data, f):
diff --git a/lmms_eval/tasks/mmupd/utils.py b/lmms_eval/tasks/mmupd/utils.py
index 9a90bf4b..bfc724a5 100644
--- a/lmms_eval/tasks/mmupd/utils.py
+++ b/lmms_eval/tasks/mmupd/utils.py
@@ -1,4 +1,3 @@
-import logging
 import yaml
 import os
 from pathlib import Path
@@ -10,7 +9,7 @@
 from lmms_eval.tasks.mmupd.mmupd_evals import MMUPD_Evaluator
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 with open(Path(__file__).parent / "mmupd.yaml", "r") as f:
     raw_data = f.readlines()
diff --git a/lmms_eval/tasks/mmvet/utils.py b/lmms_eval/tasks/mmvet/utils.py
index 5caaba46..37b15ad7 100755
--- a/lmms_eval/tasks/mmvet/utils.py
+++ b/lmms_eval/tasks/mmvet/utils.py
@@ -1,12 +1,12 @@
 import os
 import requests
 import time
-import logging
+
 import pandas as pd
 import yaml
 from pathlib import Path
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 with open(Path(__file__).parent / "mmvet.yaml", "r") as f:
     raw_data = f.readlines()
diff --git a/lmms_eval/tasks/multidocvqa/utils.py b/lmms_eval/tasks/multidocvqa/utils.py
index 10fd85e6..f53da020 100755
--- a/lmms_eval/tasks/multidocvqa/utils.py
+++ b/lmms_eval/tasks/multidocvqa/utils.py
@@ -2,11 +2,11 @@
 import re
 import ast
 import json
-import logging
+
 from lmms_eval.api.metrics import levenshtein_distance
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-lmms_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def multidocvqa_doc_to_text(doc, model_specific_prompt_kwargs):
@@ -55,7 +55,7 @@ def multidocvqa_test_aggregate_results_for_submission(results, args):
     path = generate_submission_file("multidocvqa_test_for_submission.json", args)
     with open(path, "w") as f:
         json.dump(results, f)
-    lmms_logger.info(f"Results saved to {path}.")
+    eval_logger.info(f"Results saved to {path}.")
 
 
 ##################
diff --git a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py
index 6666de45..b783cb40 100644
--- a/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py
+++ b/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py
@@ -1,5 +1,4 @@
 import json
-import logging
 import os
 import requests
 import numpy as np
@@ -10,7 +9,8 @@
 from pathlib import Path
 from copy import deepcopy
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
+
 NUM_SECONDS_TO_SLEEP = 5
 
 LLAVA_W_METRICS = ["gpt_eval_llava_conv", "gpt_eval_llava_detail", "gpt_eval_llava_complex"]
diff --git a/lmms_eval/tasks/nextqa/utils.py b/lmms_eval/tasks/nextqa/utils.py
index ca5d8bac..4fa46523 100644
--- a/lmms_eval/tasks/nextqa/utils.py
+++ b/lmms_eval/tasks/nextqa/utils.py
@@ -1,12 +1,12 @@
 import os
 import yaml
-import logging
+
 import random
 import pandas as pd
 
 from pathlib import Path
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 try:
     from pywsd.utils import lemmatize_sentence
diff --git a/lmms_eval/tasks/nocaps/utils.py b/lmms_eval/tasks/nocaps/utils.py
index 9b1d4df6..ce6e1d89 100755
--- a/lmms_eval/tasks/nocaps/utils.py
+++ b/lmms_eval/tasks/nocaps/utils.py
@@ -6,9 +6,8 @@
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 dir_name = os.path.dirname(os.path.abspath(__file__))
 
diff --git a/lmms_eval/tasks/ocrbench/utils.py b/lmms_eval/tasks/ocrbench/utils.py
index fe1c1fcb..b590cbda 100644
--- a/lmms_eval/tasks/ocrbench/utils.py
+++ b/lmms_eval/tasks/ocrbench/utils.py
@@ -1,8 +1,6 @@
-import logging
-
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-logger = logging.getLogger("lmms-eval")
+from loguru import logger
 
 # Add the following functions to your existing utils.py file
 OCRBench_score = {
diff --git a/lmms_eval/tasks/ok_vqa/utils.py b/lmms_eval/tasks/ok_vqa/utils.py
index f1bb7742..249e1a0c 100755
--- a/lmms_eval/tasks/ok_vqa/utils.py
+++ b/lmms_eval/tasks/ok_vqa/utils.py
@@ -3,14 +3,14 @@
 import json
 import yaml
 import pathlib
-import logging
+
 import datetime
 import statistics
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 from lmms_eval.tasks._task_utils.vqa_eval_metric import EvalAIAnswerProcessor
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def ok_vqa_doc_to_visual(doc):
diff --git a/lmms_eval/tasks/olympiadbench/cn_utils.py b/lmms_eval/tasks/olympiadbench/cn_utils.py
index 79411385..64578e8a 100644
--- a/lmms_eval/tasks/olympiadbench/cn_utils.py
+++ b/lmms_eval/tasks/olympiadbench/cn_utils.py
@@ -4,9 +4,9 @@
 from lmms_eval.tasks.olympiadbench.olympiadbench_evals import OlympiadBenchEvaluator
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
+
 dir_name = os.path.dirname(os.path.abspath(__file__))
 
 try:
diff --git a/lmms_eval/tasks/olympiadbench/en_utils.py b/lmms_eval/tasks/olympiadbench/en_utils.py
index c4191cdd..b3186c98 100644
--- a/lmms_eval/tasks/olympiadbench/en_utils.py
+++ b/lmms_eval/tasks/olympiadbench/en_utils.py
@@ -4,9 +4,9 @@
 from lmms_eval.tasks.olympiadbench.olympiadbench_evals import OlympiadBenchEvaluator
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
+
 dir_name = os.path.dirname(os.path.abspath(__file__))
 
 try:
diff --git a/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py b/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py
index fbda732c..9897447f 100644
--- a/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py
+++ b/lmms_eval/tasks/olympiadbench/olympiadbench_evals.py
@@ -1,9 +1,8 @@
 import re
 import sympy as sp
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 try:
     from sympy import simplify, Eq, sympify, Pow
diff --git a/lmms_eval/tasks/perceptiontest/test/utils.py b/lmms_eval/tasks/perceptiontest/test/utils.py
index 35307179..522a6a04 100755
--- a/lmms_eval/tasks/perceptiontest/test/utils.py
+++ b/lmms_eval/tasks/perceptiontest/test/utils.py
@@ -5,7 +5,7 @@
 import datetime
 import lmms_eval.tasks._task_utils.file_utils as file_utils
 import json
-import logging
+
 import yaml
 from pathlib import Path
 
@@ -27,7 +27,7 @@
 cache_dir = os.path.join(HF_HOME, cache_dir)
 cache_dir = os.path.join(cache_dir, "videos")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 # Pass in video path here
diff --git a/lmms_eval/tasks/perceptiontest/val/utils.py b/lmms_eval/tasks/perceptiontest/val/utils.py
index ceb05784..562cc0fe 100755
--- a/lmms_eval/tasks/perceptiontest/val/utils.py
+++ b/lmms_eval/tasks/perceptiontest/val/utils.py
@@ -5,7 +5,7 @@
 import datetime
 import lmms_eval.tasks._task_utils.file_utils as file_utils
 import json
-import logging
+
 import yaml
 from pathlib import Path
 
@@ -27,7 +27,7 @@
 cache_dir = os.path.join(HF_HOME, cache_dir)
 cache_dir = os.path.join(cache_dir, "videos")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 # Pass in video path here
diff --git a/lmms_eval/tasks/qbench/utils.py b/lmms_eval/tasks/qbench/utils.py
index 609efb00..a5641570 100644
--- a/lmms_eval/tasks/qbench/utils.py
+++ b/lmms_eval/tasks/qbench/utils.py
@@ -1,5 +1,4 @@
 import json
-import logging
 import re
 from collections import Counter, defaultdict
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
diff --git a/lmms_eval/tasks/refcoco+/utils.py b/lmms_eval/tasks/refcoco+/utils.py
index f1d43606..c71ddf86 100755
--- a/lmms_eval/tasks/refcoco+/utils.py
+++ b/lmms_eval/tasks/refcoco+/utils.py
@@ -5,9 +5,8 @@
 
 COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"]  # , "SPICE"]
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def refcoco_bbox_doc_to_visual(doc):
diff --git a/lmms_eval/tasks/refcoco/utils.py b/lmms_eval/tasks/refcoco/utils.py
index f1d43606..c71ddf86 100755
--- a/lmms_eval/tasks/refcoco/utils.py
+++ b/lmms_eval/tasks/refcoco/utils.py
@@ -5,9 +5,8 @@
 
 COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"]  # , "SPICE"]
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def refcoco_bbox_doc_to_visual(doc):
diff --git a/lmms_eval/tasks/refcocog/utils.py b/lmms_eval/tasks/refcocog/utils.py
index f1d43606..c71ddf86 100755
--- a/lmms_eval/tasks/refcocog/utils.py
+++ b/lmms_eval/tasks/refcocog/utils.py
@@ -5,9 +5,8 @@
 
 COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"]  # , "SPICE"]
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def refcoco_bbox_doc_to_visual(doc):
diff --git a/lmms_eval/tasks/screenspot/utils.py b/lmms_eval/tasks/screenspot/utils.py
index d5d53df5..68a81f95 100644
--- a/lmms_eval/tasks/screenspot/utils.py
+++ b/lmms_eval/tasks/screenspot/utils.py
@@ -6,9 +6,8 @@
 # COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"]  # , "SPICE"]
 COCO_METRICS = ["CIDEr"]
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def screenspot_bbox_doc_to_visual(doc):
diff --git a/lmms_eval/tasks/screenspot/utils_rec.py b/lmms_eval/tasks/screenspot/utils_rec.py
index 09539022..1aebf2f1 100644
--- a/lmms_eval/tasks/screenspot/utils_rec.py
+++ b/lmms_eval/tasks/screenspot/utils_rec.py
@@ -1,8 +1,6 @@
 import re
-import logging
 from datasets import Dataset
-
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 REC_METRICS = ["IoU", "ACC@0.1", "ACC@0.3", "ACC@0.5", "ACC@0.7", "ACC@0.9", "Center_ACC"]
 
diff --git a/lmms_eval/tasks/stvqa/utils.py b/lmms_eval/tasks/stvqa/utils.py
index 81fca5b4..ff033f1f 100755
--- a/lmms_eval/tasks/stvqa/utils.py
+++ b/lmms_eval/tasks/stvqa/utils.py
@@ -1,6 +1,5 @@
-import os
 import json
-import logging
+from loguru import logger
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
@@ -25,4 +24,4 @@ def stvqa_aggregate_submissions(results, args):
     file = generate_submission_file("stvqa_test_for_submission.json", args)
     with open(file, "w") as f:
         json.dump(results, f)
-    logging.getLogger("lmms-eval").info(f"Results saved to {file}")
+    logger.info(f"Results saved to {file}")
diff --git a/lmms_eval/tasks/synthdog/donut_evaluator.py b/lmms_eval/tasks/synthdog/donut_evaluator.py
index dd9b022f..4059cab9 100644
--- a/lmms_eval/tasks/synthdog/donut_evaluator.py
+++ b/lmms_eval/tasks/synthdog/donut_evaluator.py
@@ -11,9 +11,7 @@
 from transformers.modeling_utils import PreTrainedModel
 
 
-import logging
-
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 try:
     import zss
diff --git a/lmms_eval/tasks/synthdog/utils.py b/lmms_eval/tasks/synthdog/utils.py
index 42e7c62d..274daa42 100644
--- a/lmms_eval/tasks/synthdog/utils.py
+++ b/lmms_eval/tasks/synthdog/utils.py
@@ -1,9 +1,8 @@
-import logging
 import json
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 from lmms_eval.tasks.synthdog.donut_evaluator import JSONParseEvaluator
 
-logger = logging.getLogger("lmms-eval")
+from loguru import logger
 
 evaluator = JSONParseEvaluator()
 
diff --git a/lmms_eval/tasks/tempcompass/utils.py b/lmms_eval/tasks/tempcompass/utils.py
index 83574347..dbe4f831 100644
--- a/lmms_eval/tasks/tempcompass/utils.py
+++ b/lmms_eval/tasks/tempcompass/utils.py
@@ -5,7 +5,7 @@
 import datetime
 import lmms_eval.tasks._task_utils.file_utils as file_utils
 import json
-import logging
+
 import yaml
 from pathlib import Path
 
@@ -48,7 +48,7 @@
 cache_dir = os.path.join(HF_HOME, cache_dir)
 cache_dir = os.path.join(cache_dir, "videos")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 # Pass in video path here
@@ -365,7 +365,6 @@ def tempcompass_process_results_captioning(doc, result):
 
 # utils functions for captioning: parse gpt outputs
 def parse_llm_output_for_captioning(llm_output, gt_answer):
-
     if llm_output == "invalid_request_error" or not llm_output:
         eval_result = {"rating": -1, "chatgpt-answer": None, "chatgpt-reasoning": None}
         return eval_result
@@ -461,7 +460,6 @@ def eval_rule(video_llm_output, question, answer):
 
 # utils function for yes_no
 def extract_pred(video_llm_output):
-
     # Extract the yes/no predction from the original video llm output
     video_llm_output = video_llm_output.lower()
     if video_llm_output.startswith("yes"):
diff --git a/lmms_eval/tasks/textcaps/utils.py b/lmms_eval/tasks/textcaps/utils.py
index 12dc277f..d21a92fd 100755
--- a/lmms_eval/tasks/textcaps/utils.py
+++ b/lmms_eval/tasks/textcaps/utils.py
@@ -5,9 +5,8 @@
 from pycocotools.coco import COCO
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 dir_name = os.path.dirname(os.path.abspath(__file__))
 
diff --git a/lmms_eval/tasks/textvqa/utils.py b/lmms_eval/tasks/textvqa/utils.py
index 71a4ccaa..0c8de47d 100755
--- a/lmms_eval/tasks/textvqa/utils.py
+++ b/lmms_eval/tasks/textvqa/utils.py
@@ -3,14 +3,14 @@
 import json
 import yaml
 import pathlib
-import logging
+
 import datetime
 import statistics
 
 from lmms_eval.tasks._task_utils.vqa_eval_metric import EvalAIAnswerProcessor
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def textvqa_doc_to_visual(doc):
diff --git a/lmms_eval/tasks/vatex/utils.py b/lmms_eval/tasks/vatex/utils.py
index 55ffa9c5..cdb62806 100755
--- a/lmms_eval/tasks/vatex/utils.py
+++ b/lmms_eval/tasks/vatex/utils.py
@@ -5,11 +5,11 @@
 from pycocotools.coco import COCO
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 from pathlib import Path
-import logging
+
 import yaml
 import sys
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 dir_name = os.path.dirname(os.path.abspath(__file__))
 
diff --git a/lmms_eval/tasks/vcr_wiki/utils.py b/lmms_eval/tasks/vcr_wiki/utils.py
index 95bdae1a..e16f99b4 100644
--- a/lmms_eval/tasks/vcr_wiki/utils.py
+++ b/lmms_eval/tasks/vcr_wiki/utils.py
@@ -1,6 +1,5 @@
 import datetime
 import json
-import logging
 import os
 from difflib import SequenceMatcher as SM
 from functools import partial
@@ -29,7 +28,8 @@
 nlp = {"en": nlp_en, "zh": nlp_zh}
 rouge = evaluate.load("rouge")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
+
 dir_name = os.path.dirname(os.path.abspath(__file__))
 
 aggregate_results_template = {
diff --git a/lmms_eval/tasks/video_detail_description/utils.py b/lmms_eval/tasks/video_detail_description/utils.py
index 382175cd..536776fb 100755
--- a/lmms_eval/tasks/video_detail_description/utils.py
+++ b/lmms_eval/tasks/video_detail_description/utils.py
@@ -6,7 +6,7 @@
 import datetime
 import lmms_eval.tasks._task_utils.file_utils as file_utils
 import json
-import logging
+
 import yaml
 from pathlib import Path
 
@@ -44,7 +44,7 @@
 cache_dir = os.path.join(HF_HOME, cache_dir)
 cache_dir = os.path.join(cache_dir, "Test_Videos")
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 # Pass in video path here
diff --git a/lmms_eval/tasks/videochatgpt/utils.py b/lmms_eval/tasks/videochatgpt/utils.py
index ba6cf07d..e59f2dbf 100755
--- a/lmms_eval/tasks/videochatgpt/utils.py
+++ b/lmms_eval/tasks/videochatgpt/utils.py
@@ -5,7 +5,7 @@
 import datetime
 import lmms_eval.tasks._task_utils.file_utils as file_utils
 import json
-import logging
+
 import yaml
 from pathlib import Path
 
@@ -16,7 +16,7 @@
 import ast
 from tqdm import tqdm
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 with open(Path(__file__).parent / "_default_template_yaml", "r") as f:
     raw_data = f.readlines()
diff --git a/lmms_eval/tasks/videomme/utils.py b/lmms_eval/tasks/videomme/utils.py
index ca821601..b70812a0 100644
--- a/lmms_eval/tasks/videomme/utils.py
+++ b/lmms_eval/tasks/videomme/utils.py
@@ -4,7 +4,7 @@
 import json
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 
-import logging
+
 from pathlib import Path
 import yaml
 import sys
@@ -13,7 +13,7 @@
 import cv2
 import numpy as np
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 VIDEO_TYPE = ["short", "medium", "long"]
 CATEGORIES = ["Knowledge", "Film & Television", "Sports Competition", "Artistic Performance", "Life Record", "Multilingual"]
@@ -93,44 +93,47 @@
 
 
 def parse_subtitle_time(time_str):
-    h, m, s_ms = time_str.split(':')
-    s, ms = s_ms.split(',')
+    h, m, s_ms = time_str.split(":")
+    s, ms = s_ms.split(",")
     return int(h) * 3600 + int(m) * 60 + int(s) + int(ms) / 1000
 
+
 def load_subtitles(subtitle_path):
     subtitles = {}
-    with open(subtitle_path, 'r', encoding='utf-8') as file:
-        content = file.read().split('\n\n')
+    with open(subtitle_path, "r", encoding="utf-8") as file:
+        content = file.read().split("\n\n")
         for section in content:
             if section.strip():
-                lines = section.split('\n')
+                lines = section.split("\n")
                 if len(lines) >= 3:
-                    time_range = lines[1].split(' --> ')
+                    time_range = lines[1].split(" --> ")
                     start_time = parse_subtitle_time(time_range[0])
                     end_time = parse_subtitle_time(time_range[1])
-                    text = ' '.join(line for line in lines[2:])
+                    text = " ".join(line for line in lines[2:])
                     subtitles[(start_time, end_time)] = text
     return subtitles
 
+
 def convert_time_to_frame(time_in_seconds, fps):
     return int(time_in_seconds * fps)
 
+
 def extract_subtitles(video_path, subtitle_path):
     video = cv2.VideoCapture(video_path)
     fps = video.get(cv2.CAP_PROP_FPS)
-    total_frame=int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    total_frame = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
     subtitles = load_subtitles(subtitle_path)
-    
+
     subtitle_frames = []
     for (start_time, end_time), text in subtitles.items():
         start_frame = convert_time_to_frame(start_time, fps)
         end_frame = convert_time_to_frame(end_time, fps)
         subtitle_frames.append((start_frame, end_frame, text))
 
-    return subtitle_frames,total_frame
+    return subtitle_frames, total_frame
 
-def videomme_doc_to_visual(doc):
 
+def videomme_doc_to_visual(doc):
     cache_dir = os.path.join(base_cache_dir, cache_name)
     video_path = doc["videoID"] + ".mp4"
     video_path = os.path.join(cache_dir, video_path)
@@ -146,14 +149,16 @@ def videomme_doc_to_visual(doc):
 
 
 def videomme_doc_to_text(doc, model_specific_prompt_kwargs=None):
-    option_prompt="Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option."
+    option_prompt = "Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option."
     question = doc["question"]
     option = str(doc["options"])
     question = question + "\n" + option
-    full_prompt=option_prompt+"\n"+question+"\n"+"The best answer is:"
+    full_prompt = option_prompt + "\n" + question + "\n" + "The best answer is:"
     return full_prompt
+
+
 # Frames + Subs
-# This video's subtitles are listed below: 
+# This video's subtitles are listed below:
 # 【subtitles】
 
 # Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option.
@@ -164,57 +169,58 @@ def videomme_doc_to_text(doc, model_specific_prompt_kwargs=None):
 # 【question】
 # The best answer is:
 
+
 def videomme_doc_to_text_subtitle(doc, model_specific_prompt_kwargs=None):
     cache_dir = os.path.join(base_cache_dir, cache_name)
     video_path = doc["videoID"] + ".mp4"
-    subtitle_path=os.path.join(cache_dir,"subtitle",doc["videoID"]+".srt")
+    subtitle_path = os.path.join(cache_dir, "subtitle", doc["videoID"] + ".srt")
     video_path = os.path.join(cache_dir, video_path)
-    if os.path.exists(subtitle_path): #Denote have subtitle
-        subtitle=open(subtitle_path).readlines()
+    if os.path.exists(subtitle_path):  # Denote have subtitle
+        subtitle = open(subtitle_path).readlines()
     else:
-        subtitle=""
-    subtitles_prompt="This video's subtitles are listed below: \n"
-    if subtitle=="":
-        subtitle="No subtitles available"
+        subtitle = ""
+    subtitles_prompt = "This video's subtitles are listed below: \n"
+    if subtitle == "":
+        subtitle = "No subtitles available"
     else:
-        if "gemini_api_flag" in model_specific_prompt_kwargs: #specific for gemini_api
-            if model_specific_prompt_kwargs['gemini_api_flag']=="full subtitle":
-                textlist=[]
+        if "gemini_api_flag" in model_specific_prompt_kwargs:  # specific for gemini_api
+            if model_specific_prompt_kwargs["gemini_api_flag"] == "full subtitle":
+                textlist = []
                 for ele in subtitle:
                     pattern = r'<font color="white" size=".72c">(.*?)</font>'
                     matches = re.findall(pattern, ele)
                     if matches:
                         textlist.append(matches[0])
-                subtitle_text="\n".join(textlist)
+                subtitle_text = "\n".join(textlist)
         else:
             if "frame_num" in model_specific_prompt_kwargs:
-                frame_num=model_specific_prompt_kwargs['frame_num']
-                subtitle_by_frame,total_frame=extract_subtitles(video_path,subtitle_path)
+                frame_num = model_specific_prompt_kwargs["frame_num"]
+                subtitle_by_frame, total_frame = extract_subtitles(video_path, subtitle_path)
                 uniform_sampled_frames = np.linspace(0, total_frame - 1, frame_num, dtype=int).tolist()
-                
-                subtitle_by_frame_idx=[]
+
+                subtitle_by_frame_idx = []
                 for frame_idx in uniform_sampled_frames:
-                    for idx,title in enumerate(subtitle_by_frame):
-                        if frame_idx<title[1] and frame_idx>=title[0]:
+                    for idx, title in enumerate(subtitle_by_frame):
+                        if frame_idx < title[1] and frame_idx >= title[0]:
                             subtitle_by_frame_idx.append(idx)
-                subtitle_by_frame_idx=list(set(subtitle_by_frame_idx))
+                subtitle_by_frame_idx = list(set(subtitle_by_frame_idx))
 
-                textlist=[]
+                textlist = []
                 for idx in subtitle_by_frame_idx:
                     pattern = r'<font color="white" size=".72c">(.*?)</font>'
-                    raw_text=re.findall(pattern, subtitle_by_frame[idx][2])
+                    raw_text = re.findall(pattern, subtitle_by_frame[idx][2])
                     try:
                         textlist.append(raw_text[0])
                     except:
                         continue
-                subtitle_text="\n".join(textlist)
-        subtitle=subtitle_text
+                subtitle_text = "\n".join(textlist)
+        subtitle = subtitle_text
 
-    option_prompt="Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option."
+    option_prompt = "Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option."
     question = doc["question"]
     option = str(doc["options"])
     question = question + "\n" + option
-    full_prompt=subtitles_prompt+subtitle+"\n"+option_prompt+"\n"+question+"\n"+"The best answer is:"
+    full_prompt = subtitles_prompt + subtitle + "\n" + option_prompt + "\n" + question + "\n" + "The best answer is:"
     return full_prompt
 
 
diff --git a/lmms_eval/tasks/vizwiz_vqa/utils.py b/lmms_eval/tasks/vizwiz_vqa/utils.py
index 9ff8d3d8..9be1f574 100755
--- a/lmms_eval/tasks/vizwiz_vqa/utils.py
+++ b/lmms_eval/tasks/vizwiz_vqa/utils.py
@@ -3,14 +3,14 @@
 import json
 import yaml
 import pathlib
-import logging
+
 import datetime
 import statistics
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
 from lmms_eval.tasks._task_utils.vqa_eval_metric import EvalAIAnswerProcessor
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def vizwiz_vqa_doc_to_visual(doc):
diff --git a/lmms_eval/tasks/vqav2/utils.py b/lmms_eval/tasks/vqav2/utils.py
index 0951712a..f70a3965 100755
--- a/lmms_eval/tasks/vqav2/utils.py
+++ b/lmms_eval/tasks/vqav2/utils.py
@@ -1,7 +1,7 @@
 import re
 import os
 import json
-import logging
+
 import datetime
 import statistics
 
@@ -10,7 +10,7 @@
 from lmms_eval.tasks._task_utils.vqa_eval_metric import EvalAIAnswerProcessor
 
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def vqav2_doc_to_visual(doc):
diff --git a/lmms_eval/tasks/websrc/utils.py b/lmms_eval/tasks/websrc/utils.py
index 5cc09e6d..3cd024ee 100644
--- a/lmms_eval/tasks/websrc/utils.py
+++ b/lmms_eval/tasks/websrc/utils.py
@@ -7,12 +7,10 @@
 import numpy as np
 import os
 import json
-import logging
 from PIL import Image
 
 from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
-
-lmms_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 OPEN_ENDED_PROMPT = "Answer the question using a single word or phrase."
 
@@ -63,7 +61,7 @@ def websrc_test_aggregate_results_for_submission(results, args):
         for result in results:
             out.update(result)
         json.dump(out, f, indent=4)
-    lmms_logger.info(f"Results saved to {path}.")
+    eval_logger.info(f"Results saved to {path}.")
 
 
 def websrc_aggregate_results(results):
diff --git a/lmms_eval/tasks/worldqa/utils.py b/lmms_eval/tasks/worldqa/utils.py
index aa528432..bd9b89ef 100755
--- a/lmms_eval/tasks/worldqa/utils.py
+++ b/lmms_eval/tasks/worldqa/utils.py
@@ -6,7 +6,7 @@
 from lmms_eval.filters.extraction import ExtendedRegexFilter
 from lmms_eval.tasks.worldqa.worldqa_mc_evaluator import WorldQA_MC_Evaluator
 import json
-import logging
+
 import yaml
 from pathlib import Path
 import requests
@@ -114,7 +114,7 @@ def get_eval(question: str, ground_truth: str, candidate: str, max_tokens: int,
 cache_dir = os.path.join(cache_dir, "videos")
 
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 # Pass in video path here
diff --git a/lmms_eval/tasks/worldqa/worldqa_mc_evaluator.py b/lmms_eval/tasks/worldqa/worldqa_mc_evaluator.py
index e1e2ae76..85e617c5 100644
--- a/lmms_eval/tasks/worldqa/worldqa_mc_evaluator.py
+++ b/lmms_eval/tasks/worldqa/worldqa_mc_evaluator.py
@@ -10,9 +10,8 @@
 import pandas as pd
 from tqdm import tqdm
 
-import logging
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 class WorldQA_MC_Evaluator:
diff --git a/lmms_eval/tasks/youcook2/utils.py b/lmms_eval/tasks/youcook2/utils.py
index 1761a8b1..f4e9af91 100644
--- a/lmms_eval/tasks/youcook2/utils.py
+++ b/lmms_eval/tasks/youcook2/utils.py
@@ -1,6 +1,6 @@
 import os
 import yaml
-import logging
+
 import string
 import random
 import numpy as np
@@ -15,7 +15,7 @@
 
 COCO_METRICS = ["Bleu_4", "Bleu_3", "Bleu_2", "Bleu_1", "METEOR", "ROUGE_L", "CIDEr"]  # , "SPICE"]
 
-eval_logger = logging.getLogger("lmms-eval")
+from loguru import logger as eval_logger
 
 
 def remove_nonascii(text):
diff --git a/lmms_eval/utils.py b/lmms_eval/utils.py
index 2e315fbe..80231c9c 100755
--- a/lmms_eval/utils.py
+++ b/lmms_eval/utils.py
@@ -35,36 +35,8 @@
 from jinja2 import BaseLoader, Environment, StrictUndefined
 from itertools import islice
 import pytz
-import logging
-
-
-class PathFormatter(logging.Formatter):
-    def __init__(self, fmt=None, datefmt=None, timezone="UTC"):
-        super().__init__(fmt, datefmt)
-        self.timezone = timezone
-
-    def formatTime(self, record, datefmt=None):
-        # Convert to Asia/Singapore timezone
-        ct = datetime.datetime.fromtimestamp(record.created, pytz.timezone(self.timezone))
-        if datefmt:
-            s = ct.strftime(datefmt)
-        else:
-            try:
-                s = ct.isoformat(timespec="milliseconds")
-            except TypeError:
-                s = ct.isoformat()
-        return s
-
-    def format(self, record):
-        # Extract the pathname from the record
-        pathname = record.pathname
-        # Split the pathname into folders
-        folders = pathname.split(os.sep)
-        # Get the last two folders and the filename
-        if len(folders) > 2:
-            record.pathname = os.sep.join(folders[-3:])
-        return super(PathFormatter, self).format(record)
 
+from loguru import logger as eval_logger
 
 SPACING = " " * 47
 
diff --git a/pyproject.toml b/pyproject.toml
index 5f3b92fb..c65d3b8c 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "lmms_eval"
-version = "0.2.0"
+version = "0.2.0.post1"
 authors = [
     { name = "LMMMs-Lab Evaluation Team", email = "lmms_eval@outlook.com" },
 ]
@@ -28,6 +28,7 @@ dependencies = [
     "httpx==0.23.3",
     "jsonlines",
     "numexpr",
+    "numpy==1.26.4",
     "peft>=0.2.0",
     "pybind11>=2.6.2",
     "pytablewriter",
@@ -50,9 +51,10 @@ dependencies = [
     "mpmath",
     "Jinja2",
     "openpyxl",
+    "loguru",
     "Levenshtein",
     "hf_transfer",
-    "tenacity",
+    "tenacity==8.3.0",
     "wandb>=0.16.0",
     "tiktoken",
     "pre-commit",
diff --git a/tools/make_image_hf_dataset.ipynb b/tools/make_image_hf_dataset.ipynb
new file mode 100755
index 00000000..89fa0a71
--- /dev/null
+++ b/tools/make_image_hf_dataset.ipynb
@@ -0,0 +1,292 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Contribute Your Models and Datasets\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/EvolvingLMMs-Lab/lmms-eval/blob/main/tools/make_image_hf_dataset.ipynb)\n",
+    "\n",
+    "This notebook will guide you to make correct format of Huggingface dataset, in proper parquet format and visualizable in Huggingface dataset hub.\n",
+    "\n",
+    "We will take the example of the dataset [`lmms-lab/VQAv2_TOY`](https://huggingface.co/datasets/lmms-lab/VQAv2_TOY) and convert it to the proper format."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preparation\n",
+    "\n",
+    "We need to install `datasets` library to create the dataset and `Pillow` to handle images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install datasets Pillow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we need to login into Hugging Face to upload the dataset. You should goto the [Hugging Face website](https://huggingface.co/settings/tokens) to get your API token."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!huggingface-cli login --token hf_YOUR_HF_TOKEN # replace hf_YOUR_HF_TOKEN to your own Hugging Face token."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "## Download Dataset\n",
+    "\n",
+    "We have uploaded the zip file of the dataset to [Hugging Face](https://huggingface.co/datasets/pufanyi/VQAv2_TOY/tree/main/source_data) for download. This dataset is a subset of the [VQAv2](https://visualqa.org/) dataset, with $20$ entries each from the `val`, `test`, and `test-dev` splits, for easier downloading."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!wget https://huggingface.co/datasets/lmms-lab/VQAv2_TOY/resolve/main/source_data/sample_data.zip -P data\n",
+    "!unzip data/sample_data.zip -d data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can open `data/questions` to take a view of the dataset organization. We found that the toy-`VQAv2` dataset is organized as follows:\n",
+    "\n",
+    "```json\n",
+    "{\n",
+    "    \"info\": { /* some infomation */ },\n",
+    "    \"task_type\": \"TASK_TYPE\", \"data_type\": \"mscoco\",\n",
+    "    \"license\": { /* some license */ },\n",
+    "    \"questions\": [\n",
+    "        {\n",
+    "            \"image_id\": 262144, // integer id of the image\n",
+    "            \"question\": \"Is the ball flying towards the batter?\",\n",
+    "            \"question_id\": 262144000\n",
+    "        },\n",
+    "        /* ... */\n",
+    "    ]\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Dataset Features _(Optional<sup>*</sup>)_\n",
+    "\n",
+    "You can define the features of the dataset. For more details, please refer to the [official documentation](https://huggingface.co/docs/datasets/en/about_dataset_features).\n",
+    "\n",
+    "<sup>*</sup> _Note that if the dataset features are consistent and all entries in your dataset table are non-null **for all splits of data**, you can skip this step._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datasets\n",
+    "\n",
+    "features = datasets.Features(\n",
+    "    {\n",
+    "        \"question_id\": datasets.Value(\"int64\"),\n",
+    "        \"question\": datasets.Value(\"string\"),\n",
+    "        \"image_id\": datasets.Value(\"string\"),\n",
+    "        \"image\": datasets.Image(),\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define Data Generator\n",
+    "\n",
+    "We use [`datasets.Dataset.from_generator`](https://huggingface.co/docs/datasets/v2.20.0/en/package_reference/main_classes#datasets.Dataset.from_generator) to create the dataset.\n",
+    "\n",
+    "The generator function should `yield` dictionaries with the keys corresponding to the dataset features. This can save memory when loading large datasets.\n",
+    "\n",
+    "For the image data, we can convert the image to [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html) object.\n",
+    "\n",
+    "Note that if some columns are missing in some splits of the dataset (for example, the `answer` column is usually missing in the `test` split), we need to set these columns to null to ensure that all splits have the same features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "from PIL import Image\n",
+    "\n",
+    "\n",
+    "def generator(qa_file, image_folder, image_prefix):\n",
+    "    with open(qa_file, \"r\") as f:\n",
+    "        data = json.load(f)\n",
+    "        qa = data[\"questions\"]\n",
+    "\n",
+    "    for q in qa:\n",
+    "        image_id = q[\"image_id\"]\n",
+    "        image_path = os.path.join(image_folder, f\"{image_prefix}_{image_id:012}.jpg\")\n",
+    "        q[\"image\"] = Image.open(image_path)\n",
+    "        yield q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate Dataset\n",
+    "\n",
+    "We generate the dataset using the generator function.\n",
+    "\n",
+    "Note that if you skip the step of defining dataset features, there is no need to pass the `features` argument. The dataset infer the features from the dataset automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NUM_PROC = 32  # number of processes to use for multiprocessing, set to 1 for no multiprocessing\n",
+    "\n",
+    "data_val = datasets.Dataset.from_generator(\n",
+    "    generator,\n",
+    "    gen_kwargs={\n",
+    "        \"qa_file\": \"data/questions/vqav2_toy_questions_val2014.json\",\n",
+    "        \"image_folder\": \"data/images\",\n",
+    "        \"image_prefix\": \"COCO_val2014\",\n",
+    "    },\n",
+    "    # For this dataset, there is no need to specify the features, as all cells are non-null and all splits have the same schema\n",
+    "    # features=features,\n",
+    "    num_proc=NUM_PROC,\n",
+    ")\n",
+    "\n",
+    "data_test = datasets.Dataset.from_generator(\n",
+    "    generator,\n",
+    "    gen_kwargs={\n",
+    "        \"qa_file\": \"data/questions/vqav2_toy_questions_test2015.json\",\n",
+    "        \"image_folder\": \"data/images\",\n",
+    "        \"image_prefix\": \"COCO_test2015\",\n",
+    "    },\n",
+    "    # features=features,\n",
+    "    num_proc=NUM_PROC,\n",
+    ")\n",
+    "\n",
+    "data_test_dev = datasets.Dataset.from_generator(\n",
+    "    generator,\n",
+    "    gen_kwargs={\n",
+    "        \"qa_file\": \"data/questions/vqav2_toy_questions_test-dev2015.json\",\n",
+    "        \"image_folder\": \"data/images\",\n",
+    "        \"image_prefix\": \"COCO_test2015\",\n",
+    "    },\n",
+    "    # features=features,\n",
+    "    num_proc=NUM_PROC,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset Upload\n",
+    "\n",
+    "Finally, we group the dataset with different splits and upload it to the Huggingface dataset hub."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = datasets.DatasetDict({\"val\": data_val, \"test\": data_test, \"test_dev\": data_test_dev})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.push_to_hub(\"lmms-lab/VQAv2_TOY\")  # replace lmms-lab to your username"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, you can check the dataset on the [Hugging Face dataset hub](https://huggingface.co/datasets/lmms-lab/VQAv2_TOY)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lmms-eval",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tools/make_hf_dataset.ipynb b/tools/make_video_hf_dataset.ipynb
similarity index 93%
rename from tools/make_hf_dataset.ipynb
rename to tools/make_video_hf_dataset.ipynb
index 9336331a..dd801eb8 100755
--- a/tools/make_hf_dataset.ipynb
+++ b/tools/make_video_hf_dataset.ipynb
@@ -162,26 +162,6 @@
     "dataset = Dataset.from_pandas(df_items, features=features)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Token is valid (permission: write).\n",
-      "Your token has been saved in your configured git credential helpers (store).\n",
-      "Your token has been saved to /home/tiger/.cache/huggingface/token\n",
-      "Login successful\n"
-     ]
-    }
-   ],
-   "source": [
-    "!huggingface-cli login --token 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 --add-to-git-credential"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 9,

From 09147186b6dc771a7cee16b52fa25f5d421b99fa Mon Sep 17 00:00:00 2001
From: Pu Fanyi <FPU001@e.ntu.edu.sg>
Date: Wed, 3 Jul 2024 04:27:23 -0700
Subject: [PATCH 10/32] LiveBench June (#129)

* init live bench

* update path

* chore: Refactor live_bench package structure and update dependencies

* update

* Merge remote-tracking branch 'origin/internal_main_dev'

* Refactor live_bench package structure and update dependencies

* Refactor live_bench package structure and update dependencies

* Fix execution count in example.ipynb

* extract_infomation

* Refactor extract_infomation.py to improve text extraction from HTML

* fix

* fix

* extract infomation

* chore: Refactor extract_infomation.py for improved readability and maintainability

* chore: Refactor data_generator prompt.md and check_prompt.md for improved clarity and instructions

* lint

* update

* update prompt

* extract infomation

* add info

* lint

* update

* filter

* update version of live_bench

* Update model version to gemini-1.5-pro

* update

* livebench_eval

* livebench

* update
---
 lmms_eval/evaluator.py                        |   2 +-
 lmms_eval/models/claude.py                    |   1 +
 lmms_eval/models/gemini_api.py                |   4 +-
 lmms_eval/models/model_utils/load_video.py    |   2 +
 lmms_eval/tasks/live_bench/live_bench.yaml    |  29 ++
 lmms_eval/tasks/live_bench/utils.py           | 197 +++++++++
 tools/live_bench/create_dataset.py            |  12 +
 tools/live_bench/data_summary.ipynb           | 336 ++++++++++++++++
 tools/live_bench/example.ipynb                | 371 +++++++++++++++++
 tools/live_bench/filter.ipynb                 | 341 ++++++++++++++++
 tools/live_bench/live_bench/__init__.py       |   2 +
 tools/live_bench/live_bench/api/live_bench.py |  20 +
 .../live_bench/data_generator/__init__.py     |   4 +
 .../live_bench/data_generator/check_prompt.md |  25 ++
 .../data_generator/default_criteria.md        |  16 +
 .../example/example_output.json               |  57 +++
 .../example/example_website.png               | Bin 0 -> 559429 bytes
 .../live_bench/data_generator/live_bench.py   | 145 +++++++
 .../data_generator/live_bench_data.py         | 116 ++++++
 .../live_bench/data_generator/prompt.md       |  17 +
 .../live_bench/data_generator/qa_generator.py | 379 ++++++++++++++++++
 .../live_bench/data_generator/response.py     |  12 +
 .../live_bench/data_generator/score_getter.py | 157 ++++++++
 .../live_bench/data_generator/score_prompt.md |  20 +
 .../data_generator/utils/__init__.py          |   0
 .../live_bench/data_generator/utils/claude.py |  42 ++
 .../utils/extract_infomation.py               | 116 ++++++
 .../live_bench/data_generator/utils/gemini.py |  37 ++
 .../live_bench/data_generator/utils/gpt4v.py  |  72 ++++
 tools/live_bench/live_bench/driver/.gitignore |   1 +
 .../live_bench/live_bench/driver/__init__.py  |   1 +
 .../live_bench/driver/load_driver.py          |  71 ++++
 .../live_bench/screen_shoter/__init__.py      |   2 +
 .../live_bench/screen_shoter/screen.py        |  30 ++
 .../live_bench/screen_shoter/screen_shoter.py | 141 +++++++
 tools/live_bench/live_bench/view.ipynb        | 354 ++++++++++++++++
 .../live_bench/websites/__init__.py           |   2 +
 .../live_bench/websites/load_website.py       |  34 ++
 .../live_bench/live_bench/websites/website.py |  62 +++
 .../live_bench/websites/website_list.yaml     |  79 ++++
 tools/live_bench/pyproject.toml               |  47 +++
 tools/live_bench/setup.py                     |   3 +
 tools/make_vatex.py                           |  31 ++
 43 files changed, 3388 insertions(+), 2 deletions(-)
 create mode 100644 lmms_eval/tasks/live_bench/live_bench.yaml
 create mode 100644 lmms_eval/tasks/live_bench/utils.py
 create mode 100644 tools/live_bench/create_dataset.py
 create mode 100644 tools/live_bench/data_summary.ipynb
 create mode 100644 tools/live_bench/example.ipynb
 create mode 100644 tools/live_bench/filter.ipynb
 create mode 100644 tools/live_bench/live_bench/__init__.py
 create mode 100644 tools/live_bench/live_bench/api/live_bench.py
 create mode 100644 tools/live_bench/live_bench/data_generator/__init__.py
 create mode 100644 tools/live_bench/live_bench/data_generator/check_prompt.md
 create mode 100644 tools/live_bench/live_bench/data_generator/default_criteria.md
 create mode 100644 tools/live_bench/live_bench/data_generator/example/example_output.json
 create mode 100644 tools/live_bench/live_bench/data_generator/example/example_website.png
 create mode 100644 tools/live_bench/live_bench/data_generator/live_bench.py
 create mode 100644 tools/live_bench/live_bench/data_generator/live_bench_data.py
 create mode 100644 tools/live_bench/live_bench/data_generator/prompt.md
 create mode 100644 tools/live_bench/live_bench/data_generator/qa_generator.py
 create mode 100644 tools/live_bench/live_bench/data_generator/response.py
 create mode 100644 tools/live_bench/live_bench/data_generator/score_getter.py
 create mode 100644 tools/live_bench/live_bench/data_generator/score_prompt.md
 create mode 100644 tools/live_bench/live_bench/data_generator/utils/__init__.py
 create mode 100644 tools/live_bench/live_bench/data_generator/utils/claude.py
 create mode 100644 tools/live_bench/live_bench/data_generator/utils/extract_infomation.py
 create mode 100644 tools/live_bench/live_bench/data_generator/utils/gemini.py
 create mode 100644 tools/live_bench/live_bench/data_generator/utils/gpt4v.py
 create mode 100644 tools/live_bench/live_bench/driver/.gitignore
 create mode 100644 tools/live_bench/live_bench/driver/__init__.py
 create mode 100644 tools/live_bench/live_bench/driver/load_driver.py
 create mode 100644 tools/live_bench/live_bench/screen_shoter/__init__.py
 create mode 100644 tools/live_bench/live_bench/screen_shoter/screen.py
 create mode 100644 tools/live_bench/live_bench/screen_shoter/screen_shoter.py
 create mode 100644 tools/live_bench/live_bench/view.ipynb
 create mode 100644 tools/live_bench/live_bench/websites/__init__.py
 create mode 100644 tools/live_bench/live_bench/websites/load_website.py
 create mode 100644 tools/live_bench/live_bench/websites/website.py
 create mode 100644 tools/live_bench/live_bench/websites/website_list.yaml
 create mode 100755 tools/live_bench/pyproject.toml
 create mode 100755 tools/live_bench/setup.py
 create mode 100644 tools/make_vatex.py

diff --git a/lmms_eval/evaluator.py b/lmms_eval/evaluator.py
index 0104b01a..b05f00d1 100755
--- a/lmms_eval/evaluator.py
+++ b/lmms_eval/evaluator.py
@@ -325,7 +325,7 @@ def evaluate(
             # hack: remove image columns to speed avoid loading images and speed up postprocessing
             # reason: doc_iterator will actually load image if it's in the doc.
             docs = task.test_docs() if task.has_test_docs() else task.validation_docs()
-            if "d170" not in task_name and "dc100" not in task_name and "dc200" not in task_name and "llava_wilder" not in task_name and "livebench" not in task_name:
+            if "d170" not in task_name and "dc100" not in task_name and "dc200" not in task_name and "llava_wilder" not in task_name and "live_bench" not in task_name:
                 remove_cols = []
                 features = docs.features
                 # If it is an Image instance or a Sequence of Image instance. Remove it
diff --git a/lmms_eval/models/claude.py b/lmms_eval/models/claude.py
index 4c967e88..ff066d35 100644
--- a/lmms_eval/models/claude.py
+++ b/lmms_eval/models/claude.py
@@ -238,6 +238,7 @@ def generate_until(self, requests) -> List[str]:
                         pbar.update(1)
                         continue
 
+            response_text = message.content[0].text
             res.append(message.content[0].text)
             pbar.update(1)
 
diff --git a/lmms_eval/models/gemini_api.py b/lmms_eval/models/gemini_api.py
index 4a43c9af..4dbc25bd 100644
--- a/lmms_eval/models/gemini_api.py
+++ b/lmms_eval/models/gemini_api.py
@@ -31,7 +31,7 @@
 class GeminiAPI(lmms):
     def __init__(
         self,
-        model_version: str = "gemini-1.5-flash-latest",
+        model_version: str = "gemini-1.5-pro",
         modality: str = "image",
         timeout: int = 120,
         continual_mode: bool = False,
@@ -46,6 +46,8 @@ def __init__(
         if self.continual_mode and response_persistent_folder is None:
             raise ValueError("Continual mode requires a persistent path for the response. We will cache the Gemini API response in this path and use it for future requests. Please provide a valid path.")
         self.response_persistent_folder = response_persistent_folder
+        if not os.path.exists(self.response_persistent_folder):
+            os.makedirs(self.response_persistent_folder)
         self.response_persistent_file = os.path.join(self.response_persistent_folder, f"{self.model_version}_response.json")
 
         if os.path.exists(self.response_persistent_file):
diff --git a/lmms_eval/models/model_utils/load_video.py b/lmms_eval/models/model_utils/load_video.py
index 25be5fa4..dbb3cf6f 100644
--- a/lmms_eval/models/model_utils/load_video.py
+++ b/lmms_eval/models/model_utils/load_video.py
@@ -29,6 +29,8 @@ def record_video_length_packet(container):
 
 
 def read_video_pyav(video_path, num_frm=8):
+    container = av.open(video_path)
+
     if "webm" not in video_path and "mkv" not in video_path:
         # For mp4, we try loading with stream first
         try:
diff --git a/lmms_eval/tasks/live_bench/live_bench.yaml b/lmms_eval/tasks/live_bench/live_bench.yaml
new file mode 100644
index 00000000..1612931b
--- /dev/null
+++ b/lmms_eval/tasks/live_bench/live_bench.yaml
@@ -0,0 +1,29 @@
+dataset_path: lmms-lab/LiveBench
+dataset_kwargs:
+  token: True
+task: "live_bench"
+test_split: test
+dataset_name: 2024-06
+output_type: generate_until
+doc_to_visual: !function utils.livebench_doc_to_visual
+doc_to_text: !function utils.livebench_doc_to_text
+doc_to_target: "answer"
+generation_kwargs:
+  max_new_tokens: 1024
+  temperature: 0
+  top_p: 1.0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.livebench_process_results
+metric_list:
+  - metric: gpt4_eval_score
+    aggregation: !function utils.livebench_aggregate_results
+    higher_is_better: true
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: ""
+metadata:
+  version: "2024-06"
+  api_type : openai
+  gpt_eval_model_name: "gpt-4o"
diff --git a/lmms_eval/tasks/live_bench/utils.py b/lmms_eval/tasks/live_bench/utils.py
new file mode 100644
index 00000000..f8381293
--- /dev/null
+++ b/lmms_eval/tasks/live_bench/utils.py
@@ -0,0 +1,197 @@
+from pathlib import Path
+import yaml
+import os
+import requests
+import logging
+import time
+import base64
+import openai
+import json
+from io import BytesIO
+from tqdm import tqdm
+import pandas as pd
+import numpy as np
+
+
+eval_logger = logging.getLogger("lmms-eval")
+
+
+with open(Path(__file__).parent / "live_bench.yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+
+    config = yaml.safe_load("".join(safe_data))
+
+GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"]
+API_TYPE = config["metadata"]["api_type"]
+
+if API_TYPE == "openai":
+    API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
+    API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
+    headers = {
+        "Authorization": f"Bearer {API_KEY}",
+        "Content-Type": "application/json",
+    }
+
+elif API_TYPE == "azure":
+    API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
+    API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
+    headers = {
+        "api-key": API_KEY,
+        "Content-Type": "application/json",
+    }
+
+_PROMPT_WITH_IMAGE = """\
+[Question]
+
+{prompt}
+
+[Assistant Response]
+
+{generation}
+
+[Ground Truth Response]
+
+{reference}
+
+[System]
+
+Rate whether the assistant response correctly matches the ground truth, in regards to the image above.
+
+The rating should be 0-10, where 0 is incorrect and 10 is correct.
+
+Below is the specific criteria for rating:
+
+{criteria}
+
+Total score is out of 10. If the model's answer cannot be provided due to political reasons, please assign a score of 0 for further processing. If the model's response is biased due to political factors, please score it based on its understanding of the image, but reduce the objectivity score accordingly.
+
+Your response should be in the JSON format:
+```json
+{{
+    "Explanation": "(your explanation)",
+    "Rating": "(int)"
+}}
+```
+"""
+
+
+def format_prompt(question, ground_truth_answer, answer, criteria):
+    return _PROMPT_WITH_IMAGE.format(prompt=question, generation=answer, reference=ground_truth_answer, criteria=criteria)
+
+
+def get_chat_response(base64_images, question, ground_truth_answer, answer, criteria, max_retries=5, wait_time=10):
+    client = openai.OpenAI(api_key=API_KEY)
+
+    content = []
+    for base64_image in base64_images:
+        content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}})
+    prompt = format_prompt(question, ground_truth_answer, answer, criteria)
+    content.append(
+        {
+            "type": "text",
+            "text": prompt,
+        }
+    )
+
+    messages = [
+        {
+            "role": "user",
+            "content": content,
+        }
+    ]
+
+    # payload = {
+    #     "model": GPT_EVAL_MODEL_NAME,
+    #     "response_format": {"type": "json_object"},
+    #     "max_tokens": 1024,
+    #     "temperature": 0.0,
+    # }
+
+    for attempt in range(max_retries):
+        try:
+            response = client.chat.completions.create(model=GPT_EVAL_MODEL_NAME, messages=messages, max_tokens=1024, response_format={"type": "json_object"}, temperature=0.0)
+            response_data = response.choices[0].message.content
+            # print(response_data)
+            response_data = json.loads(response_data)
+            rating = response_data["Rating"]
+            explanation = response_data["Explanation"]
+            return rating, explanation, GPT_EVAL_MODEL_NAME
+        except requests.exceptions.RequestException as e:
+            eval_logger.warning(f"Request failed on attempt {attempt + 1}: {e}")
+            time.sleep(wait_time)
+            if attempt == max_retries - 1:
+                eval_logger.error(f"Failed to get response after {max_retries} attempts")
+                return -1, str(e), GPT_EVAL_MODEL_NAME
+        except Exception as e:
+            eval_logger.error(f"Error on attempt {attempt + 1}: {e}")
+            return -1, str(e), GPT_EVAL_MODEL_NAME
+
+
+def image_to_base64(pil_image):
+    buffered = BytesIO()
+    pil_image.save(buffered, format="PNG")
+    return base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+
+_images = {}
+
+dataset = None
+
+
+def livebench_doc_to_visual(doc):
+    img_list = [image.convert("RGB") for image in doc["images"]]
+    return img_list
+
+
+def livebench_doc_to_text(doc, model_specific_prompt_kwargs=None):
+    if model_specific_prompt_kwargs is None:
+        model_specific_prompt_kwargs = {}
+    pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "")
+    post_prompt = model_specific_prompt_kwargs.get("post_prompt", "")
+    return f"{pre_prompt}{doc['question']}{post_prompt}"
+
+
+SUBTASKS = ("Basic Understanding", "Contextual Analysis", "Deeper Implications", "Broader Implications", "Further Insights")
+
+
+def livebench_process_results(doc, results):
+    base64_images = [image_to_base64(image) for image in livebench_doc_to_visual(doc)]
+    subtask = doc["subtask"]
+    criteria = doc["criteria"]
+    if subtask not in SUBTASKS:
+        subtask = "further insights"
+    if not results:
+        return {"gpt4_eval_score": {"rating": -1, "explanation": "No response", "model_name": "N/A", "subtask": subtask}}
+    rating, explanation, model_name = get_chat_response(base64_images=base64_images, question=doc["question"], ground_truth_answer=doc["answer"], answer=results[0] if results else "", criteria=criteria)
+    if rating >= 0:
+        return {"gpt4_eval_score": {"rating": rating, "explanation": explanation, "model_name": model_name, "subtask": subtask, "id": doc["id"]}}
+    else:
+        return {"gpt4_eval_score": {"rating": -1, "explanation": explanation, "model_name": "N/A", "subtask": subtask, "id": doc["id"]}}
+
+
+def livebench_aggregate_results(results):
+    sum_score, count = 0, 0
+    score = {}
+    for subtask in SUBTASKS:
+        score[subtask] = []
+    for result in results:
+        if result["rating"] == -1:
+            continue
+        sum_score += result["rating"] / 10
+        count += 1
+        subtask = result["subtask"]
+        if subtask not in SUBTASKS:
+            subtask = "further insights"
+        score[result["subtask"]].append(result["rating"] / 10)
+    res = pd.DataFrame([(subtask, len(score[subtask]), np.mean(score[subtask]) * 100) for subtask in SUBTASKS], columns=["Subtask", "Count", "Average Score"])
+    print("=" * 50)
+    print(res)
+    print("=" * 50)
+    if count == 0:
+        eval_logger.warning("No valid scores to aggregate")
+    return sum_score / count * 100 if count > 0 else None
diff --git a/tools/live_bench/create_dataset.py b/tools/live_bench/create_dataset.py
new file mode 100644
index 00000000..c02d811c
--- /dev/null
+++ b/tools/live_bench/create_dataset.py
@@ -0,0 +1,12 @@
+from live_bench.websites import load_websites, load_websites_from_file
+from live_bench import LiveBench
+
+
+if __name__ == "__main__":
+    website = load_websites()
+    dataset = LiveBench(force_clear=False, name="2024-06")
+    dataset.capture(websites=website, driver_kwargs={"headless": True}, screen_shoter="single_screen", shoter_kwargs={"screen_size": (1024, 1024)}, qa_generator="gpt4v", scorer="gpt4v", checker="gemini")
+
+    website = load_websites_from_file("/data/pufanyi/project/lmms-eval/temp/images")
+    dataset.capture(websites=website, screen_shoter="human", qa_generator="gpt4v", scorer="gpt4v", checker="gemini", driver_kwargs={}, shoter_kwargs={}, generator_kwargs={})
+    dataset.upload()
diff --git a/tools/live_bench/data_summary.ipynb b/tools/live_bench/data_summary.ipynb
new file mode 100644
index 00000000..f7bc83e9
--- /dev/null
+++ b/tools/live_bench/data_summary.ipynb
@@ -0,0 +1,336 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/data/pufanyi/anaconda3/anacondabin/envs/live_bench/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = load_dataset(\"lmms-lab/LiveBench\", \"2024-06\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = data[\"test\"].to_pandas()\n",
+    "df = df[df[\"checker\"].notna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[620][\"checker\"] is None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "subtask\n",
+       "Contextual Analysis     477\n",
+       "Deeper Implications     132\n",
+       "Basic Understanding     118\n",
+       "Further Insights         63\n",
+       "Broader Implications     52\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"subtask\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.sample(frac=1).reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_303711/5529174.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  top_50_per_subtask = df.groupby('subtask').apply(lambda x: x.nlargest(50, 'score'))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "250"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "top_50_per_subtask = df.groupby('subtask').apply(lambda x: x.nlargest(50, 'score'))\n",
+    "top_50_per_subtask.reset_index(drop=True, inplace=True)\n",
+    "len(top_50_per_subtask)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "subtask\n",
+       "Basic Understanding     50\n",
+       "Broader Implications    50\n",
+       "Contextual Analysis     50\n",
+       "Deeper Implications     50\n",
+       "Further Insights        50\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "top_50_per_subtask[\"subtask\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.276"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "np.mean(top_50_per_subtask[\"score\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import Dataset, Features\n",
+    "import datasets\n",
+    "\n",
+    "def gen():\n",
+    "    for d in top_50_per_subtask:\n",
+    "        yield d\n",
+    "\n",
+    "data = Dataset.from_pandas(top_50_per_subtask, features=Features({\n",
+    "            \"id\": datasets.Value(\"int32\"),\n",
+    "            \"images\": datasets.Sequence(datasets.Image()),\n",
+    "            \"website\": datasets.Value(\"string\"),\n",
+    "            \"question\": datasets.Value(\"string\"),\n",
+    "            \"answer\": datasets.Value(\"string\"),\n",
+    "            \"criteria\": datasets.Value(\"string\"),\n",
+    "            \"subtask\": datasets.Value(\"string\"),\n",
+    "            \"data_generator\": datasets.Value(\"string\"),\n",
+    "            \"checker\": datasets.Value(\"string\"),\n",
+    "            \"date_time\": datasets.Value(\"string\"),\n",
+    "            \"screen_shoter\": datasets.Value(\"string\"),\n",
+    "            \"screen_size\": datasets.Value(\"string\"),\n",
+    "            \"score\": datasets.Value(\"int32\"),\n",
+    "            \"reason\": datasets.Value(\"string\"),\n",
+    "            \"scorer_name\": datasets.Value(\"string\"),\n",
+    "        }))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dataset({\n",
+       "    features: ['id', 'images', 'website', 'question', 'answer', 'criteria', 'subtask', 'data_generator', 'checker', 'date_time', 'screen_shoter', 'screen_size', 'score', 'reason', 'scorer_name'],\n",
+       "    num_rows: 250\n",
+       "})"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'id': Value(dtype='int32', id=None),\n",
+       " 'images': Sequence(feature=Image(mode=None, decode=True, id=None), length=-1, id=None),\n",
+       " 'website': Value(dtype='string', id=None),\n",
+       " 'question': Value(dtype='string', id=None),\n",
+       " 'answer': Value(dtype='string', id=None),\n",
+       " 'criteria': Value(dtype='string', id=None),\n",
+       " 'subtask': Value(dtype='string', id=None),\n",
+       " 'data_generator': Value(dtype='string', id=None),\n",
+       " 'checker': Value(dtype='string', id=None),\n",
+       " 'date_time': Value(dtype='string', id=None),\n",
+       " 'screen_shoter': Value(dtype='string', id=None),\n",
+       " 'screen_size': Value(dtype='string', id=None),\n",
+       " 'score': Value(dtype='int32', id=None),\n",
+       " 'reason': Value(dtype='string', id=None),\n",
+       " 'scorer_name': Value(dtype='string', id=None)}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Map: 100%|██████████| 250/250 [00:00<00:00, 273.33 examples/s]it/s]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00,  4.98ba/s]\n",
+      "Uploading the dataset shards: 100%|██████████| 1/1 [00:18<00:00, 18.34s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/datasets/lmms-lab/LiveBench/commit/89ce4e07cf0988b2305b7227ac2677d481e6112a', commit_message='Upload dataset', commit_description='', oid='89ce4e07cf0988b2305b7227ac2677d481e6112a', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.push_to_hub(\"lmms-lab/LiveBench\", \"2024-06\", split=\"test\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lmms-eval",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tools/live_bench/example.ipynb b/tools/live_bench/example.ipynb
new file mode 100644
index 00000000..73332ab3
--- /dev/null
+++ b/tools/live_bench/example.ipynb
@@ -0,0 +1,371 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/data/pufanyi/anaconda3/anacondabin/envs/live_bench/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from random import sample\n",
+    "\n",
+    "from live_bench.websites.website import DefaultWebsite\n",
+    "from live_bench.websites import load_websites\n",
+    "\n",
+    "# website = load_websites()\n",
+    "# website = sample(website, 1)\n",
+    "# website[0].url\n",
+    "website = [DefaultWebsite(url=\"https://www.asahi.com/\")]  # , DefaultWebsite(url=\"https://www.bbc.com/sport\"), DefaultWebsite(url=\"https://www.bbc.com/business\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "could not detect version_main.therefore, we are assuming it is chrome 108 or higher\n"
+     ]
+    }
+   ],
+   "source": [
+    "from live_bench.data_generator.utils.extract_infomation import InfomationExtractor\n",
+    "from live_bench.screen_shoter import get_shoter\n",
+    "from live_bench.driver import load_driver\n",
+    "\n",
+    "shoter = get_shoter(\"single_screen\")\n",
+    "driver = load_driver()\n",
+    "w = shoter(driver, website[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extractor = InfomationExtractor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = extractor.extract_infomation(w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "**Here is something you can take as reference.**\n",
+      "\n",
+      "## Text Extracted in the HTML\n",
+      "\n",
+      "Below is the text extracted from the website for you to take reference:\n",
+      "BBC Home - Breaking News, World News, US News, Sports, Business, Innovation, Climate, Culture, Travel, Video & Audio\n",
+      "\n",
+      "What son's conviction means for President Biden\n",
+      "The guilty verdict is unlikely to change voters' minds, but it will be a personal blow for the US president.\n",
+      "1 hr ago | US & Canada\n",
+      "\n",
+      "Hunter Biden found guilty on all counts in gun trial\n",
+      "The US president's son is found guilty of lying about his drug use when buying a handgun in 2018.\n",
+      "- The struggles and scandals of Hunter Biden\n",
+      "\n",
+      "Blinken says fate of ceasefire plan down to Hamas\n",
+      "The US diplomat says Israel's prime minister \"reaffirmed his commitment\" to a Gaza ceasefire plan.\n",
+      "2 hrs ago | Middle East\n",
+      "\n",
+      "Ukraine 'hits missile launch sites in Russia'\n",
+      "The mayor of the city of Kharkiv says the situation there is \"calmer\" as Russia has been shelling less.\n",
+      "1 hr ago | Europe\n",
+      "\n",
+      "Four US college instructors stabbed in public park in China\n",
+      "The instructors were on a daytime visit to a public park when they were attacked, Cornell College says.\n",
+      "4 hrs ago | Asia\n",
+      "\n",
+      "Animal-rights protesters attack portrait of King\n",
+      "Animal-rights protesters attack a portrait of King Charles III, in a London gallery.\n",
+      "2 hrs ago | UK\n",
+      "\n",
+      "Warning shots from South as NK soldiers cross border\n",
+      "The incident at the DMZ comes at a time of heightened tensions between the two Koreas.\n",
+      "11 hrs ago | Asia\n",
+      "\n",
+      "## Image Features\n",
+      "\n",
+      "From the screenshot of the news website you provided, here is the analysis based on the images displayed alongside the corresponding news headings and text:\n",
+      "\n",
+      "1. **Image Associated with Hunter Biden's Conviction**:\n",
+      "   - **Description**: The image depicts Hunter Biden escorted by, possibly, security personnel or aides. He seems to be dressed in a formal dark suit and appears to be descending stairs or possibly exiting a vehicle. He carries an air of seriousness, likely reflective of the gravity of his legal situation.\n",
+      "   - **Relevance**: This image effectively captures the serious, personal, and public nature of the judicial proceedings against the President's son, making the situation more relatable to the audience. It directly ties to the news confirming Hunter Biden’s guilty verdict in a gun trial related to lying about drug use.\n",
+      "\n",
+      "2. **Image Accompanying the Article on Biden's Supporters**:\n",
+      "   - **Description**: The accompanying image shows a group of enthusiastic supporters holding signs, with one prominently reading \"Say Yes to Biden,\" suggesting a political rally or campaign event. The participants display expressions of support and enthusiasm.\n",
+      "   - **Relevance**: This image provides a visual contrast to the first, highlighting the ongoing support for the Biden family or campaign despite the legal issues faced by Hunter Biden. It serves to illustrate the political backdrop and public opinion dynamic mentioned in the news headlines.\n",
+      "\n",
+      "These images serve different purposes:\n",
+      "- The first image personalizes the news story, putting a face to the name in a high-stakes legal controversy. It underlines the personal and public challenges faced by the Biden family due to the conviction.\n",
+      "- The second image contextualizes the broader political support for the Biden family, suggesting that despite personal legal woes, there is a segment of the populace fervently supporting them.\n",
+      "\n",
+      "The clear connection between the images and the corresponding textual content on the news site helps readers visualize and better understand the unfolding events, enhancing the impact of the news storytelling.\n",
+      "\n",
+      "## Interesting Points\n",
+      "\n",
+      "The BBC news website, as demonstrated through the detailed examination of its content, offers a dynamic and visually engaging approach to news presentation. Here’s a deeper analysis of how it distinguishes itself:\n",
+      "\n",
+      "1. **Comprehensive and Geographically Diverse News Coverage**:\n",
+      "   - The content spans a wide range of geographical locations including the US, Middle East, Europe, Asia, and the UK. Each news piece targets a major recent event, reflecting the website’s commitment to global news coverage. This expansive geographic focus ensures that readers have access to a broad spectrum of significant, impactful news.\n",
+      "\n",
+      "2. **Varied Content Themes**: \n",
+      "   - The news themes are diverse, covering political, social, and cultural issues. From the legal troubles of a high-profile political figure’s son in the US to a ceasefire plan in the Middle East and violent incidents in Asia, the website covers a wide array of topics. This variety meets different readers' interests and keeps the content engaging.\n",
+      "\n",
+      "3. **Immediate Relevance**:\n",
+      "   - The website's content is timely, as indicated by timestamps such as “1 hr ago” and “2 hrs ago.” This reflects the website’s commitment to providing the latest news, which is crucial for maintaining reader engagement and trust in a digital age where current information is highly valued.\n",
+      "\n",
+      "4. **Stylistic and Engaging Visual Design**:\n",
+      "   - The use of compelling images alongside the news articles plays a critical role in storytelling. For instance, the image of Hunter Biden descending steps with a serious demeanor visually reinforces the gravity of the news about his conviction. \n",
+      "   - Meanwhile, the image of supporters holding \"Say Yes to Biden\" signs juxtaposed with Hunter Biden's legal news offers a visual narrative of continued support amidst political strife, underscoring the complexity and depth of public and personal life in politics.\n",
+      "\n",
+      "5. **Interactive and Multimedia Features**:\n",
+      "   - The use of tags such as \"OLIVE\" beside the breaking story of Hunter Biden indicates an interactive or breaking news feature that likely offers real-time updates and extensive coverage. This kind of multimedia integration enhances user interaction and engagement.\n",
+      "\n",
+      "In summary, the BBC news website sets itself apart through a combination of up-to-date, visually engaging, and comprehensively covered news items that cater to a global audience with varied interests. The effective use of images not only contextualizes the stories but also adds a layer of emotional and visual impact, making the news relatable and striking.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(str(response))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This screenshot from a news website contains several images corresponding to different news stories. Let's examine each image and extract relevant details:\n",
+      "\n",
+      "1. **Image associated with the Michael Mosley news story:**\n",
+      "   - The image depicts a middle-aged couple, smiling warmly at each other in a sunny, natural outdoor setting. This photo likely portrays Dr. Michael Mosley with his wife, representing a human interest angle to the story about Dr. Mosley's disappearance on the Greek island of Symi. The caption, \"'We will not lose hope,' says Michael Mosley's wife,\" implies a context of hope and determination amidst difficult circumstances.\n",
+      "\n",
+      "2. **Image linked to the news about the hostages freed in Gaza:**\n",
+      "   - This image features a group of soldiers with one individual in civilian clothing at the center, being lifted or celebrated, possibly right after a rescue scenario. The setting appears to be a rugged outdoor area, suggestive of a conflict or military zone, which aligns with the news story about hostages being freed in Gaza. The inclusion of armed personnel and a jubilant expression on the civilian's face highlights the relief and successful outcome of a dangerous operation.\n",
+      "\n",
+      "3. **Image for the Nova festival hostages news:**\n",
+      "   - This image depicts a motorboat on clear water under bright skies, possibly implying the geographic setting related to Michael Mosley’s disappearance near the Greek island of Symi. The serene environment contrasts starkly with the concerning news of his disappearance during what might have been a routine outing or travel.\n",
+      "\n",
+      "These images serve as visual supplements to the written content, providing readers with a clearer, more immediate understanding of the stories. They help bridge the emotional and contextual gaps that pure text might leave, allowing readers to engage more deeply with the news events. Each image is carefully selected to evoke specific sentiments and to provide visual context to the news headlines and summaries.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(response[\"features\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from live_bench import LiveBench"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = LiveBench(force_clear=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2024-06'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "could not detect version_main.therefore, we are assuming it is chrome 108 or higher\n",
+      "Capturing websites:   0%|          | 0/1 [00:00<?, ?it/s]Failed to generate response: 429 Resource has been exhausted (e.g. check quota).\n",
+      "Failed to generate response: 429 Resource has been exhausted (e.g. check quota).\n",
+      "Capturing websites: 100%|██████████| 1/1 [03:53<00:00, 233.51s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.capture(websites=website, driver_kwargs={\"headless\": True}, screen_shoter=\"single_screen\", shoter_kwargs={\"screen_size\": (1024, 1024)}, qa_generator=\"gpt4v\", scorer=\"gpt4v\", checker=\"gemini\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.hf_data[0][\"images\"][0].save(\"img.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dataset({\n",
+       "    features: ['id', 'images', 'website', 'question', 'answer', 'subtask', 'data_generator', 'checker', 'date_time', 'screen_shoter', 'screen_size', 'score', 'reason', 'scorer_name'],\n",
+       "    num_rows: 8\n",
+       "})"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.hf_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Map: 100%|██████████| 9/9 [00:00<00:00, 243.32 examples/s]?, ?it/s]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 1/1 [00:00<00:00, 194.00ba/s]\n",
+      "Uploading the dataset shards: 100%|██████████| 1/1 [00:02<00:00,  2.07s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.upload()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/data/pufanyi/anaconda3/anacondabin/envs/live_bench/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading readme: 100%|██████████| 2.40k/2.40k [00:00<00:00, 4.53MB/s]\n",
+      "Downloading data: 100%|██████████| 60.6M/60.6M [00:13<00:00, 4.37MB/s]\n",
+      "Downloading data: 100%|██████████| 119M/119M [00:06<00:00, 17.7MB/s] \n",
+      "Generating test split: 100%|██████████| 787/787 [00:02<00:00, 284.48 examples/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = load_dataset(\"lmms-lab/LiveBench\", \"2024-06\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lmms-eval",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tools/live_bench/filter.ipynb b/tools/live_bench/filter.ipynb
new file mode 100644
index 00000000..1a9b59e0
--- /dev/null
+++ b/tools/live_bench/filter.ipynb
@@ -0,0 +1,341 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/data/pufanyi/anaconda3/anacondabin/envs/live_bench/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import Dataset, load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading readme: 100%|██████████| 1.60k/1.60k [00:00<00:00, 2.29MB/s]\n",
+      "Downloading data: 100%|██████████| 48.9M/48.9M [00:04<00:00, 11.3MB/s]\n",
+      "Downloading data: 100%|██████████| 87.6M/87.6M [00:04<00:00, 20.7MB/s]\n",
+      "Downloading data: 100%|██████████| 103M/103M [00:05<00:00, 18.8MB/s] \n",
+      "Generating test split: 100%|██████████| 943/943 [00:03<00:00, 290.89 examples/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = load_dataset(\"lmms-lab/LiveBench\", \"2024-06\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    test: Dataset({\n",
+       "        features: ['id', 'images', 'website', 'question', 'answer', 'criteria', 'subtask', 'data_generator', 'checker', 'date_time', 'screen_shoter', 'screen_size', 'score', 'reason', 'scorer_name'],\n",
+       "        num_rows: 943\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Filter: 100%|██████████| 441/441 [00:33<00:00, 13.09 examples/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Dataset({\n",
+       "    features: ['id', 'images', 'website', 'question', 'answer', 'subtask', 'data_generator', 'checker', 'date_time', 'screen_shoter', 'screen_size', 'score', 'reason', 'scorer_name'],\n",
+       "    num_rows: 409\n",
+       "})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_data = data[\"test\"].filter(lambda example: example[\"score\"] and example[\"score\"] > 5)\n",
+    "filtered_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>images</th>\n",
+       "      <th>website</th>\n",
+       "      <th>question</th>\n",
+       "      <th>answer</th>\n",
+       "      <th>subtask</th>\n",
+       "      <th>data_generator</th>\n",
+       "      <th>checker</th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>screen_shoter</th>\n",
+       "      <th>screen_size</th>\n",
+       "      <th>score</th>\n",
+       "      <th>reason</th>\n",
+       "      <th>scorer_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Look at the image provided. Which article head...</td>\n",
+       "      <td>\"BBC tracks down smuggler behind Channel cross...</td>\n",
+       "      <td>Deeper Implications</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-27 14:36:42</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>10</td>\n",
+       "      <td>The answer accurately identifies the relevant ...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Look at the image. What significant global eve...</td>\n",
+       "      <td>The image of a young girl wearing a life jacke...</td>\n",
+       "      <td>Contextual Analysis</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-27 14:36:42</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>10</td>\n",
+       "      <td>The answer correctly identifies Biden and Trum...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>What detail in the image connected to the \"Bol...</td>\n",
+       "      <td>The \"Interpol Bolivia\" background visible in t...</td>\n",
+       "      <td>Deeper Implications</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-27 14:36:42</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Authenticity (4/5): The answer is reasonable b...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Analyze the visual composition of the article ...</td>\n",
+       "      <td>The image of the young girl, potentially a mig...</td>\n",
+       "      <td>Contextual Analysis</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-27 14:36:42</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>8</td>\n",
+       "      <td>The question directly relates to assessing the...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Focusing on the article \"BBC tracks down smugg...</td>\n",
+       "      <td>The image of a child, juxtaposed with an artic...</td>\n",
+       "      <td>Broader Implications</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-27 14:36:42</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>10</td>\n",
+       "      <td>The answer directly correlates with the story'...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id                                             images  \\\n",
+       "0   0  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "1   1  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "2   2  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "3   3  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "4   4  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "\n",
+       "                           website  \\\n",
+       "0  {'url': 'https://www.bbc.com/'}   \n",
+       "1  {'url': 'https://www.bbc.com/'}   \n",
+       "2  {'url': 'https://www.bbc.com/'}   \n",
+       "3  {'url': 'https://www.bbc.com/'}   \n",
+       "4  {'url': 'https://www.bbc.com/'}   \n",
+       "\n",
+       "                                            question  \\\n",
+       "0  Look at the image provided. Which article head...   \n",
+       "1  Look at the image. What significant global eve...   \n",
+       "2  What detail in the image connected to the \"Bol...   \n",
+       "3  Analyze the visual composition of the article ...   \n",
+       "4  Focusing on the article \"BBC tracks down smugg...   \n",
+       "\n",
+       "                                              answer               subtask  \\\n",
+       "0  \"BBC tracks down smuggler behind Channel cross...   Deeper Implications   \n",
+       "1  The image of a young girl wearing a life jacke...   Contextual Analysis   \n",
+       "2  The \"Interpol Bolivia\" background visible in t...   Deeper Implications   \n",
+       "3  The image of the young girl, potentially a mig...   Contextual Analysis   \n",
+       "4  The image of a child, juxtaposed with an artic...  Broader Implications   \n",
+       "\n",
+       "  data_generator checker            date_time  screen_shoter   screen_size  \\\n",
+       "0          gpt4v  gemini  2024-06-27 14:36:42  single_screen  (1024, 1024)   \n",
+       "1          gpt4v  gemini  2024-06-27 14:36:42  single_screen  (1024, 1024)   \n",
+       "2          gpt4v  gemini  2024-06-27 14:36:42  single_screen  (1024, 1024)   \n",
+       "3          gpt4v  gemini  2024-06-27 14:36:42  single_screen  (1024, 1024)   \n",
+       "4          gpt4v  gemini  2024-06-27 14:36:42  single_screen  (1024, 1024)   \n",
+       "\n",
+       "   score                                             reason scorer_name  \n",
+       "0     10  The answer accurately identifies the relevant ...       gpt4v  \n",
+       "1     10  The answer correctly identifies Biden and Trum...       gpt4v  \n",
+       "2      7  Authenticity (4/5): The answer is reasonable b...       gpt4v  \n",
+       "3      8  The question directly relates to assessing the...       gpt4v  \n",
+       "4     10  The answer directly correlates with the story'...       gpt4v  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_data.to_pandas().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Map: 100%|██████████| 409/409 [00:30<00:00, 13.63 examples/s]?it/s]\n",
+      "Creating parquet from Arrow format: 100%|██████████| 5/5 [00:00<00:00, 34.92ba/s]\n",
+      "Uploading the dataset shards: 100%|██████████| 1/1 [00:38<00:00, 38.26s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/datasets/lmms-lab/LiveBench/commit/7eaf5caa899cc0b8bae7156cc534e12825a97565', commit_message='Upload dataset', commit_description='', oid='7eaf5caa899cc0b8bae7156cc534e12825a97565', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_data.push_to_hub(\"lmms-lab/LiveBench\", \"2024-06\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "live_bench",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tools/live_bench/live_bench/__init__.py b/tools/live_bench/live_bench/__init__.py
new file mode 100644
index 00000000..ec6dc67d
--- /dev/null
+++ b/tools/live_bench/live_bench/__init__.py
@@ -0,0 +1,2 @@
+from live_bench.data_generator import LiveBench
+from live_bench.api.live_bench import generate_live_bench_from_path, generate_live_bench
diff --git a/tools/live_bench/live_bench/api/live_bench.py b/tools/live_bench/live_bench/api/live_bench.py
new file mode 100644
index 00000000..17403e66
--- /dev/null
+++ b/tools/live_bench/live_bench/api/live_bench.py
@@ -0,0 +1,20 @@
+from live_bench.websites import load_websites, load_websites_from_file
+from live_bench import LiveBench
+
+
+def generate_live_bench(*, force_clear=False, screen_shoter="single_screen", qa_generator="gpt4v", scorer="gpt4v", checker="gemini", driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}):
+    website = load_websites()
+    dataset = LiveBench(force_clear=force_clear)
+    dataset.capture(websites=website, screen_shoter=screen_shoter, qa_generator=qa_generator, scorer=scorer, checker=checker, driver_kwargs=driver_kwargs, shoter_kwargs=shoter_kwargs, generator_kwargs=generator_kwargs)
+    dataset.upload()
+
+
+def generate_live_bench_from_path(path, *, force_clear=False, qa_generator="gpt4v", scorer="gpt4v", checker="gemini", driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}):
+    website = load_websites_from_file(path)
+    dataset: LiveBench = LiveBench(force_clear=force_clear)
+    dataset.capture(websites=website, screen_shoter="human", qa_generator=qa_generator, scorer=scorer, checker=checker, driver_kwargs=driver_kwargs, shoter_kwargs=shoter_kwargs, generator_kwargs=generator_kwargs)
+    dataset.upload()
+
+
+if __name__ == "__main__":
+    generate_live_bench()
diff --git a/tools/live_bench/live_bench/data_generator/__init__.py b/tools/live_bench/live_bench/data_generator/__init__.py
new file mode 100644
index 00000000..65fd9781
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/__init__.py
@@ -0,0 +1,4 @@
+from live_bench.data_generator.qa_generator import get_generator, get_random_generator
+from live_bench.data_generator.live_bench_data import LiveBenchData
+from live_bench.data_generator.live_bench import LiveBench
+from live_bench.data_generator.response import Response
diff --git a/tools/live_bench/live_bench/data_generator/check_prompt.md b/tools/live_bench/live_bench/data_generator/check_prompt.md
new file mode 100644
index 00000000..4532e561
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/check_prompt.md
@@ -0,0 +1,25 @@
+I would like you to act as a quizmaster who designs questions based on a provided image that would challenge adults to think critically. The image in question is a screenshot from the homepage or section of news website. You are to create high-quality questions focusing on the information displayed within this webpage, which might contain multiple news articles. Your questions should specifically target the picture and the thematic information of a single article. Your question should be answerable, and checkable. Please disregard redundant elements of the website such as headers, and focus on the events depicted in the images themselves. If it is challenging to pose questions about a specific article due to insufficient information, design questions around the main information and events depicted in the image.
+
+Now, you are given a screenshot of the homepage of a news website, with a already generated question and answer. Your task is to refine the question and answer, and refractor them to make the question more answerable, checkable, and challenging. If you don't think the question is good, please provide a new question and answer.
+
+Note that the subtask must be one of these five:
+
+- Basic Understanding
+- Contextual Analysis
+- Deeper Implications
+- Broader Implications
+- Further Insights
+
+If you think the question does not correspond to the subtask, you have two options:
+1. Modify the question to correspond to the subtask.
+2. Modify the subtask to correspond to the question.
+
+However, you should not change the original question's subtask unless the original subtask is not one of these five. If you feel the original question's subtask does not match the question, modify the question to match the subtask instead of rewriting the subtask.
+
+Please note that although the image may contain a lot of political content, try to avoid questions with any political bias when asking questions. The question should focus on understanding and thinking about the image, not on political opinions. Within your capabilities, try to make the questions more challenging. However, you also need to consider the gradability of the questions you set. It is reiterated that what you need to assess is the ability to understand the news webpage, not politics.
+
+You should try to be innovative, and you can also try different types of questions, like multiple-choice questions, fill-in-the-blank questions, or even image-text matching questions, and sequencing questions if possible. Within your capabilities, try to make the questions more challenging.
+
+If you think the question is not good, or it is not answerable, please provide a new question and answer.
+
+Reminder again that you cannot change the original subtask unless the original subtask is not one of the five listed above.
diff --git a/tools/live_bench/live_bench/data_generator/default_criteria.md b/tools/live_bench/live_bench/data_generator/default_criteria.md
new file mode 100644
index 00000000..f276b439
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/default_criteria.md
@@ -0,0 +1,16 @@
+### 1. Authenticity (5 points)
+- **5 Points**: The information is directly observable in the image or can be reasonably inferred with strong evidence.
+- **3 Points**: The information has a plausible connection to the image but requires assumptions that are not strongly supported by the image.
+- **1 Point**: The information cannot be observed or reasonably inferred from the image; it seems unrelated or speculative.
+
+### 2. Logical Coherence (3 points)
+- **3 Points**: The answer logically follows from the question and maintains consistency with the image context.
+- **2 Points**: There are minor logical gaps or inconsistencies in the answer relative to the question.
+- **1 Point**: The answer is logically inconsistent or contradictory to the question or image context.
+
+### 3. Clarity and Precision (2 points)
+- **2 Points**: The question and answer are clearly articulated and precisely address specifics of the image.
+- **1 Point**: The question or answer is somewhat vague or overly general, lacking specific details related to the image.
+- **0 Points**: The question or answer is unclear or too ambiguous to determine relevance to the image.
+
+Each Q&A pair can score a maximum of 10 points. The sum of points from these three categories determines the final score for each pair. Provide a brief explanation for each rating, focusing on how well the Q&A adheres to these criteria.
diff --git a/tools/live_bench/live_bench/data_generator/example/example_output.json b/tools/live_bench/live_bench/data_generator/example/example_output.json
new file mode 100644
index 00000000..2526e7df
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/example/example_output.json
@@ -0,0 +1,57 @@
+{
+    "Basic Understanding": [
+        {
+            "Question": "Which of the following topics is NOT covered in the news articles shown in the image?\nA) Middle East politics\nB) Technological advancements\nC) Natural disasters\nD) Animal welfare",
+            "Answer": "C) Natural disasters",
+            "Criteria": "Give 10 marks if correctly selected C, otherwise 0 marks."
+        },
+        {
+            "Question": "Based on the image and the headlines provided on the BBC webpage, fill in the blank with the most appropriate word or phrase:\n\"The article titled 'UN Security Council backs US Israel-Gaza ceasefire plan' is accompanied by an image of ______, which symbolizes the impact of the conflict on civilians and the urgency for a ceasefire.\"",
+            "Answer": "The article titled 'UN Security Council backs US Israel-Gaza ceasefire plan' is accompanied by an image of **children amidst rubble**, which symbolizes the impact of the conflict on civilians and the urgency for a ceasefire.",
+            "Criteria": "Award 10 marks for the correct answer 'children amidst rubble', 5 marks for partially correct synonyms or related phrases, and 0 marks for incorrect answers."
+        }
+    ],
+    "Contextual Analysis": [
+		{
+            "Question": "In the image associated with the article about the US Security Council backing the Israel-Gaza ceasefire plan, what are the people in the image doing, and how do their actions and expressions relate to the content of the article?",
+            "Answer": "In the image associated with the article about the US Security Council backing the Israel-Gaza ceasefire plan, the people are navigating through rubble, indicating a scene of destruction. One person is climbing over debris, while another is looking directly at the camera with a serious expression. Their actions and expressions reflect the aftermath of conflict and the urgency of the situation, which aligns with the article's focus on the need for a ceasefire and the release of hostages held by Hamas.",
+            "Criteria": "Award up to 10 marks based on the accuracy and detail of the response: 2 marks for identifying the scene of destruction, 2 marks for mentioning a person climbing over debris, 2 marks for noting someone looking directly at the camera with a serious expression, 4 marks for correctly relating these observations to the aftermath of conflict, the urgency of the situation, and the article's focus on ceasefire and hostage release."
+        },
+        {
+            "Question": "How might the image of children navigating through rubble relate to the themes discussed in the article about Netanyahu and Gaza ceasefire?",
+            "Answer": "The image powerfully underscores the humanitarian impact of the conflict, aligning with the themes discussed in the article where Netanyahu's political maneuvers are contrasted with the pressing need for a ceasefire to alleviate civilian suffering in Gaza.",
+            "Criteria": "Award up to 10 marks based on the accuracy and relevance of the response: 2 marks for identifying the humanitarian impact, 3 marks for linking the image to the themes of the article, 2 marks for mentioning Netanyahu's political maneuvers, 3 marks for correctly associating the need for a ceasefire with the alleviation of civilian suffering."
+        }
+    ],
+    "Deeper Implications": [
+        {
+            "Question": "What broader issues are raised by the UN Security Council's backing of a ceasefire in the Israel-Gaza context?",
+            "Answer": "The broader issues include international involvement in regional conflicts, the effectiveness of UN resolutions in conflict resolution, and the ongoing debate over the balance between national security and humanitarian needs in conflict zones.",
+            "Criteria": "Award up to 10 marks based on the accuracy and completeness of the response: 3 marks for mentioning international involvement in regional conflicts, 3 marks for discussing the effectiveness of UN resolutions in conflict resolution, 4 marks for addressing the debate over the balance between national security and humanitarian needs in conflict zones."
+        },
+        {
+            "Question": "How does the image of a child in distress in a green field relate symbolically to the outcomes or themes of conflict depicted in the ceasefire article?",
+            "Answer": "The image symbolically represents the innocent casualties of conflict, particularly how children are affected, resonating with the urgency and necessity of a ceasefire to protect the most vulnerable populations from the consequences of prolonged conflict.",
+            "Criteria": "Award up to 10 marks based on the accuracy and relevance of the response: 2 marks for identifying the symbolic representation of innocent casualties, 3 marks for specifically mentioning how children are affected, 3 marks for relating this to the urgency and necessity of a ceasefire, 2 marks for connecting these elements to the protection of vulnerable populations."
+        }
+    ],
+    "Broader Implications": [
+        {
+            "Question": "Rank the news articles in the image in order of their potential global impact, from highest to lowest.",
+            "Answer": "1. **UN Security Council backs US Israel-Gaza ceasefire plan**\n2. **Netanyahu walks tightrope as US urges Gaza ceasefire deal**\n3. **Apple brings ChatGPT to iPhones in AI overhaul**\n4. **Aircraft carrying Malawi vice-president goes missing**\n5. **Fire at famous Bangkok market kills 1,000 animals**\n6. **Four US college instructors stabbed in public park in China**\n7. **Baltimore shipping channel reopens after bridge collapse**",
+            "Criteria": "Award up to 10 marks based on the accuracy of the ranking: 2 marks for correctly placing the UN Security Council article first, 2 marks for correctly placing the Netanyahu article second, 1 mark each for correctly placing the next three articles (Apple, Aircraft, Fire), and 1 mark each for correctly placing the last two articles (Stabbing, Bridge). Deduct 1 mark for each position an article is away from its correct placement."
+        }
+    ],
+    "Further Insights": [
+        {
+            "Question": "Based on the image, which of the following statements best explains the potential global impact of the events described in the news articles?\nA. The US-Israel-Gaza ceasefire plan backed by the UN Security Council is likely to reduce tensions in the Middle East, potentially leading to a more stable geopolitical environment in the region.\nB. The introduction of ChatGPT to iPhones is expected to significantly disrupt the technology market, overshadowing the geopolitical events in the Middle East and Africa.\nC. The fire at the Bangkok market, which killed 1,000 animals, is likely to have a more profound impact on global environmental policies than the ceasefire plan in the Middle East.\nD. The disappearance of the aircraft carrying the Malawi vice-president is expected to lead to a major international search and rescue operation, diverting attention from other global issues.",
+            "Answer": "A. The US-Israel-Gaza ceasefire plan backed by the UN Security Council is likely to reduce tensions in the Middle East, potentially leading to a more stable geopolitical environment in the region.",
+            "Criteria": "Award up to 10 marks based on the accuracy and relevance of the response: 10 marks for selecting option A, 0 marks for any other option. Detailed justification for scoring: Option A directly addresses the reduction of tensions and potential stabilization in the Middle East, which is a significant global impact. Other options, while plausible, do not directly relate to the primary global impact as depicted in the provided image and articles."
+        },
+        {
+            "Question": "Considering the current global attention on AI, how might the article about Apple bringing ChatGPT to iPhones in an AI overhaul reflect on broader technological trends and consumer expectations?",
+            "Answer": "This article reflects broader trends in AI integration into consumer technology, highlighting competitive dynamics in the tech industry, and growing consumer expectations for sophisticated AI features in everyday devices.",
+            "Criteria": "Award up to 10 marks based on the depth and accuracy of the response: 3 marks for identifying AI integration into consumer technology, 3 marks for discussing competitive dynamics in the tech industry, 4 marks for explaining the growth in consumer expectations for sophisticated AI features in everyday devices."
+        }
+    ]
+}
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diff --git a/tools/live_bench/live_bench/data_generator/live_bench.py b/tools/live_bench/live_bench/data_generator/live_bench.py
new file mode 100644
index 00000000..4748d75a
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/live_bench.py
@@ -0,0 +1,145 @@
+import os
+from datetime import datetime
+from typing import List
+from tqdm import tqdm
+from live_bench.data_generator.live_bench_data import LiveBenchData
+from datasets import Dataset, load_dataset
+from live_bench.websites import Website
+from live_bench.driver import load_driver
+from live_bench.data_generator import get_generator, get_random_generator
+from live_bench.screen_shoter import get_shoter
+from live_bench.data_generator.qa_generator import QAGenerator, QAData
+from live_bench.screen_shoter import ScreenImage, ScreenShoter
+from live_bench.data_generator.score_getter import get_score_getter, get_random_score_getter
+from live_bench.data_generator.response import Response
+from live_bench.data_generator.utils.extract_infomation import ImageInfomation, InfomationExtractor
+
+import json
+from typing import List, Tuple
+import logging
+
+logger = logging.getLogger("lmms-eval")
+
+
+def get_qa_data(images: ScreenImage, qa_generator: QAGenerator, *, infomation_getter: InfomationExtractor = None, test=False) -> Tuple[List[QAData], Response]:
+    if infomation_getter:
+        infomation = infomation_getter.extract_infomation(images)
+    else:
+        infomation = None
+    response = qa_generator.generate(images, test=test, infomation=infomation)
+    qa_data = qa_generator.format_response(response)
+    return qa_data, response
+
+
+def get_live_bench_data(driver, website: Website, screen_shoter: ScreenShoter, qa_generator: QAGenerator, checker: QAGenerator, infomation_getter: InfomationExtractor, test=False, scorer=None, score_threshold = 5) -> Tuple[List[LiveBenchData], Response]:
+    images = screen_shoter.capture(driver, website)
+    qa_data, logs = get_qa_data(images, qa_generator, test=test, infomation_getter=infomation_getter)
+    data = []
+    for qa in qa_data:
+        item = LiveBenchData(screen=images, question=qa.question, answer=qa.answer, subtask=qa.subtask, criteria=qa.criteria, data_generator=qa_generator.get_name(), checker=checker, scorer=scorer)
+        if score_threshold and (not item.score or item.score < score_threshold):
+            continue
+        data.append(item)
+    return data, logs
+
+
+class LiveBench(object):
+    def __init__(self, path: str = "lmms-lab/LiveBench", *, name="auto", split="test", cache_dir=None, remote_path=None, trust_remote_code=True, force_clear=False, **kwargs):
+        self.path = path
+        if name == "auto":
+            name = datetime.now().strftime("%Y-%m")
+        self.name = name
+        self.split = split
+        self.cache_dir = cache_dir
+        self.dataset_kwargs = kwargs
+        if remote_path is None:
+            self.remote_path = path
+        if force_clear:
+            self.clear()
+        else:
+            try:
+                self.hf_data = load_dataset(self.path, name=self.name, split=split, cache_dir=cache_dir, trust_remote_code=trust_remote_code, **kwargs)
+            except Exception as e:
+                logger.error(f"Error loading dataset: {e}")
+                self.clear()
+
+    def clear(self):
+        self.hf_data = Dataset.from_dict(
+            {"id": [], "images": [], "website": [], "question": [], "answer": [], "criteria": [], "subtask": [], "data_generator": [], "checker": [], "date_time": [], "screen_shoter": [], "screen_size": [], "score": [], "reason": [], "scorer_name": []},
+            features=LiveBenchData.features,
+        )
+
+    def add(self, data: LiveBenchData, id: int = None):
+        if id is None:
+            id = len(self.hf_data)
+        organized_data = data.to_hf_dict()
+        organized_data["id"] = id
+        self.hf_data = self.hf_data.add_item(organized_data)
+
+    def capture(self, websites: List[Website] = None, *, screen_shoter="single_screen", qa_generator=None, checker=None, driver=None, scorer=None, test=False, driver_kwargs={}, shoter_kwargs={}, generator_kwargs={}, log_folder="./logs"):
+        can_quit_driver = False
+        if driver is None and screen_shoter != "human":
+            driver = load_driver(**driver_kwargs)
+            can_quit_driver = True
+        screen_shoter = get_shoter(screen_shoter, **shoter_kwargs)
+        if qa_generator is not None:
+            qa_generator = get_generator(qa_generator, **generator_kwargs)
+        else:
+            qa_generator = get_random_generator(**generator_kwargs)
+        if checker is None:
+            checker = get_random_generator(**generator_kwargs)
+        else:
+            checker = get_generator(checker, **generator_kwargs)
+        if scorer is not None and isinstance(scorer, str):
+            scorer = get_score_getter(scorer)
+        elif scorer is None:
+            scorer = get_random_score_getter()
+        logs = []
+        infomation_getter = InfomationExtractor()
+        for website in tqdm(websites, desc="Capturing websites"):
+            try:
+                data, log = get_live_bench_data(driver, website, screen_shoter, qa_generator, checker, test=test, scorer=scorer, infomation_getter=infomation_getter)
+                logs.append(log.to_dict())
+                for d in data:
+                    self.add(d)
+            except Exception as e:
+                logger.error(f"Error capturing website: {e}")
+                logger.error(f"Website: {website.get_info()}")
+                logs.append(
+                    {
+                        "success": False,
+                        "content": f"Error capturing website: {e}",
+                        "full_log": {
+                            "website": website.get_info(),
+                            "error": str(e),
+                        },
+                    }
+                )
+                continue
+        if not os.path.exists(log_folder):
+            os.makedirs(log_folder)
+        date_time = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+        log_file = os.path.join(log_folder, f"{date_time}.json")
+        full_log = {
+            "info": {
+                "date_time": date_time,
+                "screen_shoter": screen_shoter.get_name(),
+                "qa_generator": qa_generator.get_name(),
+                "checker": checker.get_name(),
+                "scorer": scorer.get_name(),
+            },
+            "websites": [w.get_info() for w in websites],
+            "logs": logs,
+        }
+        with open(log_file, "w") as f:
+            json.dump(full_log, f, indent=4)
+        logger.info(f"Logs saved to {os.path.abspath(log_file)}")
+        if can_quit_driver:
+            driver.quit()
+
+    def upload(self, **kwargs):
+        self.hf_data.push_to_hub(self.remote_path, config_name=self.name, split=self.split, **kwargs)
+
+    def save(self, path: str):
+        self.hf_data.save_to_disk(path)
+        logger.info(f"Data saved to {os.path.abspath(path)}")
diff --git a/tools/live_bench/live_bench/data_generator/live_bench_data.py b/tools/live_bench/live_bench/data_generator/live_bench_data.py
new file mode 100644
index 00000000..65c83970
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/live_bench_data.py
@@ -0,0 +1,116 @@
+from live_bench.screen_shoter.screen import ScreenImage
+from live_bench.data_generator.utils.extract_infomation import ImageInfomation
+from live_bench.data_generator.qa_generator import QAGenerator
+import datasets
+
+
+class LiveBenchData(object):
+    SUBTASKS = ("Basic Understanding", "Contextual Analysis", "Deeper Implications", "Broader Implications", "Further Insights")
+    
+    features = datasets.Features(
+        {
+            "id": datasets.Value("int32"),
+            "images": datasets.Sequence(datasets.Image()),
+            "website": datasets.Value("string"),
+            "question": datasets.Value("string"),
+            "answer": datasets.Value("string"),
+            "criteria": datasets.Value("string"),
+            "subtask": datasets.Value("string"),
+            "data_generator": datasets.Value("string"),
+            "checker": datasets.Value("string"),
+            "date_time": datasets.Value("string"),
+            "screen_shoter": datasets.Value("string"),
+            "screen_size": datasets.Value("string"),
+            "score": datasets.Value("int32"),
+            "reason": datasets.Value("string"),
+            "scorer_name": datasets.Value("string"),
+        }
+    )
+
+    def __init__(
+        self, *, screen: ScreenImage, question: str, answer: str, criteria: str, subtask: str, data_generator: str, infomation: ImageInfomation = None, score: int = None, reason: str = None, checker: QAGenerator = None, scorer_name=None, scorer=None
+    ):
+        self.screen = screen
+        self.question = question
+        self.answer = answer
+        self.criteria = criteria
+        self.subtask = subtask
+        self.data_generator = data_generator
+        self.infomation = infomation
+        self.checker = None
+        if checker:
+            response = checker.check(screen, question, answer, criteria, subtask, infomation=infomation)
+            if response.success:
+                formatted_response = checker.format_checked_response(response)
+                if formatted_response.question and formatted_response.answer and formatted_response.criteria:
+                    self.question = formatted_response.question
+                    self.answer = formatted_response.answer
+                    self.criteria = formatted_response.criteria
+                    if formatted_response.subtask:
+                        self.subtask = formatted_response.subtask
+                    else:
+                        self.subtask = subtask
+                    self.checker = checker.get_name()
+        if self.subtask:
+            for sub in LiveBenchData.SUBTASKS:
+                if sub.lower() in self.subtask.lower():
+                    self.subtask = sub
+                    break
+            else:
+                self.subtask = "Further Insights"
+        else:
+            self.subtask = "Not Specified"
+        if score is not None:
+            self.score = score
+            self.reason = reason
+            self.scorer_name = scorer_name
+        else:
+            score = scorer.get_score(question, answer, screen.images)
+            self.score = score.score
+            self.reason = score.reason
+            self.scorer_name = scorer.get_name()
+
+    def to_dict(self):
+        images = self.screen.images
+        website = self.screen.website.get_info()
+        question = self.question
+        answer = self.answer
+        subtask = self.subtask
+        data_generator = self.data_generator
+        date_time = self.screen.capture_datetime
+        screen_shoter = self.screen.shoter
+        screen_size = self.screen.screen_size
+        criteria = self.criteria
+        return {
+            "images": images,
+            "website": website,
+            "question": question,
+            "answer": answer,
+            "criteria": criteria,
+            "subtask": subtask,
+            "data_generator": data_generator,
+            "checker": self.checker,
+            "date_time": date_time,
+            "screen_shoter": screen_shoter,
+            "screen_size": screen_size,
+            "score": self.score,
+            "reason": self.reason,
+            "scorer_name": self.scorer_name,
+        }
+
+    def to_hf_dict(self):
+        return self.features.encode_example(self.to_dict())
+
+    def to_output_dict(self):
+        return {
+            "screen": self.screen.to_output_dict(),
+            "question": self.question,
+            "answer": self.answer,
+            "criteria": self.criteria,
+            "subtask": self.subtask,
+            "data_generator": self.data_generator,
+            "checker": self.checker,
+            "score": self.score,
+            "reason": self.reason,
+            "scorer_name": self.scorer_name,
+        }
diff --git a/tools/live_bench/live_bench/data_generator/prompt.md b/tools/live_bench/live_bench/data_generator/prompt.md
new file mode 100644
index 00000000..10835d73
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/prompt.md
@@ -0,0 +1,17 @@
+I would like you to act as a quizmaster who designs questions based on a provided image that would challenge adults to think critically. The image in question is a screenshot from the homepage or section of a news website. You are to create high-quality questions focusing on the information displayed within this webpage, which might contain multiple news articles. Your questions should specifically target the picture and the thematic information of a single article. Your question should be answerable, and checkable. Please disregard redundant elements of the website such as headers, and focus on the events depicted in the images themselves. If it is challenging to pose questions about a specific article due to insufficient information, design questions around the main information and events depicted in the image.
+
+A well-crafted question about an event should allow respondents to gain deeper insights by observing and analyzing the event, paying attention to the following aspects:
+
+- Basic Understanding: Questions that require direct observation or recall of the information presented in the image. These questions test the ability to identify and understand the basic elements and facts shown.
+- Contextual Analysis: Questions that delve into the context or setting of the information presented. This involves understanding the background, the circumstances surrounding the information, or the broader setting in which the image is placed.
+- Deeper Implications: Questions that explore the underlying meanings, implications, or consequences of the information in the image. These questions encourage critical thinking about the deeper effects or hidden messages.
+- Broader Implications: Questions that extend the discussion beyond the immediate context of the image to its wider impact on society, other fields, or global issues.
+- Further Insights: Questions that prompt exploration of additional layers of understanding or connections to other knowledge and concepts not immediately apparent from the image.
+
+Consider designing a multi-round Q&A process, progressively deepening the understanding of the event’s essence. Always remember not to design questions that you are not sure of the answers to simply to increase the difficulty deliberately.
+
+Please note that although the image may contain a lot of political content, try to avoid questions with any political bias when asking questions. Your questions should focus on understanding and thinking about the image, not on political opinions.
+
+You should try to be innovative, and you may propose some difficult questions, as well as multiple-choice questions, fill-in-the-blank questions, or even image-text matching questions, and sequencing questions. Within your capabilities, try to make the questions more challenging.
+
+At the same time, you need to generate how this question should be scored, that is, the criteria. Each question is scored as $0\sim 10$, and the correct answers should be scored scored as $10$. Your grading criteria need to be clear and reasonable, closely aligned with the topic. When establishing the criteria, you should also consider measurability and flexibility to accommodate the answers of various respondents.
diff --git a/tools/live_bench/live_bench/data_generator/qa_generator.py b/tools/live_bench/live_bench/data_generator/qa_generator.py
new file mode 100644
index 00000000..2ae893ab
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/qa_generator.py
@@ -0,0 +1,379 @@
+import io
+import re
+import os
+import openai
+import base64
+import json
+import random
+import logging
+import pathlib
+import textwrap
+import google.generativeai as genai
+from time import sleep
+from PIL import Image
+from typing import List
+from abc import ABC, abstractmethod
+from live_bench.data_generator.response import Response
+from live_bench.screen_shoter import ScreenImage
+from live_bench.data_generator.utils.gpt4v import format_gpt4v_images, gpt4v_generate_response
+from live_bench.data_generator.utils.gemini import gemini_generate_response
+from live_bench.data_generator.utils.extract_infomation import InfomationExtractor, ImageInfomation
+
+logger = logging.getLogger("lmms-eval")
+
+SUBTASKS = {"Basic Understanding", "Contextual Analysis", "Deeper Implications", "Broader Implications", "Further Insights"}
+
+
+class QAData(object):
+    def __init__(self, question: str = None, answer: str = None, criteria: str = None, subtask: str = None):
+        self.question = question
+        self.answer = answer
+        self.criteria = criteria
+        self.subtask = subtask
+
+    def parse_subtask(subtask: str) -> str:
+        subtask = subtask.strip().lower()
+        for valid_subtask in SUBTASKS:
+            if valid_subtask.lower() in subtask.lower():
+                return valid_subtask
+        return "Unknown Subtask"
+
+    def set_subtask(self, subtask: str):
+        """
+        Set the subtask for the QAData instance after parsing it.
+
+        Args:
+            subtask (str): The subtask string to be set.
+        """
+        self.subtask = self.parse_subtask(subtask)
+
+    def to_dict(self):
+        return {"question": self.question, "answer": self.answer}
+
+
+class QAGenerator(ABC):
+    def __init__(self, prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md")):
+        self.prompt_file = prompt_file
+        self.prompt = self._load_prompt()
+
+    def _load_prompt(self):
+        with open(self.prompt_file, "r") as f:
+            return f.read()
+
+    def __call__(self, images: ScreenImage, *args, **kwargs):
+        return self.generate(images, *args, **kwargs)
+
+    def generate(self, images: ScreenImage, *, test=False, infomation=None, **kwargs) -> Response:
+        if test:
+            return Response(success=True, content="This is a test response.", full_log={})
+        return self._generate(images, infomation=infomation, test=test, **kwargs)
+
+    def check(self, images: ScreenImage, question, answer, criteria, subtask, *, infomation=None, test=False, **kwargs) -> Response:
+        if test:
+            return Response(success=True, content="This is a test response.", full_log={})
+        return self._check(images, question, answer, criteria, subtask, infomation=infomation, **kwargs)
+
+    @abstractmethod
+    def _generate(self, images: ScreenImage, **kwargs) -> Response:
+        raise NotImplementedError("_generate not implemented")
+
+    @abstractmethod
+    def _check(self, images: ScreenImage, question, answer, criteria, subtask, **kwargs) -> Response:
+        raise NotImplementedError("_check not implemented")
+
+    def format_response(self, response: Response) -> QAData:
+        if response.success:
+            qa_data = self._format_response(response)
+            if qa_data is None:
+                return []
+            else:
+                return qa_data
+        else:
+            return []
+
+    @abstractmethod
+    def _format_response(self, response: Response) -> str:
+        raise NotImplementedError("format_response not implemented")
+
+    @abstractmethod
+    def format_checked_response(self, response: Response) -> QAData:
+        raise NotImplementedError("format_checked_response not implemented")
+
+    def get_name(self) -> str:
+        raise NotImplementedError("get_name not implemented")
+
+
+class GeneratorRegistry:
+    def __init__(self):
+        self.generators = {}
+
+    def register_generator(self, name):
+        def decorator(cls):
+            self.generators[name] = cls
+            cls.get_name = lambda self: name
+            return cls
+
+        return decorator
+
+    def get_generator(self, name) -> QAGenerator:
+        return self.generators[name]
+
+    def get_random_generator(self) -> QAGenerator:
+        return random.choice(list(self.generators.values()))
+
+
+generator_registry = GeneratorRegistry()
+
+
+def register_generator(name):
+    return generator_registry.register_generator(name)
+
+
+def get_generator(name, *args, **kwargs) -> QAGenerator:
+    return generator_registry.get_generator(name)(*args, **kwargs)
+
+
+def get_random_generator(*args, **kwargs) -> QAGenerator:
+    return generator_registry.get_random_generator()(*args, **kwargs)
+
+
+@register_generator("gpt4v")
+class GPT4Generator(QAGenerator):
+    def __init__(
+        self,
+        prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md"),
+        model="gpt-4-turbo",
+        example_path=os.path.join(os.path.dirname(__file__), "example"),
+        check_prompt=os.path.join(os.path.dirname(__file__), "check_prompt.md"),
+    ):
+        super().__init__(prompt_file)
+        API_KEY = os.getenv("OPENAI_API_KEY")
+        if not API_KEY:
+            raise ValueError("OPENAI_API_KEY environment variable not set.")
+        self.api_key = API_KEY
+        self.client = openai.OpenAI(api_key=self.api_key)
+        self.model = model
+        if os.path.exists(example_path):
+            self.example_path = example_path
+        else:
+            self.example_path = None
+        if os.path.exists(check_prompt):
+            with open(check_prompt, "r") as f:
+                self.check_prompt = f.read()
+        else:
+            self.check_prompt = check_prompt
+
+    def format_messages(self, images: List[Image.Image], example_image: Image.Image, example_output: str, infomation: ImageInfomation):
+        example = [
+            {
+                "type": "text",
+                "text": "Here are few examples about the task and the expected output format. You can take these as examples to generate your own questions.",
+            },
+            format_gpt4v_images(example_image),
+            {
+                "type": "text",
+                "text": example_output,
+            },
+        ]
+        content = example + [format_gpt4v_images(image) for image in images]
+        if infomation:
+            content.append({"type": "text", "text": str(infomation)})
+        content.append(
+            {
+                "type": "text",
+                "text": "Please generate high-quality questions focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.",
+            },
+        )
+        messages = [
+            {
+                "role": "system",
+                "content": self.prompt,
+            },
+            {
+                "role": "user",
+                "content": content,
+            },
+        ]
+        return messages
+
+    def _generate(self, images: ScreenImage, *, max_tokens=4096, max_try_times=5, infomation=None, **kwargs):
+        if self.example_path:
+            example_image_path = os.path.join(self.example_path, "example_website.png")
+            example_output_path = os.path.join(self.example_path, "example_output.json")
+            example_image = Image.open(example_image_path)
+            with open(example_output_path, "r") as f:
+                example_output = f.read()
+
+        messages = self.format_messages(images.images, example_image, example_output, infomation)
+
+        return gpt4v_generate_response(client=self.client, model=self.model, messages=messages, max_tokens=max_tokens, max_try_times=max_try_times, json_format=True, **kwargs)
+
+    def get_check_prompt(self, question: str, answer: str, criteria, subtask, images: List[Image.Image], infomation: ImageInfomation = None):
+        messages = [
+            {
+                "role": "system",
+                "content": self.check_prompt,
+            }
+        ]
+        content = []
+        for img in images:
+            content.append(format_gpt4v_images(img))
+        content.append(
+            {
+                "type": "text",
+                "text": f"Question: {question}\nQuestioner's Answer: {answer}\nCriteria: {criteria}\nSubtask: {subtask}",
+            },
+        )
+        if infomation:
+            content.append(
+                {
+                    "type": "text",
+                    "text": str(infomation),
+                },
+            )
+        content.append(
+            {
+                "type": "text",
+                "text": "Please rephrase or rewrite the high-quality question focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.",
+            },
+        )
+        messages.append(
+            {
+                "role": "user",
+                "content": content,
+            }
+        )
+        return messages
+
+    def _check(self, images: ScreenImage, question, answer, criteria, subtask, *, max_tokens=4096, max_try_times=5, **kwargs):
+        messages = self.get_check_prompt(question, answer, criteria, subtask, images.images)
+        return gpt4v_generate_response(client=self.client, model=self.model, messages=messages, max_tokens=max_tokens, max_try_times=max_try_times, json_format=True, **kwargs)
+
+    def format_checked_response(self, response: Response):
+        data = json.loads(response.content)
+        question = data.get("question", None)
+        answer = data.get("answer", None)
+        criteria = data.get("criteria", None)
+        subtask = data.get("subtask", None)
+        return QAData(question=question, answer=answer, criteria=criteria, subtask=subtask)
+
+    def _format_response(self, response: Response) -> List[QAData]:
+        try:
+            qa_data = []
+            content = json.loads(response.content)
+            for subtask, messages in content.items():
+                subtask = subtask.lower()
+                for message in messages:
+                    message_lower = {k.lower(): v for k, v in message.items()}
+                    try:
+                        question = message_lower["question"]
+                        answer = message_lower["answer"]
+                        criteria = message_lower["criteria"]
+                        qa_data.append(QAData(question=question, answer=answer, criteria=criteria, subtask=subtask))
+                    except KeyError as e:
+                        logger.error(f"Failed to parse response: {message}")
+                        logger.error(f"Error: {e}")
+            return qa_data
+        except Exception as e:
+            logger.error(f"Failed to format response: {e}")
+            return []
+
+
+@register_generator("gemini")
+class GeminiGenerator(QAGenerator):
+    def __init__(
+        self,
+        prompt_file: str = os.path.join(os.path.dirname(__file__), "prompt.md"),
+        model="gemini-1.5-pro-latest",
+        example_path=os.path.join(os.path.dirname(__file__), "example"),
+        check_prompt=os.path.join(os.path.dirname(__file__), "check_prompt.md"),
+    ):
+        super().__init__(prompt_file)
+        GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+        if not GOOGLE_API_KEY:
+            raise ValueError("GOOGLE_API_KEY environment variable not set.")
+        genai.configure(api_key=GOOGLE_API_KEY)
+
+        self.api_key = GOOGLE_API_KEY
+        self.model = model
+        self.client = genai.GenerativeModel(model)
+        if os.path.exists(example_path):
+            self.example_path = example_path
+        else:
+            self.example_path = None
+        if os.path.exists(check_prompt):
+            with open(check_prompt, "r") as f:
+                self.check_prompt = f.read()
+        else:
+            self.check_prompt = check_prompt
+
+    def format_messages(self, images: List[Image.Image], example_image: Image.Image, example_output: str, infomation: ImageInfomation = None):
+        content = [self.prompt, "\n", "Example Image:", example_image, "\n", "Example Output:", example_output]
+        content.extend(images)
+        content.append(str(infomation))
+        content.append("Please generate high-quality questions focusing on the information displayed within this webpage. Your response should be in the format of the examples provided above and in JSON format.")
+        return content
+
+    def _generate(self, images: ScreenImage, *, max_tokens=4096, max_try_times=5, infomation: ImageInfomation = None, **kwargs):
+        if self.example_path:
+            example_image_path = os.path.join(self.example_path, "example_website.png")
+            example_output_path = os.path.join(self.example_path, "example_output.json")
+            example_image = Image.open(example_image_path)
+            with open(example_output_path, "r") as f:
+                example_output = f.read()
+
+        messages = self.format_messages(images.images, example_image, example_output, infomation)
+
+        return gemini_generate_response(self.client, messages, max_tokens, max_try_times, **kwargs)
+
+    def get_check_prompt(self, question: str, answer: str, criteria, subtask, images: List[Image.Image], infomation: ImageInfomation = None):
+        content = [self.check_prompt] + images
+        content.append(f"Question: {question}\nQuestioner's Answer: {answer}\nCriteria: {criteria}, Subtask: {subtask}")
+        content.append("Your response should be strictly in the below format:\n\nQuestion: <question>\nAnswer: <answer>\nCriteria: <criteria>\nSubtask: <subtask>")
+        if infomation:
+            content.append(str(infomation))
+        return content
+
+    def _check(self, images: ScreenImage, question, answer, criteria, subtask, *, max_tokens=4096, max_try_times=5, infomation: ImageInfomation = None, **kwargs):
+        messages = self.get_check_prompt(question, answer, criteria, subtask, images.images, infomation)
+        return gemini_generate_response(self.client, messages, max_tokens, max_try_times, **kwargs)
+
+    def format_checked_response(self, response: Response):
+        # Extract the question, answer, and subtask from the normalized content
+        question_match = re.search(r"question:\s*(.*?)\nAnswer:", response.content, re.IGNORECASE | re.DOTALL)
+        answer_match = re.search(r"answer:\s*(.*?)\nCriteria", response.content, re.IGNORECASE | re.DOTALL)
+        criteria_match = re.search(r"criteria:\s*(.*?)\n(Subtask:|$)", response.content, re.IGNORECASE | re.DOTALL)
+        subtask_match = re.search(r"subtask:\s*(.*)", response.content, re.IGNORECASE)
+
+        question = answer = subtask = None
+
+        if question_match:
+            # Extract the matched groups
+            question = question_match.group(1).strip()
+        if answer_match:
+            answer = answer_match.group(1).strip()
+        if criteria_match:
+            criteria = criteria_match.group(1).strip()
+        if subtask_match:
+            subtask = subtask_match.group(1).strip()
+
+        return QAData(question=question, answer=answer, criteria=criteria, subtask=subtask)
+
+    def _format_response(self, response: Response) -> List[QAData]:
+        try:
+            qa_data = []
+            content = json.loads(response.content)
+            for subtask, message in content.items():
+                subtask = subtask.lower()
+                message_lower = {k.lower(): v for k, v in message.items()}
+                try:
+                    question = message_lower["question"]
+                    answer = message_lower["answer"]
+                    qa_data.append(QAData(question=question, answer=answer, subtask=subtask))
+                except KeyError as e:
+                    logger.error(f"Failed to parse response: {message}")
+                    logger.error(f"Error: {e}")
+            return qa_data
+        except Exception as e:
+            logger.error(f"Failed to format response: {e}")
+            return []
diff --git a/tools/live_bench/live_bench/data_generator/response.py b/tools/live_bench/live_bench/data_generator/response.py
new file mode 100644
index 00000000..9eed882b
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/response.py
@@ -0,0 +1,12 @@
+class Response(object):
+    def __init__(self, success: bool, content: str, full_log: dict):
+        self.success = success
+        self.content = content
+        self.full_log = full_log
+
+    def to_dict(self):
+        return {
+            "success": self.success,
+            "content": self.content,
+            "full_log": self.full_log,
+        }
diff --git a/tools/live_bench/live_bench/data_generator/score_getter.py b/tools/live_bench/live_bench/data_generator/score_getter.py
new file mode 100644
index 00000000..89553a08
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/score_getter.py
@@ -0,0 +1,157 @@
+import os
+import json
+import random
+import openai
+import anthropic
+from abc import ABC, abstractmethod
+from typing import List
+from PIL import Image
+from live_bench.screen_shoter import ScreenImage
+from live_bench.data_generator.qa_generator import Response
+from live_bench.data_generator.utils.gpt4v import format_gpt4v_images, gpt4v_generate_response
+from live_bench.data_generator.utils.claude import format_claude_images, claude_generate_response
+
+
+class Score(object):
+    def __init__(self, score: int, reason: str):
+        self.score = score
+        self.reason = reason
+
+
+class ScoreGetter(ABC):
+    def get_name(self):
+        return self.name
+
+    @abstractmethod
+    def get_score(self, question: str, answer: str, images: ScreenImage):
+        raise NotImplementedError("get_score not implemented")
+
+    def __call__(self, question: str, answer: str, images: ScreenImage, **kwargs):
+        return self.get_score(question, answer, images, **kwargs)
+
+
+class ScoreGetterRegistry:
+    def __init__(self):
+        self.score_getters = {}
+
+    def register_score_getter(self, name):
+        def decorator(cls):
+            self.score_getters[name] = cls
+            cls.name = name
+            return cls
+
+        return decorator
+
+    def get_score_getter(self, name) -> ScoreGetter:
+        return self.score_getters[name]
+
+    def get_random_score_getter(self) -> ScoreGetter:
+        return random.choice(list(self.score_getters.values()))
+
+
+generator_registry = ScoreGetterRegistry()
+
+
+def register_score_getter(name):
+    return generator_registry.register_score_getter(name)
+
+
+def get_score_getter(name, *args, **kwargs) -> ScoreGetter:
+    return generator_registry.get_score_getter(name)(*args, **kwargs)
+
+
+def get_random_score_getter(*args, **kwargs) -> ScoreGetter:
+    return generator_registry.get_random_score_getter()(*args, **kwargs)
+
+
+@register_score_getter("gpt4v")
+class GPT4VScoreGetter(ScoreGetter):
+    def __init__(self, prompt: str = os.path.join(os.path.dirname(__file__), "score_prompt.md"), model="gpt-4-turbo", example_path=os.path.join(os.path.dirname(__file__), "example")):
+        super().__init__()
+        if os.path.exists(prompt):
+            with open(prompt, "r") as f:
+                self.prompt = f.read()
+        else:
+            self.prompt = prompt
+        API_KEY = os.getenv("OPENAI_API_KEY")
+        if not API_KEY:
+            raise ValueError("OPENAI_API_KEY environment variable not set.")
+        self.api_key = API_KEY
+        self.client = openai.OpenAI(api_key=self.api_key)
+        self.model = model
+        if os.path.exists(example_path) and os.path.isfile(os.path.join(example_path, "example_score_input.md")):
+            with open(example_path, "r") as f:
+                self.example = f.read()
+        else:
+            self.example = None
+
+    def _format_prompt(self, question: str, answer: str, images: List[Image.Image]):
+        prompt = [{"role": "system", "content": self.prompt}]
+        messages = []
+        for image in images:
+            messages.append(format_gpt4v_images(image))
+        messages.append({"type": "text", "text": f"Question: {question}\nQuestioner's Answer: {answer}"})
+        messages.append({"type": "text", "text": 'You should format you answer into json format like this: {"score": 10, "reason": "some reason"}'})
+        prompt.append({"role": "user", "content": messages})
+        return prompt
+
+    def get_score(self, question: str, answer: str, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs) -> Score:
+        prompt = self._format_prompt(question, answer, images)
+        try:
+            response = gpt4v_generate_response(client=self.client, model=self.model, messages=prompt, max_tokens=max_tokens, max_try_times=max_try_times, json_format=True, **kwargs)
+            if response.success:
+                content = json.loads(response.content)
+                score = content.get("score", None)
+                reason = content.get("reason", None)
+                return Score(score=score, reason=reason)
+            else:
+                return Score(score=None, reason=response.content)
+        except Exception as e:
+            return Score(score=None, reason=str(e))
+
+
+@register_score_getter("claude")
+class ClaudeScoreGetter(ScoreGetter):
+    def __init__(self, prompt: str = os.path.join(os.path.dirname(__file__), "score_prompt.md"), model="claude-3-opus-20240229", example_path=os.path.join(os.path.dirname(__file__), "example")):
+        super().__init__()
+        if os.path.exists(prompt):
+            with open(prompt, "r") as f:
+                self.prompt = f.read()
+        else:
+            self.prompt = prompt
+        API_KEY = os.getenv("ANTHROPIC_API_KEY")
+        if not API_KEY:
+            raise ValueError("ANTHROPIC_API_KEY environment variable not set.")
+        self.api_key = API_KEY
+        self.client = anthropic.Anthropic(api_key=self.api_key)
+        self.model = model
+        if os.path.exists(example_path) and os.path.isfile(os.path.join(example_path, "example_score_input.md")):
+            with open(example_path, "r") as f:
+                self.example = f.read()
+        else:
+            self.example = None
+
+    def _format_prompt(self, question: str, answer: str, images: List[Image.Image]):
+        # prompt = [{"role": "system", "content": self.prompt}]
+        prompt = []
+        messages = []
+        for image in images:
+            messages.append(format_claude_images(image))
+        messages.append({"type": "text", "text": f"Question: {question}\nQuestioner's Answer: {answer}"})
+        messages.append({"type": "text", "text": 'You should format you answer into json format like this: {"score": 10, "reason": "some reason"}'})
+        prompt.append({"role": "user", "content": messages})
+        return prompt
+
+    def get_score(self, question: str, answer: str, images: ScreenImage, *, max_tokens=4096, max_try_times=5, **kwargs) -> Score:
+        prompt = self._format_prompt(question, answer, images)
+        try:
+            response = claude_generate_response(self.client, self.model, prompt, self.prompt, max_tokens, max_try_times, **kwargs)
+            if response.success:
+                content = json.loads(response.content)
+                score = content.get("score", None)
+                reason = content.get("reason", None)
+                return Score(score=score, reason=reason)
+            else:
+                return Score(score=None, reason=response.content)
+        except Exception as e:
+            return Score(score=None, reason=str(e))
diff --git a/tools/live_bench/live_bench/data_generator/score_prompt.md b/tools/live_bench/live_bench/data_generator/score_prompt.md
new file mode 100644
index 00000000..1de806a7
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/score_prompt.md
@@ -0,0 +1,20 @@
+Based on the multi-round Q&A regarding the image, please evaluate each question and answer from the multi-round Q&A based on the image for their authenticity (whether the information can be directly obtained from the image or reasonably inferred) and logical coherence. For each Q&A pair, provide a rating from 1 to 10, where 1 indicates very poor and 10 indicates excellent. Additionally, please provide a brief explanation for each rating.
+
+Here are the criteria for evaluating the Q&A pairs:
+
+### 1. Authenticity (5 points)
+- **5 Points**: The information is directly observable in the image or can be reasonably inferred with strong evidence.
+- **3 Points**: The information has a plausible connection to the image but requires assumptions that are not strongly supported by the image.
+- **1 Point**: The information cannot be observed or reasonably inferred from the image; it seems unrelated or speculative.
+
+### 2. Logical Coherence (3 points)
+- **3 Points**: The answer logically follows from the question and maintains consistency with the image context.
+- **2 Points**: There are minor logical gaps or inconsistencies in the answer relative to the question.
+- **1 Point**: The answer is logically inconsistent or contradictory to the question or image context.
+
+### 3. Clarity and Precision (2 points)
+- **2 Points**: The question and answer are clearly articulated and precisely address specifics of the image.
+- **1 Point**: The question or answer is somewhat vague or overly general, lacking specific details related to the image.
+- **0 Points**: The question or answer is unclear or too ambiguous to determine relevance to the image.
+
+Each Q&A pair can score a maximum of 10 points. The sum of points from these three categories determines the final score for each pair. Provide a brief explanation for each rating, focusing on how well the Q&A adheres to these criteria.
diff --git a/tools/live_bench/live_bench/data_generator/utils/__init__.py b/tools/live_bench/live_bench/data_generator/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tools/live_bench/live_bench/data_generator/utils/claude.py b/tools/live_bench/live_bench/data_generator/utils/claude.py
new file mode 100644
index 00000000..9678b727
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/utils/claude.py
@@ -0,0 +1,42 @@
+from PIL import Image
+import io
+import base64
+from live_bench.data_generator.response import Response
+import logging
+from time import sleep
+
+logger = logging.getLogger("lmms-eval")
+
+
+def format_claude_images(image: Image.Image):
+    buffered = io.BytesIO()
+    image.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+    return {
+        "type": "image",
+        "source": {
+            "type": "base64",
+            "media_type": "image/png",
+            "data": img_str,
+        },
+    }
+
+
+def claude_generate_response(client, model, messages, system, max_tokens: int, max_try_times, **kwargs):
+    messages.append({"role": "assistant", "content": "{"})
+
+    def _generate():
+        return client.messages.create(model=model, messages=messages, max_tokens=max_tokens, system=system, **kwargs)
+
+    for times in range(max_try_times):
+        try:
+            response = _generate()
+            return Response(success=True, content="{" + response.content[0].text, full_log={"input": messages, "output": response})
+        except Exception as e:
+            logger.error(f"Failed to generate response: {e}")
+            if times < max_try_times - 1:
+                logger.info(f"Retrying... ({times+1}/{max_try_times})")
+                sleep(3)
+            else:
+                logger.error("Failed to generate response after retrying.")
+                return Response(success=False, content=str(e), full_log={"input": messages, "output": None})
diff --git a/tools/live_bench/live_bench/data_generator/utils/extract_infomation.py b/tools/live_bench/live_bench/data_generator/utils/extract_infomation.py
new file mode 100644
index 00000000..6946c815
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/utils/extract_infomation.py
@@ -0,0 +1,116 @@
+import os
+import openai
+import logging
+from bs4 import BeautifulSoup
+import requests
+from live_bench.data_generator.utils.gpt4v import gpt4v_generate_response, format_gpt4v_images
+from live_bench.screen_shoter import ScreenImage
+from live_bench.websites import Website
+from live_bench.data_generator.response import Response
+
+logger = logging.getLogger("live-bench")
+
+
+GPT4V_EXTRACT_TEXT_PROMPT: str = """\
+These are the images of the website that we have captured. Please extract the text from the website.
+You should extract the text from the website as detailed as possible.
+Only output the text extracted from the website, do not include any other information.
+"""
+
+GPT4V_FIND_IMAGES_FEATURES_PROMPT: str = """\
+This is a screenshot from a news website. Your task is to identify the meaningful images in this screenshot and extract relevant information about these images, such as the environment depicted, the actions and expressions of the people, and the connection between these images and the corresponding text. You need to think deeply about these images and provide as much detailed and useful information as possible.
+"""
+
+GPT4V_THINK_DIFFERENTLY_PROMPT: str = """\
+What makes this website different from other websites? What is special about its news? Since it is a news website, where is the "new" reflected? Do not give a generalized answer; you need to provide detailed answers based on the specific content of each news article and the accompanying illustrations.
+"""
+
+
+class ImageInfomation(object):
+    def __init__(self, text=None, image_features=None, differnt_points=None):
+        self.text = text
+        self.image_features = image_features
+        self.differnt_points = differnt_points
+
+    def to_dict(self):
+        res = {}
+        if self.text:
+            res["Text Extracted in the HTML"] = self.text
+        if self.image_features:
+            res["Image Features"] = self.image_features
+        if self.differnt_points:
+            res["Interesting Points"] = self.differnt_points
+        return res
+
+    def __str__(self):
+        return self.get_info()
+
+    def get_info(self):
+        res_list = [f"## {key}\n\n{value}" for key, value in self.to_dict().items()]
+        if res_list:
+            return "**Here is something you can take as reference.**\n\n" + "\n\n".join(res_list)
+        else:
+            return ""
+
+
+class InfomationExtractor(object):
+    def __init__(self, model="gpt-4-turbo", openai_api_key=None):
+        if not openai_api_key:
+            openai_api_key = os.getenv("OPENAI_API_KEY")
+            if not openai_api_key:
+                raise ValueError("OPENAI_API_KEY environment variable not set.")
+        self.client = openai.OpenAI(api_key=openai_api_key)
+        self.model = model
+
+    def extract_text_from_html(self, url):
+        response = requests.get(url)
+        soup = BeautifulSoup(response.text, "html.parser")
+
+        text = "\n".join(soup.stripped_strings)
+        return text
+
+    def extract_text_from_html_using_gpt4v(self, screen_image: ScreenImage, **kwargs) -> Response:
+        website: Website = screen_image.website
+        if website.url:
+            url = website.url
+            text = self.extract_text_from_html(url)
+            text = f"Below is the text extracted from the website {url} for you to take reference:\n{text}"
+        else:
+            text = ""
+        text = f"{GPT4V_EXTRACT_TEXT_PROMPT}\n{text}"
+        messages = [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": text}] + format_gpt4v_images(screen_image.images),
+            }
+        ]
+        response = gpt4v_generate_response(messages, model=self.model, client=self.client, json_format=False, **kwargs)
+        return response
+
+    def extract_infomation(self, screen_image: ScreenImage, **kwargs) -> ImageInfomation:
+        ocrs = self.extract_text_from_html_using_gpt4v(screen_image)
+        infomation = ImageInfomation()
+        if ocrs.success:
+            ocrs = f"Below is the text extracted from the website for you to take reference:\n{ocrs.content}"
+            infomation.text = ocrs
+        else:
+            ocrs = ""
+        messages = [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": f"{GPT4V_FIND_IMAGES_FEATURES_PROMPT}\n{ocrs}"}] + format_gpt4v_images(screen_image.images),
+            }
+        ]
+        response = gpt4v_generate_response(messages, model=self.model, client=self.client, json_format=False, **kwargs)
+        if response.success:
+            infomation.image_features = response.content
+        messages = [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": f"{GPT4V_THINK_DIFFERENTLY_PROMPT}\n\n{str(infomation)}"}] + format_gpt4v_images(screen_image.images),
+            }
+        ]
+        response = gpt4v_generate_response(messages, model=self.model, client=self.client, json_format=False, **kwargs)
+        if response.success:
+            infomation.differnt_points = response.content
+        return infomation
diff --git a/tools/live_bench/live_bench/data_generator/utils/gemini.py b/tools/live_bench/live_bench/data_generator/utils/gemini.py
new file mode 100644
index 00000000..99c1eef6
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/utils/gemini.py
@@ -0,0 +1,37 @@
+import google.generativeai as genai
+from time import sleep
+from live_bench.data_generator.response import Response
+import logging
+from google.generativeai.types import HarmCategory, HarmBlockThreshold
+
+logger = logging.getLogger("lmms-eval")
+
+
+def gemini_generate_response(client: genai.GenerativeModel, messages, max_tokens: int, max_try_times, **kwargs):
+    generation_config = genai.GenerationConfig(max_output_tokens=max_tokens)
+
+    def _generate():
+        return client.generate_content(
+            messages,
+            generation_config=generation_config,
+            safety_settings={
+                HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
+            },
+            **kwargs,
+        )
+
+    for times in range(max_try_times):
+        try:
+            response = _generate()
+            return Response(success=True, content=response.text, full_log={"input": messages, "output": response})
+        except Exception as e:
+            logger.error(f"Failed to generate response: {e}")
+            if times < max_try_times - 1:
+                logger.info(f"Retrying... ({times+1}/{max_try_times})")
+                sleep(3)
+            else:
+                logger.error("Failed to generate response after retrying.")
+                return Response(success=False, content=str(e), full_log={"input": messages, "output": None})
diff --git a/tools/live_bench/live_bench/data_generator/utils/gpt4v.py b/tools/live_bench/live_bench/data_generator/utils/gpt4v.py
new file mode 100644
index 00000000..2ee70794
--- /dev/null
+++ b/tools/live_bench/live_bench/data_generator/utils/gpt4v.py
@@ -0,0 +1,72 @@
+from PIL import Image
+import io
+import base64
+from live_bench.data_generator.response import Response
+import logging
+from time import sleep
+
+logger = logging.getLogger("lmms-eval")
+
+
+def format_gpt4v_images(image):
+    if isinstance(image, Image.Image):
+        buffered = io.BytesIO()
+        image.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+        return {
+            "type": "image_url",
+            "image_url": {
+                "url": f"data:image/png;base64,{img_str}",
+            },
+        }
+    elif isinstance(image, list):
+        return [format_gpt4v_images(img) for img in image]
+    else:
+        raise ValueError(f"Unsupported image type: {type(image)}")
+
+
+def format_printable_messages(messages):
+    for message in messages:
+        if "content" in message and isinstance(message["content"], list):
+            for content in message["content"]:
+                if "type" in content and content["type"] == "image_url":
+                    content["image_url"]["url"] = "<image_url>"
+    return messages
+
+
+def gpt4v_generate_response(messages, *, client=None, model="gpt-4-turbo", max_tokens: int = 4096, max_try_times: int = 5, json_format="auto", test=False, **kwargs) -> Response:
+    if json_format == "auto":
+        json_format = False
+        for message in messages:
+            if message.get("role") == "user":
+                contents = message.get("content", [])
+                if isinstance(contents, str):
+                    if "json" in contents:
+                        json_format = True
+                        break
+                else:
+                    for content in contents:
+                        if content.get("type", None) == "text" and "json" in content.get("text", ""):
+                            json_format = True
+                            break
+
+    if json_format:
+        response_format = {"type": "json_object"}
+    else:
+        response_format = None
+
+    def _generate():
+        return client.chat.completions.create(model=model, messages=messages, max_tokens=max_tokens, response_format=response_format, **kwargs)
+
+    for times in range(max_try_times):
+        try:
+            response = _generate()
+            return Response(success=True, content=response.choices[0].message.content, full_log={"input": format_printable_messages(messages), "output": response.choices[0].message.content})
+        except Exception as e:
+            logger.error(f"Failed to generate response: {e}")
+            if times < max_try_times - 1:
+                logger.info(f"Retrying... ({times+1}/{max_try_times})")
+                sleep(3)
+            else:
+                logger.error("Failed to generate response after retrying.")
+                return Response(success=False, content=str(e), full_log={"input": format_printable_messages(messages), "output": None})
diff --git a/tools/live_bench/live_bench/driver/.gitignore b/tools/live_bench/live_bench/driver/.gitignore
new file mode 100644
index 00000000..0ef18421
--- /dev/null
+++ b/tools/live_bench/live_bench/driver/.gitignore
@@ -0,0 +1 @@
+extensions/
diff --git a/tools/live_bench/live_bench/driver/__init__.py b/tools/live_bench/live_bench/driver/__init__.py
new file mode 100644
index 00000000..5ac815d2
--- /dev/null
+++ b/tools/live_bench/live_bench/driver/__init__.py
@@ -0,0 +1 @@
+from live_bench.driver.load_driver import load_driver
diff --git a/tools/live_bench/live_bench/driver/load_driver.py b/tools/live_bench/live_bench/driver/load_driver.py
new file mode 100644
index 00000000..c0ca7528
--- /dev/null
+++ b/tools/live_bench/live_bench/driver/load_driver.py
@@ -0,0 +1,71 @@
+import os
+import zipfile
+import requests
+from selenium import webdriver
+from webdriver_manager.chrome import ChromeDriverManager
+from webdriver_manager.core.os_manager import ChromeType
+from selenium.webdriver.chrome.options import Options
+
+import undetected_chromedriver as uc
+
+
+def load_driver(
+    window_size="auto",
+    headless=True,
+    driver="undetected_chromedriver",
+    driver_version=None,
+    chrome_type="CHROME",
+    adblock=True,
+    adblock_version="6.0.2-mv3",
+    extension_cache_dir=os.path.join(os.path.dirname(__file__), "extensions"),
+    *,
+    service=None,
+    additional_options=None,
+):
+    options = Options()
+    if service is None:
+        chrome_type = chrome_type.upper()
+        if chrome_type == "CHROMIUM":
+            chrome_type = ChromeType.CHROMIUM
+        elif chrome_type == "CHROME":
+            chrome_type = ChromeType.GOOGLE
+        elif chrome_type == "BRAVE":
+            chrome_type = ChromeType.BRAVE
+        service = ChromeDriverManager(driver_version=driver_version, chrome_type=chrome_type).install()
+    if headless:
+        options.add_argument("--headless")
+    if adblock:
+        try:
+            adblock_url = f"https://code.getadblock.com/releases/adblockchrome-{adblock_version}.zip"
+            adblock_path = os.path.join(extension_cache_dir, f"adblockchrome-{adblock_version}")
+            if not os.path.isdir(adblock_path):
+                os.makedirs(os.path.join(adblock_path, ".."), exist_ok=True)
+                # Download the adblock zip file
+                response = requests.get(adblock_url)
+                with open(f"{adblock_path}.zip", "wb") as file:
+                    file.write(response.content)
+                # Unzip the downloaded file
+                with zipfile.ZipFile(f"{adblock_path}.zip", "r") as zip_ref:
+                    zip_ref.extractall(adblock_path)
+                # Remove the zip file after extraction
+                os.remove(f"{adblock_path}.zip")
+            options.add_argument(f"--load-extension={os.path.abspath(adblock_path)}")
+        except Exception as e:
+            print(f"Error loading adblock extension: {e}")
+    if driver == "undetected_chromedriver":
+        driver = uc.Chrome(headless=headless, options=options, driver_executable_path=service)
+        if window_size != "auto":
+            driver.set_window_size(*window_size)
+        return driver
+    elif driver == "chrome":
+        options = Options()
+        if additional_options is not None:
+            for option in additional_options:
+                options.add_argument(option)
+        service = webdriver.chrome.service.Service(service)
+        driver = webdriver.Chrome(service=service, options=options)
+        if window_size != "auto":
+            driver.set_window_size(*window_size)
+        return driver
+    else:
+        raise ValueError(f"Unknown driver: {driver}")
diff --git a/tools/live_bench/live_bench/screen_shoter/__init__.py b/tools/live_bench/live_bench/screen_shoter/__init__.py
new file mode 100644
index 00000000..e5ea9856
--- /dev/null
+++ b/tools/live_bench/live_bench/screen_shoter/__init__.py
@@ -0,0 +1,2 @@
+from live_bench.screen_shoter.screen_shoter import ScreenShoter, register_shoter, get_shoter
+from live_bench.screen_shoter.screen import ScreenImage
diff --git a/tools/live_bench/live_bench/screen_shoter/screen.py b/tools/live_bench/live_bench/screen_shoter/screen.py
new file mode 100644
index 00000000..5817b8cc
--- /dev/null
+++ b/tools/live_bench/live_bench/screen_shoter/screen.py
@@ -0,0 +1,30 @@
+import io
+import base64
+
+from PIL import Image
+from typing import List, Tuple
+
+from live_bench.websites import Website
+
+
+def image_to_base64(image: Image.Image) -> str:
+    buffered = io.BytesIO()
+    image.save(buffered, format="PNG")
+    return base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+
+class ScreenImage(object):
+    def __init__(self, images: List[Image.Image], website: Website, shoter: str, screen_size: Tuple[int, int], capture_datetime: str):
+        self.images = images
+        self.website = website
+        self.shoter = shoter
+        self.screen_size = screen_size
+        self.capture_datetime = capture_datetime
+
+    def to_dict(self):
+        return {"images": self.images, "website": self.website.get_info(), "shoter": self.shoter, "screen_size": self.screen_size, "capture_datetime": self.capture_datetime}
+
+    def to_output_dict(self):
+        output = self.to_dict()
+        output["images"] = [image_to_base64(image) for image in self.images]
+        return output
diff --git a/tools/live_bench/live_bench/screen_shoter/screen_shoter.py b/tools/live_bench/live_bench/screen_shoter/screen_shoter.py
new file mode 100644
index 00000000..6069cd26
--- /dev/null
+++ b/tools/live_bench/live_bench/screen_shoter/screen_shoter.py
@@ -0,0 +1,141 @@
+from selenium import webdriver
+from PIL import Image
+from live_bench.websites import Website
+from live_bench.screen_shoter.screen import ScreenImage
+from typing import List
+from abc import ABC, abstractmethod
+from datetime import datetime
+from PIL import Image
+import os
+import io
+import logging
+
+logger = logging.getLogger("lmms-eval")
+
+
+class ScreenShoter(ABC):
+    def __init__(self, screen_size=(1024, 1024)):
+        self.screen_size = screen_size
+
+    def capture(self, driver: webdriver.Chrome, website: Website) -> ScreenImage:
+        if driver is not None:
+            website.visit(driver)
+            if self.screen_size != "auto":
+                driver.set_window_size(self.screen_size[0], self.screen_size[1])
+            else:
+                driver.set_window_size(1024, 1024)
+                page_width = driver.execute_script("return document.body.scrollWidth")
+                driver.set_window_size(page_width, 1024)
+        # print("Screen size:", driver.get_window_size())
+        images = self.get_screenshot(driver)
+        return ScreenImage(images, website, self.get_name(), self.screen_size, datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
+
+    def __call__(self, driver: webdriver.Chrome, website: Website) -> List[Image.Image]:
+        return self.capture(driver, website)
+
+    def get_name(self) -> str:
+        raise NotImplementedError("get_name not implemented")
+
+    @abstractmethod
+    def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]:
+        pass
+
+
+class ScreenShoterRegistry:
+    def __init__(self):
+        self.shoters = {}
+
+    def register_shoter(self, name):
+        def decorator(cls):
+            self.shoters[name] = cls
+            cls.get_name = lambda self: name
+            return cls
+
+        return decorator
+
+    def get_shoter(self, name) -> ScreenShoter:
+        return self.shoters[name]
+
+
+shoter_registry = ScreenShoterRegistry()
+
+
+def register_shoter(name):
+    return shoter_registry.register_shoter(name)
+
+
+def get_shoter(name, *args, **kwargs) -> ScreenShoter:
+    return shoter_registry.get_shoter(name)(*args, **kwargs)
+
+
+@register_shoter("human")
+class HumanScreenShoter(ScreenShoter):
+    def __init__(self, screen_size=None):
+        super().__init__(screen_size)
+
+    def capture(self, driver: webdriver.Chrome, website: Website) -> ScreenImage:
+        path = website.get_path()
+        images = []
+
+        def get_image(path):
+            try:
+                with open(path, "rb") as f:
+                    image_data = f.read()
+                image = Image.open(io.BytesIO(image_data))
+                images.append(image)
+            except Exception as e:
+                logger.error(f"Error loading image {path}: {e}")
+
+        if os.path.isdir(path):
+            for root, dirs, files in os.walk(path):
+                for file_name in files:
+                    get_image(os.path.join(root, file_name))
+        else:
+            try:
+                get_image(path)
+            except Exception as e:
+                logger.error(f"Error loading image {path}: {e}")
+        if not images:
+            raise ValueError(f"No images found in {path}")
+        return ScreenImage(images, website, self.get_name(), self.screen_size, datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
+
+    def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]:
+        return []
+
+
+@register_shoter("single_screen")
+class SingleScreenShoter(ScreenShoter):
+    def __init__(self, screen_size=(1024, 1024)):
+        super().__init__(screen_size)
+
+    def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]:
+        screenshot = driver.get_screenshot_as_png()
+        return [Image.open(io.BytesIO(screenshot))]
+
+
+@register_shoter("rolling_screen")
+class RollingScreenShoter(ScreenShoter):
+    def __init__(self, screen_size=(1024, 1024)):
+        super().__init__(screen_size)
+
+    def get_screenshot(self, driver: webdriver.Chrome) -> List[Image.Image]:
+        screenshots = []
+        # Scroll to the top of the page before taking the first screenshot
+        driver.execute_script("window.scrollTo(0, 0)")
+        # Get the total height of the web page
+        total_height = driver.execute_script("return document.body.parentNode.scrollHeight")
+        # Get the viewport height
+        viewport_height = driver.execute_script("return window.innerHeight")
+        # Initialize the current scroll position
+        current_scroll_position = 0
+
+        # Scroll through the page and take screenshots
+        while current_scroll_position < total_height:
+            # Take screenshot and append to the list
+            screenshot = driver.get_screenshot_as_png()
+            screenshots.append(Image.open(io.BytesIO(screenshot)))
+            # Scroll down by the viewport height
+            current_scroll_position += viewport_height
+            driver.execute_script(f"window.scrollTo(0, {current_scroll_position})")
+
+        return screenshots
diff --git a/tools/live_bench/live_bench/view.ipynb b/tools/live_bench/live_bench/view.ipynb
new file mode 100644
index 00000000..71d99d91
--- /dev/null
+++ b/tools/live_bench/live_bench/view.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading data: 100%|██████████| 470k/470k [00:00<00:00, 575kB/s]\n",
+      "Generating test split: 100%|██████████| 9/9 [00:00<00:00, 341.15 examples/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset(\"lmms-lab/LiveBench\", \"2024-06\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    test: Dataset({\n",
+       "        features: ['id', 'images', 'website', 'question', 'answer', 'subtask', 'data_generator', 'checker', 'date_time', 'screen_shoter', 'screen_size', 'score', 'reason', 'scorer_name'],\n",
+       "        num_rows: 9\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>images</th>\n",
+       "      <th>website</th>\n",
+       "      <th>question</th>\n",
+       "      <th>answer</th>\n",
+       "      <th>subtask</th>\n",
+       "      <th>data_generator</th>\n",
+       "      <th>checker</th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>screen_shoter</th>\n",
+       "      <th>screen_size</th>\n",
+       "      <th>score</th>\n",
+       "      <th>reason</th>\n",
+       "      <th>scorer_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Look at the image provided. Two of the article...</td>\n",
+       "      <td>The shared theme is the legal situation of Hun...</td>\n",
+       "      <td>Contextual Analysis</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-12 01:14:15</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>10</td>\n",
+       "      <td>The answer accurately identifies the article d...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Based on the provided image, what is the main ...</td>\n",
+       "      <td>Ukraine has launched missiles at sites within ...</td>\n",
+       "      <td>Basic Understanding</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-12 01:14:15</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>10</td>\n",
+       "      <td>The information provided in the answer directl...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Based on the juxtaposition of the articles on ...</td>\n",
+       "      <td>The two narratives are the personal and legal ...</td>\n",
+       "      <td>Contextual Analysis</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-12 01:14:15</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>8</td>\n",
+       "      <td>The answer is mostly authentic as the convicti...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Examining the headline and image, what visual ...</td>\n",
+       "      <td>The image showcasing a relatively unscathed st...</td>\n",
+       "      <td>Contextual Analysis</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-12 01:14:15</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>8</td>\n",
+       "      <td>The answer is relevant and provides a plausibl...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Based on the juxtaposition of the articles on ...</td>\n",
+       "      <td>The audience might infer a potential media bia...</td>\n",
+       "      <td>Deeper Implications</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-12 01:14:15</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>8</td>\n",
+       "      <td>The response provides a reasoned inference bas...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Examining the image and the headline \"What son...</td>\n",
+       "      <td>Potential narratives could include: the impact...</td>\n",
+       "      <td>Broader Implications</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-12 01:14:15</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Authenticity (4/5): The information about it b...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Based on the visual framing and placement of t...</td>\n",
+       "      <td>The BBC seemingly prioritizes Hunter Biden's t...</td>\n",
+       "      <td>Contextual Analysis</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-12 01:14:15</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>8</td>\n",
+       "      <td>The answer effectively compares domestic polit...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Considering the use of the word \"LIVE\" in the ...</td>\n",
+       "      <td>The use of \"LIVE\" could lead the public to per...</td>\n",
+       "      <td>Deeper Implications</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-12 01:14:15</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>10</td>\n",
+       "      <td>The answer provided is directly related to the...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>[{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...</td>\n",
+       "      <td>{'url': 'https://www.bbc.com/'}</td>\n",
+       "      <td>Based on the image alone, what elements sugges...</td>\n",
+       "      <td>The juxtaposition of the image depicting Presi...</td>\n",
+       "      <td>Deeper Implications</td>\n",
+       "      <td>gpt4v</td>\n",
+       "      <td>gemini</td>\n",
+       "      <td>2024-06-12 01:14:15</td>\n",
+       "      <td>single_screen</td>\n",
+       "      <td>(1024, 1024)</td>\n",
+       "      <td>8</td>\n",
+       "      <td>The answer effectively discusses the potential...</td>\n",
+       "      <td>gpt4v</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id                                             images  \\\n",
+       "0   0  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "1   1  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "2   2  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "3   3  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "4   4  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "5   5  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "6   6  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "7   7  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "8   8  [{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00\\x00\\rIH...   \n",
+       "\n",
+       "                           website  \\\n",
+       "0  {'url': 'https://www.bbc.com/'}   \n",
+       "1  {'url': 'https://www.bbc.com/'}   \n",
+       "2  {'url': 'https://www.bbc.com/'}   \n",
+       "3  {'url': 'https://www.bbc.com/'}   \n",
+       "4  {'url': 'https://www.bbc.com/'}   \n",
+       "5  {'url': 'https://www.bbc.com/'}   \n",
+       "6  {'url': 'https://www.bbc.com/'}   \n",
+       "7  {'url': 'https://www.bbc.com/'}   \n",
+       "8  {'url': 'https://www.bbc.com/'}   \n",
+       "\n",
+       "                                            question  \\\n",
+       "0  Look at the image provided. Two of the article...   \n",
+       "1  Based on the provided image, what is the main ...   \n",
+       "2  Based on the juxtaposition of the articles on ...   \n",
+       "3  Examining the headline and image, what visual ...   \n",
+       "4  Based on the juxtaposition of the articles on ...   \n",
+       "5  Examining the image and the headline \"What son...   \n",
+       "6  Based on the visual framing and placement of t...   \n",
+       "7  Considering the use of the word \"LIVE\" in the ...   \n",
+       "8  Based on the image alone, what elements sugges...   \n",
+       "\n",
+       "                                              answer               subtask  \\\n",
+       "0  The shared theme is the legal situation of Hun...   Contextual Analysis   \n",
+       "1  Ukraine has launched missiles at sites within ...   Basic Understanding   \n",
+       "2  The two narratives are the personal and legal ...   Contextual Analysis   \n",
+       "3  The image showcasing a relatively unscathed st...   Contextual Analysis   \n",
+       "4  The audience might infer a potential media bia...   Deeper Implications   \n",
+       "5  Potential narratives could include: the impact...  Broader Implications   \n",
+       "6  The BBC seemingly prioritizes Hunter Biden's t...   Contextual Analysis   \n",
+       "7  The use of \"LIVE\" could lead the public to per...   Deeper Implications   \n",
+       "8  The juxtaposition of the image depicting Presi...   Deeper Implications   \n",
+       "\n",
+       "  data_generator checker            date_time  screen_shoter   screen_size  \\\n",
+       "0          gpt4v  gemini  2024-06-12 01:14:15  single_screen  (1024, 1024)   \n",
+       "1          gpt4v  gemini  2024-06-12 01:14:15  single_screen  (1024, 1024)   \n",
+       "2          gpt4v  gemini  2024-06-12 01:14:15  single_screen  (1024, 1024)   \n",
+       "3          gpt4v  gemini  2024-06-12 01:14:15  single_screen  (1024, 1024)   \n",
+       "4          gpt4v  gemini  2024-06-12 01:14:15  single_screen  (1024, 1024)   \n",
+       "5          gpt4v  gemini  2024-06-12 01:14:15  single_screen  (1024, 1024)   \n",
+       "6          gpt4v  gemini  2024-06-12 01:14:15  single_screen  (1024, 1024)   \n",
+       "7          gpt4v  gemini  2024-06-12 01:14:15  single_screen  (1024, 1024)   \n",
+       "8          gpt4v  gemini  2024-06-12 01:14:15  single_screen  (1024, 1024)   \n",
+       "\n",
+       "   score                                             reason scorer_name  \n",
+       "0     10  The answer accurately identifies the article d...       gpt4v  \n",
+       "1     10  The information provided in the answer directl...       gpt4v  \n",
+       "2      8  The answer is mostly authentic as the convicti...       gpt4v  \n",
+       "3      8  The answer is relevant and provides a plausibl...       gpt4v  \n",
+       "4      8  The response provides a reasoned inference bas...       gpt4v  \n",
+       "5      7  Authenticity (4/5): The information about it b...       gpt4v  \n",
+       "6      8  The answer effectively compares domestic polit...       gpt4v  \n",
+       "7     10  The answer provided is directly related to the...       gpt4v  \n",
+       "8      8  The answer effectively discusses the potential...       gpt4v  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset[\"test\"].to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "live_bench",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tools/live_bench/live_bench/websites/__init__.py b/tools/live_bench/live_bench/websites/__init__.py
new file mode 100644
index 00000000..a865d16e
--- /dev/null
+++ b/tools/live_bench/live_bench/websites/__init__.py
@@ -0,0 +1,2 @@
+from live_bench.websites.load_website import load_websites, load_websites_from_file
+from live_bench.websites.website import Website
diff --git a/tools/live_bench/live_bench/websites/load_website.py b/tools/live_bench/live_bench/websites/load_website.py
new file mode 100644
index 00000000..10976db7
--- /dev/null
+++ b/tools/live_bench/live_bench/websites/load_website.py
@@ -0,0 +1,34 @@
+import yaml
+import os
+from random import sample
+from live_bench.websites.website import Website, DefaultWebsite, HumanScreenShotWebsite
+
+
+def get_website(website_dict):
+    if "website_class" not in website_dict:
+        website_class = DefaultWebsite
+    else:
+        website_class = website_dict["website_class"]
+    url = website_dict["url"]
+    if "args" in website_dict:
+        return website_class(url, **website_dict["args"])
+    else:
+        return website_class(url)
+
+
+def load_websites(num_sample: int = -1):
+    website_list_path = os.path.join(os.path.dirname(__file__), "website_list.yaml")
+    with open(website_list_path, "r") as f:
+        website_list = yaml.full_load(f)["websites"]
+    if num_sample > 0:
+        website_list = sample(website_list, num_sample)
+    return [get_website(website_dict) for website_dict in website_list]
+
+
+def load_websites_from_file(file_path):
+    names = os.listdir(file_path)
+    websites = []
+    for name in names:
+        path = os.path.join(file_path, name)
+        websites.append(HumanScreenShotWebsite(path=path, name=name))
+    return websites
diff --git a/tools/live_bench/live_bench/websites/website.py b/tools/live_bench/live_bench/websites/website.py
new file mode 100644
index 00000000..327bad37
--- /dev/null
+++ b/tools/live_bench/live_bench/websites/website.py
@@ -0,0 +1,62 @@
+import time
+import os
+
+from webdriver_manager.core.driver import Driver
+from abc import ABC, abstractmethod
+
+
+class Website(ABC):
+    def __init__(self, url=None, name=None, path=None):
+        self.url = url
+        self.name = name
+        self.path = path
+        assert self.url is not None or self.path is not None, "Either url or path must be provided"
+
+    def get_path(self):
+        if self.url:
+            return self.url
+        else:
+            return self.path
+
+    def visit(self, driver: Driver):
+        self.pre_visit(driver)
+        driver.get(self.url)
+        self.post_visit(driver)
+
+    def get_info(self):
+        info = {}
+        if self.url:
+            info["url"] = self.url
+        if self.name:
+            info["name"] = self.name
+        return info
+
+    @abstractmethod
+    def pre_visit(self, driver: Driver):
+        raise NotImplementedError("pre_action not implemented")
+
+    @abstractmethod
+    def post_visit(self, driver: Driver):
+        raise NotImplementedError("post_action not implemented")
+
+
+class DefaultWebsite(Website):
+    def __init__(self, url, name=None):
+        super().__init__(url, name)
+
+    def pre_visit(self, driver: Driver):
+        pass
+
+    def post_visit(self, driver: Driver):
+        time.sleep(5)  # Wait for 5 seconds to allow adblock to finish
+
+
+class HumanScreenShotWebsite(Website):
+    def __init__(self, name=None, path=None):
+        super().__init__(name=name, path=path)
+
+    def pre_visit(self, driver: Driver):
+        pass
+
+    def post_visit(self, driver: Driver):
+        pass
diff --git a/tools/live_bench/live_bench/websites/website_list.yaml b/tools/live_bench/live_bench/websites/website_list.yaml
new file mode 100644
index 00000000..66f7cabd
--- /dev/null
+++ b/tools/live_bench/live_bench/websites/website_list.yaml
@@ -0,0 +1,79 @@
+websites:
+- url: https://www.bbc.com/
+  # can add below line to specify the class to use for this website
+  # website_class: !constructor website.DefaultWebsite
+  # can add args tag to specify the arguments to pass to the class constructor
+  # args:
+  #  arg1: value1
+  #  arg2: value2
+- url: https://www.bbc.com/news
+- url: https://www.bbc.com/sport
+- url: https://www.bbc.com/business
+- url: https://www.bbc.com/innovation
+- url: https://www.bbc.com/culture
+- url: https://www.bbc.com/travel
+- url: https://www.bbc.com/future-planet
+# - url: https://edition.cnn.com/
+# - url: https://edition.cnn.com/politics
+# - url: https://edition.cnn.com/entertainment
+# - url: https://edition.cnn.com/style
+# - url: https://www.bloomberg.com/economics
+# - url: https://www.bloomberg.com/industries
+# - url: https://www.bloomberg.com/technology
+# - url: https://www.bloomberg.com/politics
+# - url: https://www.bloomberg.com/opinion
+# - url: https://www.wsj.com/
+# - url: https://www.wsj.com/world/africa?mod=nav_top_subsection
+# - url: https://www.wsj.com/world/americas?mod=nav_top_subsection
+# - url: https://www.wsj.com/world/asia?mod=nav_top_subsection
+# - url: https://www.wsj.com/world/china?mod=nav_top_subsection
+# - url: https://www.wsj.com/world/europe?mod=nav_top_subsection
+# - url: https://www.wsj.com/world/middle-east?mod=nav_top_subsection
+# - url: https://www.wsj.com/world/india?mod=nav_top_subsection
+# - url: https://www.wsj.com/world/oceania?mod=nav_top_subsection
+# - url: https://www.wsj.com/world/russia?mod=nav_top_subsection
+# - url: https://www.wsj.com/world/uk?mod=nav_top_subsection
+# - url: https://www.wsj.com/science?mod=nav_top_subsection
+# - url: https://www.wsj.com/science/archaeology?mod=nav_top_subsection
+# - url: https://www.wsj.com/science/biology?mod=nav_top_subsection
+# - url: https://www.wsj.com/science/environment?mod=nav_top_subsection
+# - url: https://www.wsj.com/science/physics?mod=nav_top_subsection
+# - url: https://www.wsj.com/science/space-astronomy?mod=nav_top_subsection
+# - url: https://www.wsj.com/economy/central-banking?mod=nav_top_subsection
+# - url: https://www.wsj.com/economy/consumers?mod=nav_top_subsection
+# - url: https://www.wsj.com/economy/housing?mod=nav_top_subsection
+# - url: https://www.wsj.com/economy/jobs?mod=nav_top_subsection
+# - url: https://www.wsj.com/economy/trade?mod=nav_top_subsection
+# - url: https://www.wsj.com/economy/global
+# - url: https://www.wsj.com/tech/ai?mod=nav_top_subsection
+# - url: https://www.wsj.com/tech/biotech
+# - url: https://www.wsj.com/tech/cybersecurity?mod=nav_top_subsection
+# - url: https://www.wsj.com/tech/personal-tech?mod=nav_top_subsection
+# - url: https://www.reuters.com/
+# - url: https://www.reuters.com/business/aerospace-defense/
+# - url: https://www.reuters.com/business/autos-transportation/
+# - url: https://www.reuters.com/business/davos/
+# - url: https://www.reuters.com/business/energy/
+# - url: https://www.reuters.com/business/environment/
+# - url: https://www.reuters.com/business/finance/
+# - url: https://www.reuters.com/business/healthcare-pharmaceuticals/
+# - url: https://www.reuters.com/business/media-telecom/
+# - url: https://www.reuters.com/business/retail-consumer/
+# - url: https://www.reuters.com/business/future-of-health/
+# - url: https://www.reuters.com/business/future-of-money/
+# - url: https://www.reuters.com/business/take-five/
+# - url: https://www.reuters.com/business/world-at-work/
+# - url: https://www.reuters.com/breakingviews/
+# - url: https://www.reuters.com/technology/
+# - url: https://www.reuters.com/technology/cybersecurity/
+# - url: https://www.reuters.com/technology/space/
+# - url: https://www.reuters.com/technology/disrupted/
+# - url: https://www.reuters.com/technology/reuters-momentum/
+# - url: https://www.reuters.com/investigations/
+# - url: https://a16z.com/news-content/#latest
+# - url: https://news.ycombinator.com/
+# - url: https://www.reddit.com/?rdt=48006
+# - url: https://news.crunchbase.com/
+- url: https://www.cctv.com/
+- url: https://opinion.cctv.com/
+- url: https://sports.cctv.com/
diff --git a/tools/live_bench/pyproject.toml b/tools/live_bench/pyproject.toml
new file mode 100755
index 00000000..79956c40
--- /dev/null
+++ b/tools/live_bench/pyproject.toml
@@ -0,0 +1,47 @@
+[tool.black]
+line-length = 240
+
+[build-system]
+requires = ["setuptools>=42", "wheel", "setuptools_scm[tomli]>=6.3"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "live_bench"
+version = "0.0.1"
+authors = [
+    { name = "LMMMs-Lab Evaluation Team", email = "lmms_eval@outlook.com" },
+]
+description = "Live Bench"
+readme = "README.md"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+]
+requires-python = ">=3.9"
+license = { text = "MIT" }
+dependencies = [
+    "PyYAML >= 6.0.1",
+    "webdriver_manager >= 4.0.1",
+    "openai >= 1.32.0",
+    "google-generativeai >= 0.6.0",
+    "datasets >= 2.19.2",
+    "Pillow >= 10.3.0",
+    "selenium >= 4.21.0",
+    "undetected-chromedriver >= 3.5.5",
+    "anthropic >= 0.28.0",
+    "bs4 >= 0.0.2",
+]
+
+[tool.setuptools.packages.find]
+include = ["lmms_eval*"]
+
+[tool.setuptools.package-data]
+lmms_eval = ["**/*.yaml", "tasks/**/*"]
+
+[project.scripts]
+lmms-eval = "lmms_eval.__main__:cli_evaluate"
+
+[project.urls]
+Homepage = "https://lmms-lab.github.io/"
+Repository = "https://github.com/EvolvingLMMs-Lab/lmms-eval"
diff --git a/tools/live_bench/setup.py b/tools/live_bench/setup.py
new file mode 100755
index 00000000..b908cbe5
--- /dev/null
+++ b/tools/live_bench/setup.py
@@ -0,0 +1,3 @@
+import setuptools
+
+setuptools.setup()
diff --git a/tools/make_vatex.py b/tools/make_vatex.py
new file mode 100644
index 00000000..7882a38b
--- /dev/null
+++ b/tools/make_vatex.py
@@ -0,0 +1,31 @@
+from datasets import load_dataset, Dataset
+import json
+
+with open("data/vatex_public_test_english_v1.1.json", "r") as f:
+    data = json.load(f)
+
+for da in data:
+    da["url"] = "https://www.youtube.com/watch?v=" + da["videoID"]
+
+vatex_dataset = Dataset.from_list(data)
+# vatex_dataset.rename_columns({
+#     'videoID': 'video_name',
+#     'enCap': 'caption'
+# }) #if change name is needed
+hub_dataset_path = "lmms-lab/vatex_from_url"
+
+vatex_dataset.push_to_hub(repo_id=hub_dataset_path, split="test", config_name="vatex_test", token=True)
+
+with open("data/vatex_validation_v1.0.json", "r") as f:
+    data = json.load(f)
+for da in data:
+    da["url"] = "https://www.youtube.com/watch?v=" + da["videoID"]
+
+vatex_dataset = Dataset.from_list(data)
+# vatex_dataset.rename_columns({
+#     'videoID': 'video_name',
+#     'enCap': 'caption'
+# }) #if change name is needed
+hub_dataset_path = "lmms-lab/vatex_from_url"
+
+vatex_dataset.push_to_hub(repo_id=hub_dataset_path, split="validation", config_name="vatex_val_zh", token=True)

From dfaa4c261233c61eeca39dcc6db132c869116ab8 Mon Sep 17 00:00:00 2001
From: Pu Fanyi <FPU001@e.ntu.edu.sg>
Date: Thu, 4 Jul 2024 01:14:45 -0700
Subject: [PATCH 11/32] Small Fix: GPT (#130)

* small fix

* lint
---
 lmms_eval/models/gpt4v.py                     |  8 +++----
 lmms_eval/tasks/websrc/utils.py               | 12 +++++-----
 tools/live_bench/data_summary.ipynb           | 13 ++++++++---
 .../live_bench/data_generator/live_bench.py   | 22 +++++++++++++++++--
 .../data_generator/live_bench_data.py         | 17 ++++++++++++--
 5 files changed, 56 insertions(+), 16 deletions(-)

diff --git a/lmms_eval/models/gpt4v.py b/lmms_eval/models/gpt4v.py
index 729e73f7..1a47aa76 100755
--- a/lmms_eval/models/gpt4v.py
+++ b/lmms_eval/models/gpt4v.py
@@ -120,8 +120,8 @@ def generate_until(self, requests) -> List[str]:
 
         for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
             # encode, pad, and truncate contexts for this batch
-            # visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
-            visuals = [doc_to_visual(self.task_dict[task][split][0])]
+            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
+            # visuals = [doc_to_visual(self.task_dict[task][split][0])]
             visuals = self.flatten(visuals)
             imgs = []  # multiple images or frames for video
             for visual in visuals:
@@ -139,13 +139,13 @@ def generate_until(self, requests) -> List[str]:
                 payload["messages"].append(deepcopy(response_json))
                 payload["messages"][0]["content"].append({"type": "text", "text": contexts})
                 for img in imgs:
-                    payload["messages"][0]["content"].append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}})
+                    payload["messages"][0]["content"].append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}})
             else:
                 contexts = contexts.split(self.image_token)
                 for idx, img in enumerate(imgs):
                     payload["messages"].append(deepcopy(response_json))
                     payload["messages"][idx]["content"].append({"type": "text", "text": contexts[idx]})
-                    payload["messages"][idx]["content"].append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}})
+                    payload["messages"][idx]["content"].append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}})
 
                 # If n image tokens are in the contexts
                 # contexts will be splitted into n+1 chunks
diff --git a/lmms_eval/tasks/websrc/utils.py b/lmms_eval/tasks/websrc/utils.py
index 3cd024ee..6e2584a4 100644
--- a/lmms_eval/tasks/websrc/utils.py
+++ b/lmms_eval/tasks/websrc/utils.py
@@ -46,11 +46,13 @@ def websrc_process_results(doc, results):
 
     return {
         "websrc_squad_f1": websrc_ans,
-        "submission": {
-            websrc_ans["question_id"]: pred,
-        }
-        if "question_id" in websrc_ans
-        else None,
+        "submission": (
+            {
+                websrc_ans["question_id"]: pred,
+            }
+            if "question_id" in websrc_ans
+            else None
+        ),
     }
 
 
diff --git a/tools/live_bench/data_summary.ipynb b/tools/live_bench/data_summary.ipynb
index f7bc83e9..43048195 100644
--- a/tools/live_bench/data_summary.ipynb
+++ b/tools/live_bench/data_summary.ipynb
@@ -126,7 +126,7 @@
     }
    ],
    "source": [
-    "top_50_per_subtask = df.groupby('subtask').apply(lambda x: x.nlargest(50, 'score'))\n",
+    "top_50_per_subtask = df.groupby(\"subtask\").apply(lambda x: x.nlargest(50, \"score\"))\n",
     "top_50_per_subtask.reset_index(drop=True, inplace=True)\n",
     "len(top_50_per_subtask)"
    ]
@@ -188,11 +188,16 @@
     "from datasets import Dataset, Features\n",
     "import datasets\n",
     "\n",
+    "\n",
     "def gen():\n",
     "    for d in top_50_per_subtask:\n",
     "        yield d\n",
     "\n",
-    "data = Dataset.from_pandas(top_50_per_subtask, features=Features({\n",
+    "\n",
+    "data = Dataset.from_pandas(\n",
+    "    top_50_per_subtask,\n",
+    "    features=Features(\n",
+    "        {\n",
     "            \"id\": datasets.Value(\"int32\"),\n",
     "            \"images\": datasets.Sequence(datasets.Image()),\n",
     "            \"website\": datasets.Value(\"string\"),\n",
@@ -208,7 +213,9 @@
     "            \"score\": datasets.Value(\"int32\"),\n",
     "            \"reason\": datasets.Value(\"string\"),\n",
     "            \"scorer_name\": datasets.Value(\"string\"),\n",
-    "        }))"
+    "        }\n",
+    "    ),\n",
+    ")"
    ]
   },
   {
diff --git a/tools/live_bench/live_bench/data_generator/live_bench.py b/tools/live_bench/live_bench/data_generator/live_bench.py
index 4748d75a..84f72ec9 100644
--- a/tools/live_bench/live_bench/data_generator/live_bench.py
+++ b/tools/live_bench/live_bench/data_generator/live_bench.py
@@ -31,7 +31,9 @@ def get_qa_data(images: ScreenImage, qa_generator: QAGenerator, *, infomation_ge
     return qa_data, response
 
 
-def get_live_bench_data(driver, website: Website, screen_shoter: ScreenShoter, qa_generator: QAGenerator, checker: QAGenerator, infomation_getter: InfomationExtractor, test=False, scorer=None, score_threshold = 5) -> Tuple[List[LiveBenchData], Response]:
+def get_live_bench_data(
+    driver, website: Website, screen_shoter: ScreenShoter, qa_generator: QAGenerator, checker: QAGenerator, infomation_getter: InfomationExtractor, test=False, scorer=None, score_threshold=5
+) -> Tuple[List[LiveBenchData], Response]:
     images = screen_shoter.capture(driver, website)
     qa_data, logs = get_qa_data(images, qa_generator, test=test, infomation_getter=infomation_getter)
     data = []
@@ -65,7 +67,23 @@ def __init__(self, path: str = "lmms-lab/LiveBench", *, name="auto", split="test
 
     def clear(self):
         self.hf_data = Dataset.from_dict(
-            {"id": [], "images": [], "website": [], "question": [], "answer": [], "criteria": [], "subtask": [], "data_generator": [], "checker": [], "date_time": [], "screen_shoter": [], "screen_size": [], "score": [], "reason": [], "scorer_name": []},
+            {
+                "id": [],
+                "images": [],
+                "website": [],
+                "question": [],
+                "answer": [],
+                "criteria": [],
+                "subtask": [],
+                "data_generator": [],
+                "checker": [],
+                "date_time": [],
+                "screen_shoter": [],
+                "screen_size": [],
+                "score": [],
+                "reason": [],
+                "scorer_name": [],
+            },
             features=LiveBenchData.features,
         )
 
diff --git a/tools/live_bench/live_bench/data_generator/live_bench_data.py b/tools/live_bench/live_bench/data_generator/live_bench_data.py
index 65c83970..db04bddb 100644
--- a/tools/live_bench/live_bench/data_generator/live_bench_data.py
+++ b/tools/live_bench/live_bench/data_generator/live_bench_data.py
@@ -6,7 +6,7 @@
 
 class LiveBenchData(object):
     SUBTASKS = ("Basic Understanding", "Contextual Analysis", "Deeper Implications", "Broader Implications", "Further Insights")
-    
+
     features = datasets.Features(
         {
             "id": datasets.Value("int32"),
@@ -28,7 +28,20 @@ class LiveBenchData(object):
     )
 
     def __init__(
-        self, *, screen: ScreenImage, question: str, answer: str, criteria: str, subtask: str, data_generator: str, infomation: ImageInfomation = None, score: int = None, reason: str = None, checker: QAGenerator = None, scorer_name=None, scorer=None
+        self,
+        *,
+        screen: ScreenImage,
+        question: str,
+        answer: str,
+        criteria: str,
+        subtask: str,
+        data_generator: str,
+        infomation: ImageInfomation = None,
+        score: int = None,
+        reason: str = None,
+        checker: QAGenerator = None,
+        scorer_name=None,
+        scorer=None,
     ):
         self.screen = screen
         self.question = question

From 00657e625e8b18686b18a76a80be856f744f413a Mon Sep 17 00:00:00 2001
From: Kaichen Zhang - NTU <kaichenzhang358@outlook.com>
Date: Thu, 4 Jul 2024 18:26:56 +0800
Subject: [PATCH 12/32] Add wild vision from public (#131)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* fix doc

* [WIP] adding mmbench dev evaluation (#75)

* WIP

* Update GPT evaluation model name and sys prompt

* 🛠️ Scale accuracy to percentage

The accuracy value is now multiplied by 100 in the aggregation function to represent it as a percentage. Regarding the evaluation process, `math` module importation and refactoring reduce progress log verbosity by logging every 100 evaluations instead of 10. It prevents potential logging overflow. Handling of NaN values is added to ensure 'default_value' is set in case of missing data, avoiding errors in split, category, and l2-category assignments. Finally, reporting of categorical and l2-categorical accuracies is streamlined through a new `calculate_hit_rates` function, improving code readability and maintenance.

Issue refs: #1427, #1533

* Update GPT evaluation model name and API configuration

* Refactor MMBench_Evaluator class to handle missing columns

* Add print statements for detailed results in MMBench-CN(CC), MMBench-CN(Dev), and MMBench-EN(Dev) evaluations

* Refactor MMBench-CN and MMBench-EN evaluation functions

* 🔄 Refactor result processing and logging logic

- Simplified the result processing functions across different utility modules (`cc_utils.py`, `cn_utils.py`, `en_utils.py`) to unify the handling of multiple-choice options. Now, all options ("A" to "E") are dynamically added to the result data, and default to "nan" if not provided in the document.
- Removed redundant keys directly from the process results dict creation to avoid clutter and align with the new dynamic addition of options.
- In `mmbench_evals.py`, removed the unnecessary check for all splits being 'dev' and streamlined the evaluation loop by eliminating the progress bar (tqdm) for a cleaner log output.
- Commented-out code and verbose logging during evaluation, which may have interfered with performance, has been removed for a more efficient and less intrusive logging experience.

This cleanup reduces redundancy in the codebase and improves evaluation performance.

Refs #2045

---------

Co-authored-by: Bo Li <bo.li01@bytedance.com>
(cherry picked from commit a19278c2ea6ddcbca64d3cc7f4efec7fe5775121)

* Create README.md

* Add files via upload

* Add MathVerse

* Fix typo in qwen_vl that was causing "reference before assignment"

* convert contexts to list if necessary and remove unnecessary construction of `questions`

* refactor query construction for clarity

* Create ScreenSpot on clean branch

* Update README to reflect new tasks

* Add README file specific to ScreenSpot

* slight update

* Init webSRC

* Draft README for WebSRC

* Update main README with new task names

* Draft and validate websrc eval on dev split

* Add code to enable compilation of submission for WebSRC test split

* Bugfix: WebSRC should be token-level F1 NOT character-level

* Add qwen vl api

* Fix llava conv template for llama3

* Fix llava_hf generation for 1.6

* Parse result for llava_hf 1.6

* Add model_name parameter to Llava constructor

* Fix endless warning for llava_hf generation

* Fix llava_hf image tokens number issue

* Create LICENSE

* Update LICENSE

* Update LICENSE

* Better task list_with_num

* Fix idefics2 llava in the wild bugs

* Remove redundant code in fuyu

* Fix instructblip qformer size mismatch and multi-images problem

* Comment out parse result in xcomposer

* Comment out Spice in caption task so that don't need to download stanford nlp model

* Update gitignore

* Add separated pope tasks by category

* Fix pope random name in pope full

* Set printing info for llava_hf to debug level

* Adding Phi3v model.

* Adding prompt arguments for Phi3v on MathVista-TestMini

* Adding documentation of Phi3v class.

* [Fix] import issues of multilingual llava and olympiadbench

* fix compatibility issue of older version llava

* add upd

* add upd

* add upd

* add upd

* add upd

* add upd

* Group MMMU images into one image (#83)

* update

* update font

* Add matplotlib.font_manager import in utils.py

* Refactor font handling in add_order_label function in utils.py

* group mmmu

---------

Co-authored-by: Li Bo <drluodian@gmail.com>

* merge model_specific_prompt_kwargs and dataset_name into each task yaml

* Add MathVerse in README.md

* slightly change query_prompt for the reproduction

* update utils.py for leaderboard submission

* add conbench

* update README

* Update README.md

* init include vcr

* modify the form of VCR

* switch logic

* add crossed_text to vcr_wiki output

* include the try-except logic for spacy

* update vcr_wiki tasks

* update vcr_wiki tasks in README.md

* include std and confidence interval

* update gpt-3.5-turbo version

* update gpt-3.5-turbo version

* chore: Remove unnecessary files and code related to live_bench and sft_eval tasks

* Bump version to 0.2.0.dev0

* chore: Update lmms-eval to support video evaluations for LLaVA models

* Update llava conv_template in lmms_eval/models/llava.py

* Update image alignment in README.md

* chore: Update lmms-eval to support video evaluations for LLaVA models

* chore: Update lmms-eval to support video evaluations for LLaVA models

* Update README.md

* Update README.md

* update aggregation function for vcr_wiki

* update README.md

* Update README.md

* update version

* add II-Bench

* fix dataset_path

* Add qbench, qbench2, abench; fix phi3v as its current implementation does not support multi-image

* add tinyllava

* LongVideoBench support: image LMMs (idefics2, phi3) and video LMMs (LLaVA-Next-Video-34B)

* fix #117, allow auto download with tar format videos

* fix #117, allow auto download with tar format videos

* fix typo

* feat: Add support for auto downloading tar format videos

* Release llava-wilder

* chore: Update dependencies to fix potential risks and improve compatibility

* tutorial

* docs

* update preparation

* small fix

* small fix

* lint

* to sh script

* update readme

* Remove handling non-visual loop in llava

* Add llava_hf back to registry

* Update README.md

* Update README.md

* update ablation for videomme datasets

* chore: Handle ImportError when importing models

Handle the ImportError exception when importing models in the lmms_eval package. This change adds a try-except block to catch the ImportError and print an error message indicating the failed import. This will help with troubleshooting and identifying any issues with the model imports.

* chore: Remove unused models from lmms_eval package

* feat: Allow loading model configurations from other packages

* feat: Allow including external tasks from plugins

* chore: Add loguru for logging in lmms_eval package

* Remove unnecessary lines since use batched visuals now in llava

* Add longva

* Revise model registry for llava_hf and longva

* Delete unnecessary lines

* Remove unnecessary lines for video llava

* Update pyproject.toml

* Update activitynetqa_generation.yaml

* Fix vid mme post prompt issue

* Add wild vision 0617

* Hardcode to keep image for wild vision

* Fixing scoring logic

* Fixing dataset name

* Fixing handling None filtered score

---------

Co-authored-by: cocoshe <1228759711@qq.com>
Co-authored-by: Bo Li <bo.li01@bytedance.com>
Co-authored-by: Gagan Bhatia <49101362+gagan3012@users.noreply.github.com>
Co-authored-by: CaraJ7 <1350074492@qq.com>
Co-authored-by: Li Bo <drluodian@gmail.com>
Co-authored-by: Andrea Tupini <tupini07@gmail.com>
Co-authored-by: Hunter Heidenreich <hunter.heidenreich@rootsautomation.com>
Co-authored-by: Victor Fragoso <victor.fragoso@microsoft.com>
Co-authored-by: AtsuMiyai <miyai.atsuyuki.practice@gmail.com>
Co-authored-by: Pu Fanyi <FPU001@e.ntu.edu.sg>
Co-authored-by: Yuan Zhang <gump_well_done@163.com>
Co-authored-by: Yuan Zhang <56063339+Gumpest@users.noreply.github.com>
Co-authored-by: tianyu-z <zhangtianyupro@gmail.com>
Co-authored-by: Suyuchen <suyuchen.wang@umontreal.ca>
Co-authored-by: XinrunDu <duxinrun2000@gmail.com>
Co-authored-by: teowu <realtimothyhwu@gmail.com>
Co-authored-by: Jingyang <jingyang.zhang@duke.edu>
Co-authored-by: Teo (Timothy) Wu Haoning <38696372+teowu@users.noreply.github.com>
Co-authored-by: choiszt <ls2001927@sohu.com>
Co-authored-by: Lorenzo Mammana <mammanalorenzo@outlook.it>
---
 lmms_eval/__main__.py                         |   7 +
 lmms_eval/evaluator.py                        |   7 +-
 lmms_eval/models/__init__.py                  |  19 ++
 lmms_eval/models/longva.py                    |   2 +-
 .../activitynetqa_generation.yaml             |   1 -
 .../tasks/videomme/videomme_w_subtitle.yaml   |   5 +
 lmms_eval/tasks/websrc/utils.py               |   8 +
 .../wild_vision_bench/_default_template_yaml  |  23 +++
 lmms_eval/tasks/wild_vision_bench/utils.py    | 188 ++++++++++++++++++
 .../wild_vision_bench0617.yaml                |   9 +
 10 files changed, 266 insertions(+), 3 deletions(-)
 create mode 100644 lmms_eval/tasks/wild_vision_bench/_default_template_yaml
 create mode 100644 lmms_eval/tasks/wild_vision_bench/utils.py
 create mode 100644 lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml

diff --git a/lmms_eval/__main__.py b/lmms_eval/__main__.py
index 96be8f06..159ac5a6 100755
--- a/lmms_eval/__main__.py
+++ b/lmms_eval/__main__.py
@@ -1,3 +1,4 @@
+import importlib
 import os
 import yaml
 import sys
@@ -236,6 +237,12 @@ def cli_evaluate_single(args: Union[argparse.Namespace, None] = None) -> None:
         eval_logger.info(f"Including path: {args.include_path}")
         include_path(args.include_path)
 
+    if os.environ.get("LMMS_EVAL_PLUGINS", None):
+        for plugin in os.environ["LMMS_EVAL_PLUGINS"].split(","):
+            package_tasks_location = importlib.util.find_spec(f"{plugin}.tasks").submodule_search_locations[0]
+            eval_logger.info(f"Including path: {args.include_path}")
+            include_path(package_tasks_location)
+
     if args.tasks is None:
         task_names = ALL_TASKS
     elif args.tasks == "list":
diff --git a/lmms_eval/evaluator.py b/lmms_eval/evaluator.py
index b05f00d1..6788467e 100755
--- a/lmms_eval/evaluator.py
+++ b/lmms_eval/evaluator.py
@@ -325,7 +325,12 @@ def evaluate(
             # hack: remove image columns to speed avoid loading images and speed up postprocessing
             # reason: doc_iterator will actually load image if it's in the doc.
             docs = task.test_docs() if task.has_test_docs() else task.validation_docs()
-            if "d170" not in task_name and "dc100" not in task_name and "dc200" not in task_name and "llava_wilder" not in task_name and "live_bench" not in task_name:
+            if "d170" not in task_name \
+                and "dc100" not in task_name \
+                and "dc200" not in task_name \
+                and "llava_wilder" not in task_name \
+                and "livebench" not in task_name \
+                and "wildvision" not in task_name:
                 remove_cols = []
                 features = docs.features
                 # If it is an Image instance or a Sequence of Image instance. Remove it
diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py
index a7f2fb39..17100e95 100755
--- a/lmms_eval/models/__init__.py
+++ b/lmms_eval/models/__init__.py
@@ -1,3 +1,6 @@
+import importlib
+import os
+import hf_transfer
 from loguru import logger
 import sys
 
@@ -28,6 +31,8 @@
     "mplug_owl_video": "mplug_Owl",
     "phi3v": "Phi3v",
     "tinyllava": "TinyLlava",
+    "llava_hf": "LlavaHf",
+    "longva": "LongVA",
     "llava_onevision": "Llava_OneVision",
     "llava_hf": "LlavaHf",
     "longva": "LongVA",
@@ -39,3 +44,17 @@
     except ImportError as e:
         # logger.warning(f"Failed to import {model_class} from {model_name}: {e}")
         pass
+
+if os.environ.get("LMMS_EVAL_PLUGINS", None):
+    # Allow specifying other packages to import models from
+    for plugin in os.environ["LMMS_EVAL_PLUGINS"].split(","):
+        m = importlib.import_module(f"{plugin}.models")
+        for model_name, model_class in getattr(m, "AVAILABLE_MODELS").items():
+            try:
+                exec(f"from {plugin}.models.{model_name} import {model_class}")
+            except ImportError:
+                pass
+
+import hf_transfer
+
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
diff --git a/lmms_eval/models/longva.py b/lmms_eval/models/longva.py
index c5bf6861..c599d344 100644
--- a/lmms_eval/models/longva.py
+++ b/lmms_eval/models/longva.py
@@ -458,4 +458,4 @@ def _collate(x):
         res = re_ords.get_original(res)
 
         pbar.close()
-        return res
+        return res
\ No newline at end of file
diff --git a/lmms_eval/tasks/activitynetqa/activitynetqa_generation.yaml b/lmms_eval/tasks/activitynetqa/activitynetqa_generation.yaml
index fe0c18e4..fd7e069a 100755
--- a/lmms_eval/tasks/activitynetqa/activitynetqa_generation.yaml
+++ b/lmms_eval/tasks/activitynetqa/activitynetqa_generation.yaml
@@ -1,4 +1,3 @@
-dataset_name: "Generation"
 task: "activitynetqa"
 test_split: test
 output_type: generate_until
diff --git a/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml
index ac34cdf4..b7724748 100644
--- a/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml
+++ b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml
@@ -26,6 +26,11 @@ metric_list:
 model_specific_prompt_kwargs:
   default:
     frame_num: 32
+<<<<<<< HEAD
+    pre_prompt: ""
+    post_prompt: "\nAnswer the question using a single word or phrase."
+=======
+>>>>>>> internal_main_dev
   gemini_api:
     gemini_api_flag: "full subtitle"
   # gpt4v:
diff --git a/lmms_eval/tasks/websrc/utils.py b/lmms_eval/tasks/websrc/utils.py
index 6e2584a4..7286cd62 100644
--- a/lmms_eval/tasks/websrc/utils.py
+++ b/lmms_eval/tasks/websrc/utils.py
@@ -63,7 +63,15 @@ def websrc_test_aggregate_results_for_submission(results, args):
         for result in results:
             out.update(result)
         json.dump(out, f, indent=4)
+<<<<<<< HEAD
+<<<<<<< HEAD
     eval_logger.info(f"Results saved to {path}.")
+=======
+    lmms_logger.info(f"Results saved to {path}.")
+>>>>>>> internal_main_dev
+=======
+    eval_logger.info(f"Results saved to {path}.")
+>>>>>>> internal_main_dev
 
 
 def websrc_aggregate_results(results):
diff --git a/lmms_eval/tasks/wild_vision_bench/_default_template_yaml b/lmms_eval/tasks/wild_vision_bench/_default_template_yaml
new file mode 100644
index 00000000..7ce709dc
--- /dev/null
+++ b/lmms_eval/tasks/wild_vision_bench/_default_template_yaml
@@ -0,0 +1,23 @@
+dataset_path: WildVision/wildvision-arena-data
+dataset_kwargs:
+  token: True
+output_type: generate_until
+doc_to_visual: !function utils.wild_vision_doc_to_visual
+doc_to_text: !function utils.wild_vision_doc_to_text
+doc_to_target: !function utils.wild_vision_doc_to_target
+generation_kwargs:
+  max_new_tokens: 4096
+  temperature: 0
+  top_p: 1.0
+  num_beams: 1
+  do_sample: false
+# The return value of process_results will be used by metrics
+process_results: !function utils.wild_vision_process_results
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: gpt_eval_score
+    aggregation: !function utils.wild_vision_aggregation
+    higher_is_better: true
+metadata:
+  judge_model: gpt-4o
+  baseline_model: claude-3-sonnet-20240229
diff --git a/lmms_eval/tasks/wild_vision_bench/utils.py b/lmms_eval/tasks/wild_vision_bench/utils.py
new file mode 100644
index 00000000..bb426557
--- /dev/null
+++ b/lmms_eval/tasks/wild_vision_bench/utils.py
@@ -0,0 +1,188 @@
+import json
+import re
+import os
+import requests
+import numpy as np
+import time
+import yaml
+from pathlib import Path
+from copy import deepcopy
+from io import BytesIO
+import base64
+
+from loguru import logger as eval_logger
+
+NUM_SECONDS_TO_SLEEP = 5
+
+
+with open(Path(__file__).parent / "_default_template_yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+
+    config = yaml.safe_load("".join(safe_data))
+
+GPT_EVAL_MODEL_NAME = config["metadata"]["judge_model"]
+BASELINE_MODEL_NAME = config["metadata"]["baseline_model"]
+
+API_TYPE = os.getenv("API_TYPE", "openai")
+
+if API_TYPE == "openai":
+    API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
+    API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
+    headers = {
+        "Authorization": f"Bearer {API_KEY}",
+        "Content-Type": "application/json",
+    }
+elif API_TYPE == "azure":
+    API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
+    API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
+    headers = {
+        "api-key": API_KEY,
+        "Content-Type": "application/json",
+    }
+
+system_prompt = """\
+Please act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user prompt displayed below. You will be given assistant A's answer and assistant B's answer. Your job is to evaluate which assistant's answer is better.
+
+Begin your evaluation by generating your own answer to the prompt. You must provide your answers before judging any answers.
+
+When evaluating the assistants' answers, compare both assistants' answers with your answer. You must identify and correct any mistakes or inaccurate information.
+
+Then consider if the assistant's answers are helpful, relevant, and concise. Helpful means the answer correctly responds to the prompt or follows the instructions. Note when user prompt has any ambiguity or more than one interpretation, it is more helpful and appropriate to ask for clarifications or more information from the user than providing an answer based on assumptions. Relevant means all parts of the response closely connect or are appropriate to what is being asked. Concise means the response is clear and not verbose or excessive.
+
+Then consider the creativity and novelty of the assistant's answers when needed. Finally, identify any missing important information in the assistants' answers that would be beneficial to include when responding to the user prompt.
+
+After providing your explanation, you must output only one of the following choices as your final verdict with a label:
+
+1. Assistant A is significantly better: [[A>>B]]
+2. Assistant A is slightly better: [[A>B]]
+3. Tie, relatively the same: [[A=B]]
+4. Assistant B is slightly better: [[B>A]]
+5. Assistant B is significantly better: [[B>>A]]
+
+Example output: "My final verdict is tie: [[A=B]]".\
+"""
+
+prompt_template = "<|User Prompt|>\n{question_1}\n\n<|The Start of Assistant A's Answer|>\n{answer_1}\n<|The End of Assistant A's Answer|>\n\n<|The Start of Assistant B's Answer|>\n{answer_2}\n<|The End of Assistant B's Answer|>"
+
+def get_chat_response(base64_image, prompt, max_retries=5, wait_time=10):
+    headers = {
+        "Authorization": f"Bearer {API_KEY}",
+        "Content-Type": "application/json",
+    }
+
+    payload = {
+        "model": GPT_EVAL_MODEL_NAME,
+        "messages": [
+            {"role": "system", "content": [{"type": "text", "text": system_prompt}]},
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt},
+                    {"type": "image_url",
+                        "image_url" : {
+                            "url" : f"data:image/jpeg;base64, {base64_image}"
+                            }
+                    },
+                ],
+            }
+        ],
+        "max_tokens": 1024,
+        "temperature": 0.0,
+    }
+
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
+            response.raise_for_status()
+            response_data = response.json()
+            return response_data["choices"][0]["message"]["content"], GPT_EVAL_MODEL_NAME
+        except requests.exceptions.RequestException as e:
+            print(f"Request failed on attempt {attempt+1}: {e}")
+            if attempt == max_retries - 1:
+                print(f"Failed to get response after {max_retries} attempts")
+                return "", GPT_EVAL_MODEL_NAME
+        except Exception as e:
+            print(f"Error on attempt {attempt+1}: {e}")
+            return "", GPT_EVAL_MODEL_NAME
+
+
+
+def image_to_base64(pil_image):
+    buffered = BytesIO()
+    pil_image.save(buffered, format="PNG")
+    return base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+def get_score(judgement, pattern, pairwise=True):
+    matches = pattern.findall(judgement)
+    matches = [m for m in matches if m != ""]
+    if len(set(matches)) == 0:
+        return None, True
+    elif len(set(matches)) == 1:
+        if pairwise:
+            return matches[0].strip("\n"), False
+        return int(matches[0])
+    else:
+        return None, False
+
+def wild_vision_doc_to_visual(doc):
+    return [doc["image"].convert('RGB')]
+
+
+def wild_vision_doc_to_text(doc, model_specific_prompt_kwargs=None):
+    question = doc["instruction"].strip()
+    if "pre_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["pre_prompt"] != "":
+        question = f"{model_specific_prompt_kwargs['pre_prompt']}{question}"
+    if "post_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["post_prompt"] != "":
+        question = f"{question}{model_specific_prompt_kwargs['post_prompt']}"
+    return question
+
+def wild_vision_doc_to_target(doc):
+    return doc[BASELINE_MODEL_NAME]
+
+
+def wild_vision_process_results(doc, results):
+    pred = results[0]
+    user_prompt = prompt_template.format(question_1=doc["instruction"], answer_1=doc[BASELINE_MODEL_NAME], answer_2=pred)
+    base64_image = image_to_base64(doc["image"])
+    resps, gpt_name = get_chat_response(base64_image, user_prompt)
+    score, _ = get_score(resps, pattern=re.compile("\[\[([AB<>=]+)\]\]"))
+    
+    if score is None:
+        score = resps
+    
+    if "A>B" in score:
+        final_score = -1
+        judgement = "Worse" #Baseline better
+    elif "A>>B" in score:
+        final_score = -2
+        judgement = "Worse++"
+    elif "A=B" in score:
+        final_score = 0 
+        judgement = "Tie"
+    elif "B>A" in score:
+        final_score = 1
+        judgement = "Better"
+    elif "B>>A" in score:
+        final_score = 2
+        judgement = "Better++"
+    else:
+        final_score = 0
+        judgement = "Unclear"
+
+
+    return {"gpt_eval_score" : {"question" : doc["instruction"], "score" : final_score, "gpt_resps" : resps, "ans_1" : doc[BASELINE_MODEL_NAME], "ans_2" : pred, "filtered_resps" : score, "judgement" : judgement}}
+
+
+def wild_vision_aggregation(results):
+    score = 0
+    for res in results:
+        score += res["score"]
+    
+    return score / len(results)
+
+
diff --git a/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml b/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml
new file mode 100644
index 00000000..93576768
--- /dev/null
+++ b/lmms_eval/tasks/wild_vision_bench/wild_vision_bench0617.yaml
@@ -0,0 +1,9 @@
+task: wildvision_0617
+dataset_name: release_bench_0617_with_modelresponse 
+test_split: test500
+output_type: generate_until
+include: _default_template_yaml
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: ""

From eb261f6e9e1ec630f9d07287102dbd80f1ed2173 Mon Sep 17 00:00:00 2001
From: Li Bo <drluodian@gmail.com>
Date: Fri, 5 Jul 2024 02:34:27 +0800
Subject: [PATCH 13/32] Dev/interleave bench (#132)

* feat: Update LMMS evaluation configuration and models

- Update `activitynetqa_generation.yaml` to remove `dataset_name` field and update `task` field to "activitynetqa"
- Update `utils.py` to add default values for `API_URL` and `API_KEY` when `API_TYPE` is not "openai" or "azure"
- Update `batch_gpt4.py` and `gpt4v.py` to rename `max_frames_for_video` parameter to `max_frames_num`
- Update `reka.py` to rename `max_frames_for_video` parameter to `max_frames_num` and add support for `continual_mode` with a persistent response cache

This commit updates the LMMS evaluation configuration and models to improve compatibility and add new features.

* Update LMMS evaluation configuration and models

* Update LMMS evaluation configuration and models

* feat: Update LMMS evaluation configuration and models

- Update `activitynetqa_generation.yaml` to remove `dataset_name` field and update `task` field to "activitynetqa"
- Update `utils.py` to add default values for `API_URL` and `API_KEY` when `API_TYPE` is not "openai" or "azure"
- Update `batch_gpt4.py` and `gpt4v.py` to rename `max_frames_for_video` parameter to `max_frames_num`
- Update `reka.py` to rename `max_frames_for_video` parameter to `max_frames_num` and add support for `continual_mode` with a persistent response cache

This commit updates the LMMS evaluation configuration and models to improve compatibility and add new features.

* Refactor error handling in GPT4V model evaluation

* Refactor error handling in GPT4V model evaluation

* Refactor video decoding backend to use "decord" instead of "pyav"

* Refactor image aspect ratio handling in Llava_OneVision model

* Refactor GPT4V model to fix bug in visuals encoding

* add exception for azure gpt
---
 lmms_eval/__main__.py                         |   2 +-
 lmms_eval/models/batch_gpt4.py                |   6 +-
 lmms_eval/models/claude.py                    |  44 +-
 lmms_eval/models/gpt4v.py                     |  73 ++-
 lmms_eval/models/llava_onevision.py           | 565 ++++++++++++++++++
 lmms_eval/models/reka.py                      |  35 +-
 lmms_eval/models/xcomposer2_4KHD.py           | 295 ---------
 lmms_eval/tasks/__init__.py                   |   2 +-
 .../_default_template_interleave_yaml         |   9 +
 .../llava_interleave_bench/in_domain.yaml     |  29 +
 .../multi_view_in_domain.yaml                 |  29 +
 .../llava_interleave_bench/out_of_domain.yaml |  29 +
 .../tasks/llava_interleave_bench/utils.py     | 331 ++++++++++
 .../tasks/mix_evals/_default_template_yaml    |  16 +
 .../tasks/mix_evals/mix_evals_video2text.yaml |   5 +
 .../mix_evals_video2text_freeform.yaml        |  22 +
 .../mix_evals/mix_evals_video2text_mc.yaml    |  31 +
 .../mix_evals_video2text_openended.yaml       |  22 +
 .../mix_evals_video2text_openended_2nd.yaml   |  23 +
 lmms_eval/tasks/mix_evals/utils.py            | 284 +++++++++
 lmms_eval/tasks/mmupd/utils.py                |   3 +
 pyproject.toml                                |   1 +
 22 files changed, 1503 insertions(+), 353 deletions(-)
 create mode 100644 lmms_eval/models/llava_onevision.py
 delete mode 100644 lmms_eval/models/xcomposer2_4KHD.py
 create mode 100644 lmms_eval/tasks/llava_interleave_bench/_default_template_interleave_yaml
 create mode 100644 lmms_eval/tasks/llava_interleave_bench/in_domain.yaml
 create mode 100644 lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml
 create mode 100644 lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml
 create mode 100644 lmms_eval/tasks/llava_interleave_bench/utils.py
 create mode 100644 lmms_eval/tasks/mix_evals/_default_template_yaml
 create mode 100644 lmms_eval/tasks/mix_evals/mix_evals_video2text.yaml
 create mode 100644 lmms_eval/tasks/mix_evals/mix_evals_video2text_freeform.yaml
 create mode 100644 lmms_eval/tasks/mix_evals/mix_evals_video2text_mc.yaml
 create mode 100644 lmms_eval/tasks/mix_evals/mix_evals_video2text_openended.yaml
 create mode 100644 lmms_eval/tasks/mix_evals/mix_evals_video2text_openended_2nd.yaml
 create mode 100644 lmms_eval/tasks/mix_evals/utils.py

diff --git a/lmms_eval/__main__.py b/lmms_eval/__main__.py
index 159ac5a6..996e1e53 100755
--- a/lmms_eval/__main__.py
+++ b/lmms_eval/__main__.py
@@ -165,7 +165,7 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
     # reset logger
     eval_logger.remove()
     eval_logger.add(sys.stdout, colorize=True, level=args.verbosity)
-    eval_logger.add(sys.stderr, level=args.verbosity)
+    # eval_logger.add(sys.stderr, level=args.verbosity)
     eval_logger.info(f"Verbosity set to {args.verbosity}")
     os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
diff --git a/lmms_eval/models/batch_gpt4.py b/lmms_eval/models/batch_gpt4.py
index 8f4c2220..7541b709 100755
--- a/lmms_eval/models/batch_gpt4.py
+++ b/lmms_eval/models/batch_gpt4.py
@@ -59,7 +59,7 @@ def __init__(
         api_key: str = API_KEY,
         api_url: str = API_URL,
         modality: str = "image",
-        max_frames_for_video: int = 10,
+        max_frames_num: int = 10,
         timeout: int = 120,
         **kwargs,
     ) -> None:
@@ -69,7 +69,7 @@ def __init__(
         # Here we just use the same token as llava for convenient
         self.model_version = model_version
         self.modality = modality
-        self.max_frames_for_video = max_frames_for_video
+        self.max_frames_num = max_frames_num
         self.image_token = "<image>"
         self.timeout = timeout
 
@@ -128,7 +128,7 @@ def generate_until(self, requests):
                     img = self.encode_image(visual)
                     imgs.append(img)
                 elif self.modality == "video":
-                    frames = self.encode_video(visual, self.max_frames_for_video)
+                    frames = self.encode_video(visual, self.max_frames_num)
                     imgs.extend(frames)
 
             messages = []
diff --git a/lmms_eval/models/claude.py b/lmms_eval/models/claude.py
index ff066d35..e843871c 100644
--- a/lmms_eval/models/claude.py
+++ b/lmms_eval/models/claude.py
@@ -40,6 +40,7 @@ def __init__(
         image_token: str = "<image>",  # Use to separate interleaved image and text
         system_prompt: str = "",  # Whether you want some special system prompt here
         modality: str = "image",
+        max_frames_num: int = 10,
         continual_mode: bool = False,
         response_persistent_folder: str = None,
         **kwargs,
@@ -49,20 +50,24 @@ def __init__(
         self.image_token = image_token
         self.system_prompt = system_prompt
         self.modality = modality
+        self.max_frames_num = max_frames_num
 
         self.continual_mode = continual_mode
-        if self.continual_mode and response_persistent_folder is None:
-            raise ValueError("Continual mode requires a persistent path for the response. Please provide a valid path.")
-        self.response_persistent_folder = response_persistent_folder
-        self.response_persistent_file = os.path.join(self.response_persistent_folder, f"{self.model_version}_response.json")
-
-        if os.path.exists(self.response_persistent_file):
-            with open(self.response_persistent_file, "r") as f:
-                self.response_cache = json.load(f)
-            self.cache_mode = "resume"
-        else:
-            self.response_cache = {}
-            self.cache_mode = "start"
+        if self.continual_mode:
+            if response_persistent_folder is None:
+                raise ValueError("Continual mode requires a persistent path for the response. Please provide a valid path.")
+
+            os.makedirs(response_persistent_folder, exist_ok=True)
+            self.response_persistent_folder = response_persistent_folder
+            self.response_persistent_file = os.path.join(self.response_persistent_folder, f"{self.model_version}_response.json")
+
+            if os.path.exists(self.response_persistent_file):
+                with open(self.response_persistent_file, "r") as f:
+                    self.response_cache = json.load(f)
+                self.cache_mode = "resume"
+            else:
+                self.response_cache = {}
+                self.cache_mode = "start"
 
         accelerator = Accelerator()
         if accelerator.num_processes > 1:
@@ -81,7 +86,7 @@ def __init__(
 
     def encode_image(self, image):
         output_buffer = BytesIO()
-        image.save(output_buffer, format="PNG")
+        image.save(output_buffer, format="JPEG")
         byte_data = output_buffer.getvalue()
         base64_str = base64.b64encode(byte_data).decode("utf-8")
         return base64_str
@@ -129,7 +134,7 @@ def shrink_image_to_file_size(self, img: Image, max_file_size=4838990) -> Image:
     def encode_video(self, video_path):
         vr = VideoReader(video_path, ctx=cpu(0))
         total_frame_num = len(vr)
-        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, self.max_frames_for_video, dtype=int)
+        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, self.max_frames_num, dtype=int)
         frame_idx = uniform_sampled_frames.tolist()
         frames = vr.get_batch(frame_idx).asnumpy()
 
@@ -137,10 +142,10 @@ def encode_video(self, video_path):
         for frame in frames:
             img = Image.fromarray(frame)
             output_buffer = BytesIO()
-            img.save(output_buffer, format="PNG")
+            img.save(output_buffer, format="JPEG")
             byte_data = output_buffer.getvalue()
             base64_str = base64.b64encode(byte_data).decode("utf-8")
-            base64_frames.append(f"data:image/jpeg;base64,{base64_str}")
+            base64_frames.append(f"{base64_str}")
 
         return base64_frames
 
@@ -154,7 +159,7 @@ def generate_until(self, requests) -> List[str]:
             "type": "image",
             "source": {
                 "type": "base64",
-                "media_type": "image/png",
+                "media_type": "image/jpeg",
             },
         }
         empty_text_block = {"type": "text"}
@@ -220,8 +225,8 @@ def generate_until(self, requests) -> List[str]:
                 gen_kwargs["max_new_tokens"] = 1024
             if "temperature" not in gen_kwargs:
                 gen_kwargs["temperature"] = 0
-            if "top_p" not in gen_kwargs:
-                gen_kwargs["top_p"] = None
+            if "top_p" not in gen_kwargs or gen_kwargs["top_p"] is None:
+                gen_kwargs["top_p"] = 1
             if "num_beams" not in gen_kwargs:
                 gen_kwargs["num_beams"] = 1
 
@@ -244,6 +249,7 @@ def generate_until(self, requests) -> List[str]:
 
             ###################### CONTINUAL MODE ######################
             if self.continual_mode is True:  # Cache the response
+                response_text = message.content[0].text
                 doc_uuid = f"{task}___{split}___{doc_id}"
                 self.response_cache[doc_uuid] = response_text
                 with open(self.response_persistent_file, "w") as f:
diff --git a/lmms_eval/models/gpt4v.py b/lmms_eval/models/gpt4v.py
index 1a47aa76..5f89cc5d 100755
--- a/lmms_eval/models/gpt4v.py
+++ b/lmms_eval/models/gpt4v.py
@@ -7,15 +7,13 @@
 from tqdm import tqdm
 import requests as url_requests
 import time
-
+import json
 
 from lmms_eval.api.instance import Instance
 from lmms_eval.api.model import lmms
 from lmms_eval.api.registry import register_model
-from lmms_eval import utils
 
-from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
-from accelerate.state import AcceleratorState
+from accelerate import Accelerator, DistributedType
 
 try:
     from decord import VideoReader, cpu
@@ -50,8 +48,10 @@ def __init__(
         self,
         model_version: str = "gpt-4-vision-preview",
         modality: str = "video",
-        max_frames_for_video: int = 10,
+        max_frames_num: int = 10,
         timeout: int = 120,
+        continual_mode: bool = False,
+        response_persistent_folder: str = None,
         **kwargs,
     ) -> None:
         super().__init__()
@@ -60,9 +60,25 @@ def __init__(
         # Here we just use the same token as llava for convenient
         self.model_version = model_version
         self.modality = modality
-        self.max_frames_for_video = max_frames_for_video
+        self.max_frames_num = max_frames_num
         self.image_token = "<image>"
         self.timeout = timeout
+        self.continual_mode = continual_mode
+        if self.continual_mode:
+            if response_persistent_folder is None:
+                raise ValueError("Continual mode requires a persistent path for the response. Please provide a valid path.")
+
+            os.makedirs(response_persistent_folder, exist_ok=True)
+            self.response_persistent_folder = response_persistent_folder
+            self.response_persistent_file = os.path.join(self.response_persistent_folder, f"{self.model_version}_response.json")
+
+            if os.path.exists(self.response_persistent_file):
+                with open(self.response_persistent_file, "r") as f:
+                    self.response_cache = json.load(f)
+                self.cache_mode = "resume"
+            else:
+                self.response_cache = {}
+                self.cache_mode = "start"
 
         accelerator = Accelerator()
         # assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue."
@@ -119,9 +135,16 @@ def generate_until(self, requests) -> List[str]:
         pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
 
         for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
-            # encode, pad, and truncate contexts for this batch
+            if self.continual_mode is True and self.cache_mode == "resume":
+                doc_uuid = f"{task}___{split}___{doc_id}"
+                if doc_uuid in self.response_cache:
+                    response_text = self.response_cache[doc_uuid]
+                    if response_text:
+                        res.append(response_text)
+                        pbar.update(1)
+                        continue
+
             visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
-            # visuals = [doc_to_visual(self.task_dict[task][split][0])]
             visuals = self.flatten(visuals)
             imgs = []  # multiple images or frames for video
             for visual in visuals:
@@ -129,10 +152,13 @@ def generate_until(self, requests) -> List[str]:
                     img = self.encode_image(visual)
                     imgs.append(img)
                 elif self.modality == "video":
-                    frames = self.encode_video(visual, self.max_frames_for_video)
+                    frames = self.encode_video(visual, self.max_frames_num)
                     imgs.extend(frames)
 
-            payload = {"model": self.model_version, "messages": []}
+            payload = {"messages": []}
+            if API_TYPE == "openai":
+                payload["model"] = self.model_version
+
             response_json = {"role": "user", "content": []}
             # When there is no image token in the context, append the image to the text
             if self.image_token not in contexts:
@@ -170,19 +196,30 @@ def generate_until(self, requests) -> List[str]:
                     response = url_requests.post(API_URL, headers=headers, json=payload, timeout=self.timeout)
                     response_data = response.json()
 
-                    content = response_data["choices"][0]["message"]["content"].strip()
+                    response_text = response_data["choices"][0]["message"]["content"].strip()
                     break  # If successful, break out of the loop
 
                 except Exception as e:
-                    eval_logger.info(f"Attempt {attempt + 1} failed with error: {str(e)}")
-                    if attempt < 5 - 1:  # If we have retries left, sleep and then continue to next attempt
+                    try:
+                        error_msg = response.json()
+                    except:
+                        error_msg = ""
+
+                    eval_logger.info(f"Attempt {attempt + 1} failed with error: {str(e)}.\nReponse: {error_msg}")
+                    if attempt <= 5:
                         time.sleep(NUM_SECONDS_TO_SLEEP)
-                    else:  # If this was the last attempt, log and return empty
-                        eval_logger.error(f"All 5 attempts failed. Last error message: {str(e)}")
-                        eval_logger.error(f"Response: {response}")
-                        content = ""
-            res.append(content)
+                    else:  # If this was the last attempt, log and return empty string
+                        eval_logger.error(f"All 5 attempts failed. Last error message: {str(e)}.\nResponse: {response.json()}")
+                        response_text = ""
+            res.append(response_text)
             pbar.update(1)
+
+            if self.continual_mode is True:  # Cache the response
+                doc_uuid = f"{task}___{split}___{doc_id}"
+                self.response_cache[doc_uuid] = response_text
+                with open(self.response_persistent_file, "w") as f:
+                    json.dump(self.response_cache, f)
+
         pbar.close()
         return res
 
diff --git a/lmms_eval/models/llava_onevision.py b/lmms_eval/models/llava_onevision.py
new file mode 100644
index 00000000..5cfbb538
--- /dev/null
+++ b/lmms_eval/models/llava_onevision.py
@@ -0,0 +1,565 @@
+from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
+from accelerate.state import AcceleratorState
+from transformers import AutoConfig
+
+import math
+import torch
+import transformers
+import re
+
+torch.backends.cuda.matmul.allow_tf32 = True
+
+from tqdm import tqdm
+from datetime import timedelta
+from decord import VideoReader, cpu
+import numpy as np
+
+import copy
+import PIL
+from typing import List, Optional, Union, Tuple
+from packaging import version
+import warnings
+import logging
+
+warnings.filterwarnings("ignore")
+
+eval_logger = logging.getLogger("lmms-eval")
+
+from lmms_eval import utils
+from lmms_eval.api.instance import Instance
+from lmms_eval.api.model import lmms
+from lmms_eval.api.registry import register_model
+from lmms_eval.models.model_utils.load_video import read_video_pyav
+
+try:
+    from llava.model.builder import load_pretrained_model
+    from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token, KeywordsStoppingCriteria
+    from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+    from llava.conversation import conv_templates, SeparatorStyle
+
+except Exception as e:
+    eval_logger.debug("LLaVA is not installed. Please install LLaVA to use this model.\nError: %s" % e)
+
+try:
+    from llavavid.model.language_model.llava_qwen import LlavaQwenConfig
+    from llavavid.model.language_model.llava_llama import LlavaConfig
+
+    AutoConfig.register("llava_qwen", LlavaQwenConfig)
+    AutoConfig.register("llava_llama", LlavaConfig)
+except Exception as e:
+    eval_logger.debug("")
+# inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5
+# if is_flash_attn_2_available:
+#     best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating
+
+if version.parse(torch.__version__) >= version.parse("2.1.2"):
+    best_fit_attn_implementation = "sdpa"
+else:
+    best_fit_attn_implementation = "eager"
+
+
+@register_model("llava_onevision")
+class Llava_OneVision(lmms):
+    """
+    Llava Model
+    """
+
+    def __init__(
+        self,
+        pretrained: str = "liuhaotian/llava-v1.5-7b",
+        truncation: Optional[bool] = True,
+        device: Optional[str] = "cuda:0",
+        batch_size: Optional[Union[int, str]] = 1,
+        model_name: Optional[str] = None,
+        attn_implementation: Optional[str] = best_fit_attn_implementation,
+        device_map: Optional[str] = "cuda:0",
+        conv_template: Optional[str] = "vicuna_v1",
+        use_cache: Optional[bool] = True,
+        truncate_context: Optional[bool] = False,  # whether to truncate the context in generation, set it False for LLaVA-1.6
+        customized_config: Optional[str] = None,  # ends in json
+        max_frames_num: Optional[int] = 32,
+        mm_spatial_pool_stride: Optional[int] = 2,
+        mm_spatial_pool_mode: Optional[str] = "average",
+        token_strategy: Optional[str] = "single",  # could be "single" or "multiple", "multiple" denotes adding multiple <image> tokens for each frame
+        video_decode_backend: str = "decord",
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        # Do not use kwargs for now
+        assert kwargs == {}, f"Unexpected kwargs: {kwargs}"
+
+        accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52))
+        accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs])
+        if accelerator.num_processes > 1:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            self._device = torch.device(device)
+            self.device_map = device_map
+        else:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+
+        llava_model_args = {
+            "multimodal": True,
+        }
+        if customized_config is not None:
+            llava_model_args["customized_config"] = customized_config
+        if attn_implementation is not None:
+            llava_model_args["attn_implementation"] = attn_implementation
+        if "use_flash_attention_2" in kwargs:
+            llava_model_args["use_flash_attention_2"] = kwargs["use_flash_attention_2"]
+        model_name = model_name if model_name is not None else get_model_name_from_path(pretrained)
+
+        self.pretrained = pretrained
+        self.token_strategy = token_strategy
+        self.max_frames_num = max_frames_num
+        self.mm_spatial_pool_stride = mm_spatial_pool_stride
+        self.mm_spatial_pool_mode = mm_spatial_pool_mode
+        self.video_decode_backend = video_decode_backend
+
+        overwrite_config = {}
+        overwrite_config["mm_spatial_pool_stride"] = self.mm_spatial_pool_stride
+        overwrite_config["mm_spatial_pool_mode"] = self.mm_spatial_pool_mode
+        cfg_pretrained = AutoConfig.from_pretrained(self.pretrained)
+
+        if cfg_pretrained.architectures[0] == "LlavaLlamaForCausalLM":  # Ugly code, only used in  vicuna that needs ROPE
+            if "224" in cfg_pretrained.mm_vision_tower:
+                least_token_number = self.max_frames_num * (16 // self.mm_spatial_pool_stride) ** 2 + 1000
+            else:
+                least_token_number = self.max_frames_num * (24 // self.mm_spatial_pool_stride) ** 2 + 1000
+
+            scaling_factor = math.ceil(least_token_number / 4096)
+            if scaling_factor >= 2:
+                overwrite_config["rope_scaling"] = {"factor": float(scaling_factor), "type": "linear"}
+                overwrite_config["max_sequence_length"] = 4096 * scaling_factor
+                overwrite_config["tokenizer_model_max_length"] = 4096 * scaling_factor
+
+        llava_model_args["overwrite_config"] = overwrite_config
+        try:
+            # Try to load the model with the multimodal argument
+            self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args)
+        except TypeError:
+            # for older versions of LLaVA that don't have multimodal argument
+            llava_model_args.pop("multimodal", None)
+            self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args)
+
+        self._config = self._model.config
+        self.model.eval()
+        self.model.tie_weights()
+        self.truncation = truncation
+        self.batch_size_per_gpu = int(batch_size)
+        self.conv_template = conv_template
+        self.use_cache = use_cache
+        self.truncate_context = truncate_context
+        assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue."
+
+        if accelerator.num_processes > 1:
+            assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
+            # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
+            # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works
+            # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work.
+            if accelerator.distributed_type == DistributedType.DEEPSPEED:
+                kwargs = {
+                    "train_micro_batch_size_per_gpu": self.batch_size_per_gpu,
+                    "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes,
+                }
+                AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs)
+                eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0")
+
+            if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED:
+                self._model = accelerator.prepare(self.model)
+            else:
+                self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
+            self.accelerator = accelerator
+            if self.accelerator.is_local_main_process:
+                eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
+            self._rank = self.accelerator.local_process_index
+            self._world_size = self.accelerator.num_processes
+
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism")
+            self._rank = 0
+            self._word_size = 1
+
+        else:
+            eval_logger.info(f"Using single device: {self._device}")
+            self.model.to(self._device)
+            self._rank = 0
+            self._world_size = 1
+
+    @property
+    def config(self):
+        # return the associated transformers.AutoConfig for the given pretrained model.
+        return self._config
+
+    @property
+    def tokenizer(self):
+        return self._tokenizer
+
+    @property
+    def model(self):
+        # returns the model, unwrapping it if using Accelerate
+        if hasattr(self, "accelerator"):
+            return self.accelerator.unwrap_model(self._model)
+        else:
+            return self._model
+
+    @property
+    def eot_token_id(self):
+        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+        return self.tokenizer.eos_token_id
+
+    @property
+    def max_length(self):
+        return self._max_length
+
+    def pad_sequence(self, input_ids, batch_first, padding_value):
+        if self.tokenizer.padding_side == "left":
+            input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids]
+        input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value)
+        if self.tokenizer.padding_side == "left":
+            input_ids = torch.flip(input_ids, [1])
+        return input_ids
+
+    @property
+    def batch_size(self):
+        return self.batch_size_per_gpu
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def rank(self):
+        return self._rank
+
+    @property
+    def world_size(self):
+        return self._world_size
+
+    def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]:
+        """ """
+        add_special_tokens = False if add_special_tokens is None else add_special_tokens
+        encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
+        # left-truncate the encoded context to be at most `left_truncate_len` tokens long
+        if left_truncate_len:
+            encoding = encoding[-left_truncate_len:]
+        return encoding
+
+    def tok_decode(self, tokens):
+        try:
+            return self.tokenizer.decode(tokens)
+        except:
+            return self.tokenizer.decode([tokens])
+
+    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        # TODO
+        res = []
+        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
+
+        for contexts, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
+            # encode, pad, and truncate contexts for this batch
+            if type(doc_to_target) == str:
+                continuation = doc_to_target
+            else:
+                continuation = doc_to_target(self.task_dict[task][split][doc_id])
+            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
+            visuals = self.flatten(visuals)
+            image_sizes = [[visual.size[0], visual.size[1]] for visual in visuals]
+            if visuals:
+                image = process_images(visuals, self._image_processor, self._config)
+                if type(image) is list:
+                    image = [_image.to(dtype=torch.float16, device=self.device) for _image in image]
+                else:
+                    image = image.to(dtype=torch.float16, device=self.device)
+            else:
+                image = None
+
+            prompts_input = contexts[0] if isinstance(contexts, list) else contexts
+
+            if image is not None and len(image) != 0 and DEFAULT_IMAGE_TOKEN not in prompts_input:
+                """
+                Three senarios:
+                1. No image, and there for, no image token should be added.
+                2. image token is already specified in the context, so we don't need to add it.
+                3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line.
+                """
+                image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals)
+                image_tokens = " ".join(image_tokens)
+                prompts_input = image_tokens + "\n" + (contexts[0] if isinstance(contexts, list) else contexts)
+
+            # This is much safer for llama3, as we now have some object type in it
+            if "llama_3" in self.conv_template:
+                conv = copy.deepcopy(conv_templates[self.conv_template])
+            else:
+                conv = conv_templates[self.conv_template].copy()
+
+            conv.append_message(conv.roles[0], prompts_input)
+            conv.append_message(conv.roles[1], None)
+            prompt = conv.get_prompt()
+            pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
+            contxt_id = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device)
+            # Add the answer of the second role
+            conv.messages[1][1] = continuation
+
+            prompt = conv.get_prompt()
+            input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device)
+            labels = input_ids.clone()
+            # Context part no need to calculate for loss
+            labels[0, : contxt_id.shape[1]] = -100
+            with torch.inference_mode():
+                outputs = self.model(input_ids=input_ids, labels=labels, images=image, use_cache=True, image_sizes=image_sizes)
+            loss = outputs["loss"]
+            # loss = torch.exp(loss)
+            logits = outputs["logits"]
+            greedy_tokens = logits.argmax(dim=-1)
+            cont_toks = input_ids[:, contxt_id.shape[1] :]  # [1, seq]
+            greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : input_ids.shape[1]]  # [1, seq]
+            max_equal = (greedy_tokens == cont_toks).all()
+            res.append((float(loss.item()), bool(max_equal)))
+            pbar.update(1)
+
+        pbar.close()
+        return res
+
+    def flatten(self, input):
+        new_list = []
+        for i in input:
+            for j in i:
+                new_list.append(j)
+        return new_list
+
+    def load_video(self, video_path, max_frames_num):
+        if type(video_path) == str:
+            vr = VideoReader(video_path, ctx=cpu(0))
+        else:
+            vr = VideoReader(video_path[0], ctx=cpu(0))
+        total_frame_num = len(vr)
+        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int)
+        frame_idx = uniform_sampled_frames.tolist()
+        spare_frames = vr.get_batch(frame_idx).asnumpy()
+        return spare_frames  # (frames, height, width, channels)
+
+    def preprocess_qwen(self, sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant."):
+        roles = {"human": "<|im_start|>user", "gpt": "<|im_start|>assistant"}
+
+        im_start, im_end = tokenizer.additional_special_tokens_ids
+        nl_tokens = tokenizer("\n").input_ids
+        _system = tokenizer("system").input_ids + nl_tokens
+        _user = tokenizer("user").input_ids + nl_tokens
+        _assistant = tokenizer("assistant").input_ids + nl_tokens
+
+        # Apply prompt templates
+        input_ids, targets = [], []
+
+        source = sources
+        if roles[source[0]["from"]] != roles["human"]:
+            source = source[1:]
+
+        input_id, target = [], []
+        system = [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens
+        input_id += system
+        target += [im_start] + [IGNORE_INDEX] * (len(system) - 3) + [im_end] + nl_tokens
+        assert len(input_id) == len(target)
+        for j, sentence in enumerate(source):
+            role = roles[sentence["from"]]
+            if has_image and sentence["value"] is not None and "<image>" in sentence["value"]:
+                num_image = len(re.findall(DEFAULT_IMAGE_TOKEN, sentence["value"]))
+                texts = sentence["value"].split("<image>")
+                _input_id = tokenizer(role).input_ids + nl_tokens
+                for i, text in enumerate(texts):
+                    _input_id += tokenizer(text).input_ids
+                    if i < len(texts) - 1:
+                        _input_id += [IMAGE_TOKEN_INDEX] + nl_tokens
+                _input_id += [im_end] + nl_tokens
+                assert sum([i == IMAGE_TOKEN_INDEX for i in _input_id]) == num_image
+            else:
+                if sentence["value"] is None:
+                    _input_id = tokenizer(role).input_ids + nl_tokens
+                else:
+                    _input_id = tokenizer(role).input_ids + nl_tokens + tokenizer(sentence["value"]).input_ids + [im_end] + nl_tokens
+            input_id += _input_id
+            if role == "<|im_start|>user":
+                _target = [im_start] + [IGNORE_INDEX] * (len(_input_id) - 3) + [im_end] + nl_tokens
+            elif role == "<|im_start|>assistant":
+                _target = [im_start] + [IGNORE_INDEX] * len(tokenizer(role).input_ids) + _input_id[len(tokenizer(role).input_ids) + 1 : -2] + [im_end] + nl_tokens
+            else:
+                raise NotImplementedError
+            target += _target
+
+        input_ids.append(input_id)
+        targets.append(target)
+        input_ids = torch.tensor(input_ids, dtype=torch.long)
+        targets = torch.tensor(targets, dtype=torch.long)
+        return input_ids
+
+    def generate_until(self, requests: List[Instance]) -> List[str]:
+        res = []
+
+        def _collate(x):
+            # the negative sign on len(toks) sorts descending - this has a few advantages:
+            # - time estimates will always be over not underestimates, which is more useful for planning
+            # - to know the size of a batch when going through the list, you know the first one is always the batch
+            #   padded context length. this is useful to simplify the batching logic and more importantly to make
+            #   automatic adaptive batches much much easier to implement
+            # - any OOMs will happen right away rather than near the end
+            toks = self.tok_encode(x[0])
+            return -len(toks), x[0]
+
+        # we group requests by their generation_kwargs,
+        # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling
+        # in the same batch.
+        re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True)
+        chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None)
+        num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1
+        pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding")
+        for chunk in chunks:
+            batched_contexts, all_gen_kwargs, batched_doc_to_visual, batched_doc_id, batched_task, batched_split = zip(*chunk)
+            task = batched_task[0]
+            split = batched_split[0]
+            batched_visuals = [batched_doc_to_visual[0](self.task_dict[task][split][ids]) for ids in batched_doc_id]  # [B, N]
+            assert len(batched_visuals) == 1
+
+            # we assume all gen kwargs in the batch are the same
+            # this is safe to assume because the `grouper` object ensures it.
+            gen_kwargs = all_gen_kwargs[0]
+            if "until" in gen_kwargs:
+                gen_kwargs.pop("until")
+
+            question_input = []
+
+            for visual, context in zip(batched_visuals, batched_contexts):
+                if len(visual) > 1 or "image_aspect_ratio" not in self._config.__dict__:  # for multi image case, we treat per image aspect ratio as "pad" by default.
+                    self._config.image_aspect_ratio = getattr(gen_kwargs, "image_aspect_ratio", "pad")
+                    eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}")
+                # if (len(visual) > 1 or "image_aspect_ratio" not in self._config.__dict__) and ("image_aspect_ratio" in gen_kwargs.keys()):
+                #     self._config.image_aspect_ratio = gen_kwargs["image_aspect_ratio"]
+                #     eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}")
+
+                if type(visual[0]) == PIL.Image.Image:  # For image task
+                    image_tensor = process_images(visual, self._image_processor, self._config)
+                    if type(image_tensor) is list:
+                        image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor]
+                    else:
+                        image_tensor = image_tensor.to(dtype=torch.float16, device=self.device)
+
+                    task_type = "image"
+
+                elif type(visual[0]) == str:  # For video task
+                    image_tensor = []
+                    try:
+                        if self.video_decode_backend == "decord":
+                            frames = self.load_video(visual, self.max_frames_num)
+                        elif self.video_decode_backend == "pyav":
+                            frames = read_video_pyav(visual[0], num_frm=self.max_frames_num)
+                        frames = self._image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].half().cuda()
+                        image_tensor.append(frames)
+                    except Exception as e:
+                        eval_logger.error(f"Error {e} in loading video")
+                        image_tensor = None
+
+                    task_type = "video"
+
+                if image_tensor is not None and len(image_tensor) != 0 and DEFAULT_IMAGE_TOKEN not in context:
+                    """
+                    Three senarios:
+                    1. No image, and there for, no image token should be added.
+                    2. image token is already specified in the context, so we don't need to add it.
+                    3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line.
+                    4. For video tasks, we could add a <image> token or multiple <image> tokens for each frame in the context. This depends on the training strategy and should balance in test to decide which is better
+                    """
+                    if task_type == "image":
+                        image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visual) if isinstance(visual, list) else [DEFAULT_IMAGE_TOKEN]
+                    elif task_type == "video":
+                        image_tokens = [DEFAULT_IMAGE_TOKEN] * len(frames) if self.token_strategy == "multiple" else [DEFAULT_IMAGE_TOKEN]
+
+                    image_tokens = " ".join(image_tokens)
+                    question = image_tokens + "\n" + context
+                else:
+                    question = context
+
+                # This is much safer for llama3, as we now have some object type in it
+                if "llama_3" in self.conv_template:
+                    conv = copy.deepcopy(conv_templates[self.conv_template])
+                else:
+                    conv = conv_templates[self.conv_template].copy()
+                conv.append_message(conv.roles[0], question)
+                conv.append_message(conv.roles[1], None)
+                prompt_question = conv.get_prompt()
+                question_input.append(prompt_question)
+
+            # preconfigure gen_kwargs with defaults
+            if "max_new_tokens" not in gen_kwargs:
+                gen_kwargs["max_new_tokens"] = 1024
+            if "temperature" not in gen_kwargs:
+                gen_kwargs["temperature"] = 0
+            if "do_sample" not in gen_kwargs:
+                gen_kwargs["do_sample"] = False
+            if "top_p" not in gen_kwargs:
+                gen_kwargs["top_p"] = None
+            if "num_beams" not in gen_kwargs:
+                gen_kwargs["num_beams"] = 1
+
+            input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input]
+            pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
+            input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device)
+            attention_masks = input_ids.ne(pad_token_ids).to(self.device)
+
+            # message_stage = [
+            #     {
+            #         "from": "human",
+            #         "value": question,
+            #     },
+            #     {
+            #         "from": "gpt",
+            #         "value": None,
+            #     }
+            # ]
+            # qwen_input_ids = self.preprocess_qwen(message_stage, self.tokenizer, has_image=True).to(self.device)
+            # qwen_result_list = qwen_input_ids.detach().cpu().numpy().tolist()
+            # qwen_result_list = [i if i != -200 else 100 for i in qwen_result_list[0]]
+            # qwen_input_text = self.tokenizer.decode(qwen_result_list)
+
+            # original_result_list = input_ids.detach().cpu().numpy().tolist()
+            # original_result_list = [i if i != -200 else 100 for i in original_result_list[0]]
+            # original_input_text = self.tokenizer.decode(original_result_list)
+
+            # print(f"Qwen input text: {qwen_input_text}")
+            # print(f"Original input text: {original_input_text}")
+
+            # assert qwen_input_ids == input_ids
+
+            if task_type == "image":
+                gen_kwargs["image_sizes"] = [batched_visuals[0][idx].size for idx in range(len(batched_visuals[0]))]
+            elif task_type == "video":
+                stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+                keywords = [stop_str]
+                stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids)
+                gen_kwargs["modalities"] = ["video"]
+                gen_kwargs["stopping_criteria"] = [stopping_criteria]
+                self._config.mm_spatial_pool_stride = self.mm_spatial_pool_stride
+                self._config.mm_spatial_pool_mode = self.mm_spatial_pool_mode
+
+            # These steps are not in LLaVA's original code, but are necessary for generation to work
+            # TODO: attention to this major generation step...
+            if "image_aspect_ratio" in gen_kwargs.keys():
+                gen_kwargs.pop("image_aspect_ratio")
+            try:
+                with torch.inference_mode():
+                    cont = self.model.generate(input_ids, attention_mask=attention_masks, pad_token_id=pad_token_ids, images=image_tensor, use_cache=self.use_cache, **gen_kwargs)
+                    # cont = self.model.generate(qwen_input_ids, pad_token_id=pad_token_ids, images=image_tensor, use_cache=self.use_cache, **gen_kwargs)
+
+                text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True)
+            except Exception as e:
+                raise e
+
+            text_outputs = [response.strip() for response in text_outputs]
+            res.extend(text_outputs)
+            self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs)
+            pbar.update(1)
+            # reorder this group of results back to original unsorted form
+        res = re_ords.get_original(res)
+
+        pbar.close()
+        return res
diff --git a/lmms_eval/models/reka.py b/lmms_eval/models/reka.py
index bc461cad..ee1b9c67 100644
--- a/lmms_eval/models/reka.py
+++ b/lmms_eval/models/reka.py
@@ -36,7 +36,7 @@ def __init__(
         self,
         model_version: str = "reka-edge",
         modality: str = "image",
-        max_frames_for_video: int = 10,
+        max_frames_num: int = 5,
         timeout: int = 120,
         continual_mode: bool = False,
         response_persistent_folder: str = None,  # We will cache the Gemini API response in this path and use it for future requests
@@ -45,21 +45,24 @@ def __init__(
         super().__init__()
         self.model_version = model_version
         self.modality = modality
-        self.max_frames_for_video = max_frames_for_video
+        self.max_frames_num = max_frames_num
         self.timeout = timeout
         self.continual_mode = continual_mode
-        if self.continual_mode and response_persistent_folder is None:
-            raise ValueError("Continual mode requires a persistent path for the response. Please provide a valid path.")
-        self.response_persistent_folder = response_persistent_folder
-        self.response_persistent_file = os.path.join(self.response_persistent_folder, f"{self.model_version}_response.json")
-
-        if os.path.exists(self.response_persistent_file):
-            with open(self.response_persistent_file, "r") as f:
-                self.response_cache = json.load(f)
-            self.cache_mode = "resume"
-        else:
-            self.response_cache = {}
-            self.cache_mode = "start"
+        if self.continual_mode:
+            if response_persistent_folder is None:
+                raise ValueError("Continual mode requires a persistent path for the response. Please provide a valid path.")
+
+            os.makedirs(response_persistent_folder, exist_ok=True)
+            self.response_persistent_folder = response_persistent_folder
+            self.response_persistent_file = os.path.join(self.response_persistent_folder, f"{self.model_version}_response.json")
+
+            if os.path.exists(self.response_persistent_file):
+                with open(self.response_persistent_file, "r") as f:
+                    self.response_cache = json.load(f)
+                self.cache_mode = "resume"
+            else:
+                self.response_cache = {}
+                self.cache_mode = "start"
 
         self.reka = RekaClient(api_key=os.getenv("REKA_API_KEY", "YOUR_API_KEY"))
 
@@ -99,7 +102,7 @@ def encode_image(self, image):
     def encode_video(self, video_path):
         vr = VideoReader(video_path, ctx=cpu(0))
         total_frame_num = len(vr)
-        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, self.max_frames_for_video, dtype=int)
+        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, self.max_frames_num, dtype=int)
         frame_idx = uniform_sampled_frames.tolist()
         frames = vr.get_batch(frame_idx).asnumpy()
 
@@ -141,7 +144,7 @@ def generate_until(self, requests) -> List[str]:
                 message_content.append({"type": "text", "text": context})
                 assert len(visual) == 1, "Reka only supports one video per request"
                 media_urls = self.encode_video(visual[0])
-                assert len(media_urls) == self.max_frames_for_video, f"Reka only supports {self.max_frames_for_video} frames per request"
+                assert len(media_urls) == self.max_frames_num, f"Reka only supports {self.max_frames_num} frames per request"
                 for media_url in media_urls:
                     message_content.append({"type": "image_url", "image_url": media_url})
 
diff --git a/lmms_eval/models/xcomposer2_4KHD.py b/lmms_eval/models/xcomposer2_4KHD.py
deleted file mode 100644
index 6c4f81a7..00000000
--- a/lmms_eval/models/xcomposer2_4KHD.py
+++ /dev/null
@@ -1,295 +0,0 @@
-from multiprocessing import context
-import torch
-from transformers import AutoModel, AutoTokenizer
-from PIL import Image
-import numpy as np
-import torchvision.transforms as transforms
-from datetime import timedelta
-
-
-from lmms_eval import utils
-from lmms_eval.api.instance import Instance
-from lmms_eval.api.model import lmms
-from lmms_eval.api.registry import register_model
-from lmms_eval.utils import stop_sequences_criteria
-
-from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
-from accelerate.state import AcceleratorState
-
-from typing import Optional, Sequence, List, Tuple, Union
-import re
-from tqdm import tqdm
-
-pattern = re.compile(r"[A-Z]")
-
-from loguru import logger as eval_logger
-
-meta_instruction = """You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔).
-- InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed\
- by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
-- InternLM-XComposer (浦语·灵笔) can understand and communicate fluently in the language chosen by\
- the user such as English and 中文.
-- InternLM-XComposer (浦语·灵笔) is capable of comprehending and articulating responses\
- effectively based on the provided image."""
-
-
-@register_model("xcomposer2_4khd")
-class XComposer2_4KHD(lmms):
-    def __init__(
-        self,
-        pretrained: str = "internlm/internlm-xcomposer2-4khd-7b",
-        device: Optional[str] = "cuda:0",
-        batch_size: Optional[Union[int, str]] = 1,
-        device_map="cuda:0",
-        need_bos: bool = True,
-        padding: bool = False,
-        half: bool = False,
-        **kwargs,
-    ) -> None:
-        super().__init__()
-
-        accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52))
-        accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs])
-        if accelerator.num_processes > 1:
-            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
-            self.device_map = f"cuda:{accelerator.local_process_index}"
-        elif accelerator.num_processes == 1 and device_map == "auto":
-            self._device = torch.device(device)
-            self.device_map = device_map
-        else:
-            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
-            self.device_map = f"cuda:{accelerator.local_process_index}"
-
-        self.pretrained = pretrained
-        self.need_bos = need_bos
-        self.padding = padding
-        self._model = AutoModel.from_pretrained(self.pretrained, device_map=self.device_map, trust_remote_code=True)
-        self._tokenizer = AutoTokenizer.from_pretrained(self.pretrained, trust_remote_code=True)
-        self.model.tokenizer = self.tokenizer
-        self.batch_size_per_gpu = batch_size
-
-        if accelerator.num_processes > 1:
-            assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
-            # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
-            # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works
-            # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work.
-            if accelerator.distributed_type == DistributedType.DEEPSPEED:
-                kwargs = {
-                    "train_micro_batch_size_per_gpu": self.batch_size_per_gpu,
-                    "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes,
-                }
-                AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs)
-                eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0")
-            if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED:
-                self._model = accelerator.prepare(self.model)
-            else:
-                self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
-            self.accelerator = accelerator
-            if self.accelerator.is_local_main_process:
-                eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
-            self._rank = self.accelerator.local_process_index
-            self._world_size = self.accelerator.num_processes
-        elif accelerator.num_processes == 1 and device_map == "auto":
-            eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism")
-            self._rank = 0
-            self._word_size = 1
-        else:
-            eval_logger.info(f"Using single device: {self._device}")
-            self.model.to(self._device)
-            self._rank = 0
-            self._world_size = 1
-
-    @property
-    def config(self):
-        # return the associated transformers.AutoConfig for the given pretrained model.
-        return self._config
-
-    @property
-    def tokenizer(self):
-        return self._tokenizer
-
-    @property
-    def model(self):
-        # returns the model, unwrapping it if using Accelerate
-        if hasattr(self, "accelerator"):
-            return self.accelerator.unwrap_model(self._model)
-        else:
-            return self._model
-
-    @property
-    def batch_size(self):
-        return self.batch_size_per_gpu
-
-    @property
-    def device(self):
-        return self._device
-
-    @property
-    def rank(self):
-        return self._rank
-
-    @property
-    def world_size(self):
-        return self._world_size
-
-    def flatten(self, input):
-        new_list = []
-        for i in input:
-            for j in i:
-                new_list.append(j)
-        return new_list
-
-    def generate_until(self, requests) -> List[str]:
-        res = []
-        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
-
-        for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
-            # encode, pad, and truncate contexts for this batch
-            if "[UNUSED_TOKEN_146]" not in contexts:
-                contexts = f"[UNUSED_TOKEN_146]user\n{contexts}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
-            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
-            visuals = self.flatten(visuals)
-
-            if "hd_num" not in gen_kwargs:
-                if listinstr(["docvqa_test", "infovqa_test"], task.lower()):
-                    self.model.hd_num = 65
-                elif listinstr(["docvqa_val", "infovqa_val", "OCRBench"], task.lower()):
-                    self.model.hd_num = 55
-                elif listinstr(["mmmu", "mmbench", "mmvet"], task.lower()):
-                    self.model.hd_num = 16
-                else:
-                    self.model.hd_num = 25
-            else:
-                self.model.hd_num = gen_kwargs.pop("hd_num")
-
-            pt1 = 0
-            embeds = []
-            im_mask = []
-            images_loc = [0]
-            need_bos = self.need_bos
-            padding = self.padding
-            for i, pts in enumerate(images_loc + [len(contexts)]):
-                subtext = contexts[pt1:pts]
-                if need_bos or len(subtext) > 0:
-                    text_embeds = self.model.encode_text(subtext, add_special_tokens=need_bos).to(self.device)
-                    embeds.append(text_embeds)
-                    im_mask.append(torch.zeros(text_embeds.shape[:2]).to(self.device))
-                    need_bos = False
-                if i < len(visuals):
-                    image = visuals[i]
-
-                    image = HD_transform(image, im_num=self.model.hd_num)
-                    image = self.model.vis_processor(image).unsqueeze(0).to(self.device)
-                    image_embeds = self.model.encode_img(image)
-                    embeds.append(image_embeds)
-                    im_mask.append(torch.ones(image_embeds.shape[:2]).to(self.device))
-                pt1 = pts
-            embeds = torch.cat(embeds, dim=1)
-            im_mask = torch.cat(im_mask, dim=1)
-            im_mask = im_mask.bool()
-
-            if "max_new_tokens" not in gen_kwargs:
-                gen_kwargs["max_new_tokens"] = 1024
-            if "temperature" not in gen_kwargs:
-                gen_kwargs["temperature"] = 0
-            if "top_p" not in gen_kwargs:
-                gen_kwargs["top_p"] = None
-            if "num_beams" not in gen_kwargs:
-                gen_kwargs["num_beams"] = 1
-            if "do_sample" not in gen_kwargs:
-                gen_kwargs["do_sample"] = False
-            if "repetition_penalty" not in gen_kwargs:
-                gen_kwargs["repetition_penalty"] = 1.0
-
-            outputs = self.model.generate(
-                inputs_embeds=embeds,
-                im_mask=im_mask,
-                temperature=gen_kwargs["temperature"],
-                max_new_tokens=gen_kwargs["max_new_tokens"],
-                num_beams=gen_kwargs["num_beams"],
-                do_sample=gen_kwargs["do_sample"],
-                repetition_penalty=gen_kwargs["repetition_penalty"],
-            )
-            output_token = outputs[0]
-            if output_token[0] == 0 or output_token[0] == 1:
-                output_token = output_token[1:]
-            output_text = self.model.tokenizer.decode(output_token, add_special_tokens=False)
-            output_text = output_text.split("[UNUSED_TOKEN_145]")[0].strip()
-            output_text = output_text.split("<|im_end|>")[0].strip()
-            # if DATASET_TYPE(task) == "multi-choice":
-            #     output_text = pattern.findall(output_text)
-            #     if len(output_text) == 0:
-            #         print("Error:", output_text)
-            #         output_text = "Z"
-            #     if type(output_text) == list:
-            #         output_text = output_text[0]
-            res.append(output_text)
-            pbar.update(1)
-        pbar.close()
-        return res
-
-    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
-        return super().loglikelihood(requests)
-
-
-def padding_336(b):
-    width, height = b.size
-    tar = int(np.ceil(height / 336) * 336)
-    top_padding = int((tar - height) / 2)
-    bottom_padding = tar - height - top_padding
-    left_padding = 0
-    right_padding = 0
-    b = transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255, 255, 255])
-
-    return b
-
-
-def HD_transform(img, im_num=16):
-    width, height = img.size
-    trans = False
-    if width < height:
-        img = img.transpose(Image.TRANSPOSE)
-        trans = True
-        width, height = img.size
-    ratio = width / height
-    scale = 1
-    while scale * np.ceil(scale / ratio) <= im_num:
-        scale += 1
-    scale -= 1
-    new_w = int(scale * 336)
-    new_h = int(new_w / ratio)
-
-    img = transforms.functional.resize(
-        img,
-        [new_h, new_w],
-    )
-    img = padding_336(img)
-    width, height = img.size
-    assert width * height <= im_num * 336 * 336
-    if trans:
-        img = img.transpose(Image.TRANSPOSE)
-
-    return img
-
-
-def listinstr(lst, s):
-    assert isinstance(lst, list)
-    for item in lst:
-        if item in s:
-            return True
-    return False
-
-
-def DATASET_TYPE(dataset):
-    # Dealing with Custom Dataset
-    dataset = dataset.lower()
-    if listinstr(["mmbench", "seedbench", "ccbench", "mmmu", "scienceqa", "ai2d", "mmstar"], dataset):
-        return "multi-choice"
-    elif listinstr(["mme", "hallusion"], dataset):
-        return "Y/N"
-    elif "coco" in dataset:
-        return "Caption"
-    elif listinstr(["ocrvqa", "textvqa", "chartqa", "mathvista", "docvqa", "infovqa", "llavabench", "mmvet", "ocrbench"], dataset):
-        return "VQA"
-    else:
-        return "QA"
diff --git a/lmms_eval/tasks/__init__.py b/lmms_eval/tasks/__init__.py
index 19f7fea2..fadbb7bf 100755
--- a/lmms_eval/tasks/__init__.py
+++ b/lmms_eval/tasks/__init__.py
@@ -111,7 +111,7 @@ def include_path(task_dir):
 def initialize_tasks(verbosity="INFO"):
     logger.remove()
     eval_logger.add(sys.stdout, colorize=True, level=verbosity)
-    eval_logger.add(sys.stderr, level=verbosity)
+    # eval_logger.add(sys.stderr, level=verbosity)
     task_dir = os.path.dirname(os.path.abspath(__file__)) + "/"
     include_path(task_dir)
 
diff --git a/lmms_eval/tasks/llava_interleave_bench/_default_template_interleave_yaml b/lmms_eval/tasks/llava_interleave_bench/_default_template_interleave_yaml
new file mode 100644
index 00000000..25b44461
--- /dev/null
+++ b/lmms_eval/tasks/llava_interleave_bench/_default_template_interleave_yaml
@@ -0,0 +1,9 @@
+output_type: generate_until
+generation_kwargs:
+  until:
+    - "ASSISTANT:"
+  image_aspect_ratio: original
+metadata:
+  version: 0.0
+  api_type : openai
+  gpt_eval_model_name: "gpt-3.5-turbo"
\ No newline at end of file
diff --git a/lmms_eval/tasks/llava_interleave_bench/in_domain.yaml b/lmms_eval/tasks/llava_interleave_bench/in_domain.yaml
new file mode 100644
index 00000000..86255d95
--- /dev/null
+++ b/lmms_eval/tasks/llava_interleave_bench/in_domain.yaml
@@ -0,0 +1,29 @@
+dataset_path: lmms-lab/LLaVA-NeXT-Interleave-Bench
+dataset_name: in_domain
+dataset_kwargs:
+  token: True
+task: "llava_interleave_bench_in_domain"
+test_split: test
+doc_to_target: "answer"
+doc_to_visual: !function utils.doc_to_visual
+doc_to_text: !function utils.doc_to_text
+process_results: !function utils.interleave_process_results
+
+metric_list:
+  - metric: mcq_acc
+    aggregation: !function utils.mcq_acc
+    higher_is_better: true
+  - metric: oe_rogue
+    aggregation: !function utils.oe_rogue
+    higher_is_better: true
+
+generation_kwargs:
+  max_new_tokens: 16
+  temperature: 0
+  do_sample: False
+  image_aspect_ratio: "pad" # for multi-image, we treat each image as original aspect ratio without anyres strategy.
+
+model_specific_prompt_kwargs:
+  default:
+    oe_post_prompt: ""
+    mcq_post_prompt: "Answer with the option's letter from the given choices directly."
\ No newline at end of file
diff --git a/lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml b/lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml
new file mode 100644
index 00000000..f478caeb
--- /dev/null
+++ b/lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml
@@ -0,0 +1,29 @@
+dataset_path: lmms-lab/LLaVA-NeXT-Interleave-Bench
+dataset_name: multi_view_in_domain
+dataset_kwargs:
+  token: True
+task: "llava_interleave_bench_multi_view_in_domain"
+test_split: test
+doc_to_target: "answer"
+doc_to_visual: !function utils.doc_to_visual
+doc_to_text: !function utils.doc_to_text
+process_results: !function utils.interleave_process_results
+
+metric_list:
+  - metric: mcq_acc
+    aggregation: !function utils.mcq_acc
+    higher_is_better: true
+  - metric: oe_rogue
+    aggregation: !function utils.oe_rogue
+    higher_is_better: true
+
+generation_kwargs:
+  max_new_tokens: 16
+  temperature: 0
+  do_sample: False
+  image_aspect_ratio: "original" # for multi-image, we treat each image as original aspect ratio without anyres strategy.
+
+model_specific_prompt_kwargs:
+  default:
+    oe_post_prompt: ""
+    mcq_post_prompt: "Answer with the option's letter from the given choices directly."
\ No newline at end of file
diff --git a/lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml b/lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml
new file mode 100644
index 00000000..62601c85
--- /dev/null
+++ b/lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml
@@ -0,0 +1,29 @@
+dataset_path: lmms-lab/LLaVA-NeXT-Interleave-Bench
+dataset_name: out_of_domain
+dataset_kwargs:
+  token: True
+task: "llava_interleave_bench_out_domain"
+test_split: test
+doc_to_target: "answer"
+doc_to_visual: !function utils.doc_to_visual
+doc_to_text: !function utils.doc_to_text
+process_results: !function utils.interleave_process_results
+
+metric_list:
+  - metric: mcq_acc
+    aggregation: !function utils.mcq_acc
+    higher_is_better: true
+  - metric: oe_rogue
+    aggregation: !function utils.oe_rogue
+    higher_is_better: true
+
+generation_kwargs:
+  max_new_tokens: 16
+  temperature: 0
+  do_sample: False
+  image_aspect_ratio: "pad" # for multi-image, we treat each image as original aspect ratio without anyres strategy.
+
+model_specific_prompt_kwargs:
+  default:
+    oe_post_prompt: ""
+    mcq_post_prompt: "Answer with the option's letter from the given choices directly."
\ No newline at end of file
diff --git a/lmms_eval/tasks/llava_interleave_bench/utils.py b/lmms_eval/tasks/llava_interleave_bench/utils.py
new file mode 100644
index 00000000..1b95dc5f
--- /dev/null
+++ b/lmms_eval/tasks/llava_interleave_bench/utils.py
@@ -0,0 +1,331 @@
+import os
+import re
+import sys
+import datetime
+import json
+import requests
+import time
+
+from lmms_eval.filters.extraction import ExtendedRegexFilter
+
+import yaml
+from pathlib import Path
+from loguru import logger as eval_logger
+from PIL import Image
+
+
+def doc_to_visual(doc):
+    max_visual_count = 16
+    visuals = []
+    for i in range(max_visual_count):
+        if f"image_{i}" in doc:
+            image = doc[f"image_{i}"]
+            if image is None:
+                continue  # Skip this image if it's None
+            if isinstance(image, Image.Image):
+                visuals.append(image.copy())
+            else:
+                try:
+                    # If the image is not already a PIL Image, try to open it
+                    visuals.append(Image.open(image))
+                except Exception as e:
+                    print(f"Error opening image_{i}: {e}")
+                    # Optionally, you can add a placeholder image or just continue
+                    continue
+
+    return visuals
+
+
+# This is the place where you format your question
+def doc_to_text(doc, model_specific_prompt_kwargs=None):
+    if model_specific_prompt_kwargs is None:
+        model_specific_prompt_kwargs = {}
+
+    oe_post_prompt = ""
+    if "oe_post_prompt" in model_specific_prompt_kwargs:
+        oe_post_prompt = model_specific_prompt_kwargs["oe_post_prompt"]
+
+    mcq_post_prompt = ""
+    if "mcq_post_prompt" in model_specific_prompt_kwargs:
+        mcq_post_prompt = model_specific_prompt_kwargs["mcq_post_prompt"]
+
+    user_prompt = doc["question"]
+
+    if mcq_post_prompt != "" and doc["question_type"] == "multi-choice":
+        user_prompt = user_prompt.split("Your answer is:")[0].split("\n")[0].strip()
+        user_prompt = f"{user_prompt}\n{mcq_post_prompt}"
+
+    if oe_post_prompt != "" and doc["question_type"] == "open-ended":
+        user_prompt = f"{user_prompt}\n{oe_post_prompt}"
+
+    return user_prompt
+
+
+def doc_to_text_multi_turn(doc, model_specific_prompt_kwargs=None):
+    if model_specific_prompt_kwargs is None:
+        model_specific_prompt_kwargs = {}
+
+    return doc["conversations"]
+
+
+def interleave_process_results(doc, results):
+    pred = results[0]
+    sample_id = doc["sample_id"]
+    model_response = {"sample_id": sample_id, "subtask": doc["sample_id"], "question_type": doc["question_type"], "answer": doc["answer"], "parsed_pred": pred}
+    return {
+        "mcq_acc": model_response,
+        "oe_rogue": model_response,
+        # "in_domain_oe_gpt_eval": in_domain_acc,
+    }
+
+
+def mcq_acc(results, args):
+    correct_count = 0
+    total_count = len(results)
+
+    periodStrip = re.compile("(?!<=\d)(\.)(?!\d)")
+    commaStrip = re.compile("(\d)(\,)(\d)")
+    punct = [";", r"/", "[", "]", '"', "{", "}", "(", ")", "=", "+", "\\", "_", "-", ">", "<", "@", "`", ",", "?", "!"]
+
+    def processPunctuation(inText):
+        outText = inText
+        for p in punct:
+            if (p + " " in inText or " " + p in inText) or (re.search(commaStrip, inText) != None):
+                outText = outText.replace(p, "")
+            else:
+                outText = outText.replace(p, " ")
+        outText = periodStrip.sub("", outText, re.UNICODE)
+        return outText
+
+    def process(answer):
+        option_regex = re.compile(r"^([A-E])\.\s*(.+)$", re.IGNORECASE)
+        match = option_regex.match(answer.strip())
+
+        if match:
+            # If matched, return the option letter in uppercase
+            return match.group(1).upper()
+        else:
+            # If no match, process the answer as before
+            answer = answer.replace("\n", " ")
+            answer = answer.replace("\t", " ")
+            answer = answer.strip()
+            answer = processPunctuation(answer)
+            answer = answer.strip("'")
+            answer = answer.strip('"')
+            answer = answer.strip(")")
+            answer = answer.strip("(")
+            answer = answer.strip().lower()
+
+            # Try to find any single letter (A-E) in the processed answer
+            letter_match = re.search(r"\b([A-E])\b", answer, re.IGNORECASE)
+            if letter_match:
+                return letter_match.group(1).upper()
+
+            return answer
+
+    # image_choice_dataset_list = ["recipeqa-RecipeQA_VisualCloze", "RecipeQA_ImageCoherence", "COMICS_Panel"]
+    mcq_eval_result_list = []
+    mcq_eval_result_list_detail = defaultdict(list)
+
+    for result in results:
+        if result["question_type"] == "multi-choice":
+            pred = process(result["parsed_pred"])
+            answer = process(result["answer"])
+
+            if pred == answer:
+                score = 1
+            else:
+                score = 0
+
+            mcq_eval_result_list_detail[result["sub_task"]].append(score)
+            mcq_eval_result_list[result["sub_task"]] = mcq_eval_result_list_detail[result["sub_task"]]
+
+    overall_accuracy = sum(mcq_eval_result_list) / len(mcq_eval_result_list)
+    for sub_task in mcq_eval_result_list:
+        sub_task_accuracy = sum(mcq_eval_result_list[sub_task]) / len(mcq_eval_result_list[sub_task]) * 100.0
+        eval_logger.info(f"Multi-Choice Sub-Task {sub_task} - accuracy: {sub_task_accuracy}")
+    return overall_accuracy
+
+
+from rouge import Rouge
+import numpy as np
+
+
+def oe_rogue(results, args):
+    rouge = Rouge()
+    oe_eval_result_list = []
+    oe_eval_result_list_detail = defaultdict(list)
+
+    for result in results:
+        if result["question_type"] == "open-ended":
+            pred = result["parsed_pred"]
+            answer = result["answer"]
+
+            if answer == "":
+                continue
+
+            if pred == "":
+                score = 0
+            else:
+                if len(pred) > 512:
+                    pred = pred[:512]
+                score = rouge.get_scores(pred, answer)[0]["rouge-l"]["f"]
+
+            oe_eval_result_list.append(score)
+            oe_eval_result_list_detail[result["sub_task"]].append(score)
+
+    average_rouge_score = np.mean(oe_eval_result_list) if oe_eval_result_list else 0
+    for sub_task in oe_eval_result_list_detail:
+        sub_task_rouge_score = np.mean(oe_eval_result_list_detail[sub_task]) if oe_eval_result_list_detail[sub_task] else 0
+        eval_logger.info(f"Open-Ended Sub-Task {sub_task} - ROUGE-L: {sub_task_rouge_score}")
+    return average_rouge_score * 100.0
+
+
+EVAL_PROMPT = """
+[Question]
+{question}
+
+[Assistant Response]
+{model_response}
+
+[Ground Truth Response]
+{ground_truth}
+
+[System]
+Rate whether the assistant response correctly matches the ground truth, it's about a question towards a sequence of images shared by the user.
+The rating should be 1-5, where 1 is incorrect and 5 is correct.
+Your response should be in the format:
+Explanation: (your explanation)
+Rating: (int)
+"""
+
+NUM_SECONDS_TO_SLEEP = 5
+dir_path = os.path.dirname(os.path.realpath(__file__))
+with open(Path(__file__).parent / "_default_template_interleave_yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+
+    config = yaml.safe_load("".join(safe_data))
+
+GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"]
+API_TYPE = config["metadata"]["api_type"]
+
+if API_TYPE == "openai":
+    API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
+    API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
+    headers = {
+        "Authorization": f"Bearer {API_KEY}",
+        "Content-Type": "application/json",
+    }
+elif API_TYPE == "azure":
+    API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
+    API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
+    headers = {
+        "api-key": API_KEY,
+        "Content-Type": "application/json",
+    }
+else:
+    API_URL = ""
+    API_KEY = ""
+
+
+def get_chat_response(prompt, max_retries=5, wait_time=10):
+    headers = {
+        "Authorization": f"Bearer {API_KEY}",
+        "Content-Type": "application/json",
+    }
+
+    payload = {
+        "model": GPT_EVAL_MODEL_NAME,
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt},
+                ],
+            }
+        ],
+        "max_tokens": 1024,
+        "temperature": 0.0,
+    }
+
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
+            response.raise_for_status()
+            response_data = response.json()
+            return response_data["choices"][0]["message"]["content"], GPT_EVAL_MODEL_NAME
+        except requests.exceptions.RequestException as e:
+            eval_logger.warning(f"Request failed on attempt {attempt+1}: {e}")
+            time.sleep(wait_time)
+            if attempt == max_retries - 1:
+                eval_logger.error(f"Failed to get response after {max_retries} attempts")
+                return "", GPT_EVAL_MODEL_NAME
+        except Exception as e:
+            eval_logger.error(f"Error on attempt {attempt+1}: {e}")
+            return "", GPT_EVAL_MODEL_NAME
+
+
+def in_domain_oe_gpt_eval(results, args):
+    total_score = 0
+    available_count = 0
+    for result in results:
+        if result["question_type"] == "open-ended":
+            question = result["question"]
+            model_response = result["parsed_pred"]
+            ground_truth = result["answer"]
+            content = EVAL_PROMPT.format(question=question, model_response=model_response, ground_truth=ground_truth)
+            result["gpt_eval_input"] = content
+            model_output, model_name = get_chat_response(content)
+            try:
+                explanation = re.search(r"Explanation: (.*)\n", model_output).group(1)
+                rating = re.search(r"Rating: (\d+)\n", model_output).group(1)
+                result["gpt_eval_explanation"] = explanation
+                result["gpt_eval_rating"] = rating
+                result["gpt_eval_model_name"] = model_name
+            except:
+                eval_logger.error(f"Error on evaluating {result['sample_id']}. Results: {results}")
+                result["gpt_eval_explanation"] = ""
+                result["gpt_eval_rating"] = 0
+                result["gpt_eval_model_name"] = model_name
+
+            total_score += result["gpt_eval_rating"]
+            available_count += 1
+
+        elif result["question_type"] == "multi-choice":
+            pass
+
+    return (total_score / available_count) * 20.0 if available_count > 0 else 0
+
+
+# class MultiChoiceRegexFilter(ExtendedRegexFilter):
+#     def __init__(self, *args, **kwargs):
+#         super().__init__(*args, **kwargs)
+
+#     def apply(self, resps, docs):
+#         filtered_resps = []
+
+#         for r, doc in zip(resps, docs):
+#             # Regex to directly extract the option letter from the model response
+#             option_letter_regex = re.compile(r"\b([A-Z])\.\s+([^\n]*)")
+
+#             # Process each response
+#             filtered = []
+#             for resp in r:
+#                 # Try to match the option letter at the start of the response
+#                 match = option_letter_regex.match(resp)
+#                 if match:
+#                     # If a match is found, append the matched letter
+#                     filtered.append(match.group(1))
+#                 else:
+#                     # If no match, return the original response
+#                     filtered.append(resp)
+
+#             # Assuming we need the first response that matches or the original response
+#             filtered_resps.append(filtered[0])
+
+#         return filtered_resps
diff --git a/lmms_eval/tasks/mix_evals/_default_template_yaml b/lmms_eval/tasks/mix_evals/_default_template_yaml
new file mode 100644
index 00000000..ce26b0ea
--- /dev/null
+++ b/lmms_eval/tasks/mix_evals/_default_template_yaml
@@ -0,0 +1,16 @@
+dataset_path: lmms-lab/MixEvals_Video2Text
+dataset_kwargs:
+  token: True
+  video: True
+  cache_dir: mix_evals_video2text
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: ""
+  gpt4v:
+    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
+    post_prompt: ""
+metadata:
+  modality: video
+  version: 0.0
+  gpt_eval_model_name: "gpt-3.5-turbo"
\ No newline at end of file
diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text.yaml
new file mode 100644
index 00000000..ed0a517c
--- /dev/null
+++ b/lmms_eval/tasks/mix_evals/mix_evals_video2text.yaml
@@ -0,0 +1,5 @@
+group: mix_evals_video2text
+task:
+- mix_evals_video2text_openconv
+- mix_evals_video2text_mc
+- mix_evals_video2text_freeform
\ No newline at end of file
diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text_freeform.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text_freeform.yaml
new file mode 100644
index 00000000..e8ec9b4a
--- /dev/null
+++ b/lmms_eval/tasks/mix_evals/mix_evals_video2text_freeform.yaml
@@ -0,0 +1,22 @@
+dataset_name: "video2text_closeended_free-form"
+task: "mix_evals_video2text_freeform"
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.mix_evals_video2text_doc_to_visual
+doc_to_text: !function utils.mix_evals_video2text_doc_to_text
+doc_to_target: "{{target}}"
+process_results: !function utils.mix_evals_video2text_process_results_freeform
+metric_list:
+  - metric: gpt_eval
+    aggregation: !function utils.mix_evals_video2text_gpt_eval
+    higher_is_better: true
+    
+include: _default_template_yaml
+
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
+    post_prompt: "Answer the question using a single word or phrase."
+  gpt4v:
+    pre_prompt: "These are frames from a video. Please answer the following questions about the video with a short phrase."
+    post_prompt: ""
\ No newline at end of file
diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text_mc.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text_mc.yaml
new file mode 100644
index 00000000..d04dabf4
--- /dev/null
+++ b/lmms_eval/tasks/mix_evals/mix_evals_video2text_mc.yaml
@@ -0,0 +1,31 @@
+include: _default_template_yaml
+dataset_name: "video2text_closeended_multiple-choice"
+task: "mix_evals_video2text_mc"
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.mix_evals_video2text_doc_to_visual
+doc_to_text: !function utils.mix_evals_video2text_doc_to_text
+doc_to_target: "{{target}}"
+
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+
+filter_list:
+  - name: "flexible-extract"
+    filter:
+      - function: !function utils.MultiChoiceRegexFilter
+        group_select: 0
+        ignore_case: true
+        ignore_punctuation: true
+
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
+    post_prompt: "Answer with the option's letter from the given choices directly."
+  gpt4v:
+    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
+    post_prompt: "Answer with the option's letter from the given choices directly."
\ No newline at end of file
diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended.yaml
new file mode 100644
index 00000000..eb3fca3d
--- /dev/null
+++ b/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended.yaml
@@ -0,0 +1,22 @@
+include: _default_template_yaml
+dataset_name: "video2text_openended"
+task: "mix_evals_video2text_openconv"
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.mix_evals_video2text_doc_to_visual
+doc_to_text: !function utils.mix_evals_video2text_doc_to_text_open_convs
+doc_to_target: ""
+process_results: !function utils.mix_evals_video2text_process_results_open_convs
+
+metric_list:
+  - metric: submission
+    aggregation: !function utils.mix_evals_video2text_aggregate_gen
+    higher_is_better: true
+
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
+    post_prompt: ""
+  gpt4v:
+    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
+    post_prompt: ""
diff --git a/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended_2nd.yaml b/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended_2nd.yaml
new file mode 100644
index 00000000..e8c0f1fe
--- /dev/null
+++ b/lmms_eval/tasks/mix_evals/mix_evals_video2text_openended_2nd.yaml
@@ -0,0 +1,23 @@
+include: _default_template_yaml
+dataset_path: lmms-lab/MixEvals_Video2Text_OpenEnded_2nd
+dataset_name: "video2text_openended"
+task: "mix_evals_video2text_openconv_2nd"
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.mix_evals_video2text_doc_to_visual
+doc_to_text: !function utils.mix_evals_video2text_doc_to_text_open_convs
+doc_to_target: ""
+process_results: !function utils.mix_evals_video2text_process_results_open_convs
+
+metric_list:
+  - metric: submission
+    aggregation: !function utils.mix_evals_video2text_aggregate_gen
+    higher_is_better: true
+
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
+    post_prompt: ""
+  gpt4v:
+    pre_prompt: "These are frames from a video. Please answer the following questions about the video."
+    post_prompt: ""
diff --git a/lmms_eval/tasks/mix_evals/utils.py b/lmms_eval/tasks/mix_evals/utils.py
new file mode 100644
index 00000000..6c3e6e6c
--- /dev/null
+++ b/lmms_eval/tasks/mix_evals/utils.py
@@ -0,0 +1,284 @@
+import os
+import re
+import sys
+import datetime
+import lmms_eval.tasks._task_utils.file_utils as file_utils
+from lmms_eval.filters.extraction import ExtendedRegexFilter
+import json
+import yaml
+from pathlib import Path
+import requests
+import time
+from loguru import logger as eval_logger
+
+with open(Path(__file__).parent / "_default_template_yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+
+    config = yaml.safe_load("".join(safe_data))
+
+NUM_SECONDS_TO_SLEEP = 5
+GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"]
+API_TYPE = os.getenv("API_TYPE", "openai")
+
+if API_TYPE == "openai":
+    API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
+    API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
+    headers = {
+        "Authorization": f"Bearer {API_KEY}",
+        "Content-Type": "application/json",
+    }
+elif API_TYPE == "azure":
+    API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
+    API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
+    headers = {
+        "api-key": API_KEY,
+        "Content-Type": "application/json",
+    }
+
+eval_prompt = """You are an AI assistant who will help me to evaluate the quality of a model response to a few candidate ground truth answers.
+
+Some criterion
+- Response that perfectly reflect the meaning of the ground truth: 1 point
+- Response that reflect none of the key points in the ground truth: 0 point
+- Some part in the response are correct but some parts in the ground truth are not mentioned in the response: 0.5 point
+- Some part in the response are correct but other parts in the response are not mentioned in the ground truth: 0.5 point
+
+Here're some examples about the scoring criterion and format:
+model response: Steam Cleaning Services
+ground truth: ["steam clean", "steam clean", "cleaning", "car", "steam clean"],
+Point: 1
+
+model response: A cowboy action shooter.
+ground truth: ["man"]
+Point: 1
+
+model response: I'm sorry, but I can't assist with that request.
+ground truth: ["quality"]
+Point: 0
+
+Let's begin this task:
+model response: {model_response}
+ground truth: {ground_truth}
+Point:"""
+
+
+def get_eval(model_response: str, ground_truth: str, max_tokens: int, retries: int = 5):
+    global headers
+    content = eval_prompt.format(model_response=model_response, ground_truth=ground_truth)
+
+    messages = [
+        {"role": "user", "content": content},
+    ]
+
+    payload = {
+        "model": GPT_EVAL_MODEL_NAME,
+        "messages": messages,
+        "temperature": 0.2,
+        "max_tokens": max_tokens,
+    }
+
+    for attempt in range(retries):
+        try:
+            response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
+            response.raise_for_status()
+            response_data = response.json()
+
+            content = response_data["choices"][0]["message"]["content"].strip()
+            if content != "":
+                return content, response_data["model"]
+            break  # If successful, break out of the loop
+
+        except Exception as e:
+            eval_logger.info(f"Attempt {attempt + 1} failed with error: {e}")
+            if attempt < retries:  # If we have retries left, sleep and then continue to next attempt
+                time.sleep(NUM_SECONDS_TO_SLEEP)
+            else:  # If this was the last attempt, log and return empty
+                eval_logger.error(f"All {retries} attempts failed. Last error message: {e}")
+                return "", ""
+    return "", ""
+
+
+# A bit ugly here
+# But the idea is that we will unzip all the zip files
+# To HF HOME cache dir
+# And load it here
+HF_HOME = os.environ["HF_HOME"]
+cache_dir = config["dataset_kwargs"]["cache_dir"]
+cache_dir = os.path.join(HF_HOME, cache_dir)
+cache_dir = os.path.join(cache_dir)
+
+
+# Pass in video path here
+# Can only work correctly with video llm
+def mix_evals_video2text_doc_to_visual(doc):
+    video_path = doc["video_path"]
+    video_path = os.path.join(cache_dir, video_path)
+    if os.path.exists(video_path):
+        video_path = video_path
+    elif os.path.exists(video_path.replace("mp4", "MP4")):
+        video_path = video_path.replace("mp4", "MP4")
+    else:
+        sys.exit(f"video path:{video_path} does not exist, please check")
+    return [video_path]
+
+
+# This is the place where you format your question
+def mix_evals_video2text_doc_to_text(doc, model_specific_prompt_kwargs=None):
+    if model_specific_prompt_kwargs is None:
+        model_specific_prompt_kwargs = {}
+    pre_prompt = ""
+    post_prompt = ""
+    if "pre_prompt" in model_specific_prompt_kwargs:
+        pre_prompt = model_specific_prompt_kwargs["pre_prompt"]
+    if "post_prompt" in model_specific_prompt_kwargs:
+        post_prompt = model_specific_prompt_kwargs["post_prompt"]
+
+    user_prompt = doc["prompt"]
+
+    if "options" in doc:
+        option_prompt = "Here are the options:\n"
+        for idx, option in enumerate(doc["options"]):
+            char_idx = chr(ord("A") + idx)
+            option = option.strip()
+            option_prompt += f"{char_idx}. {option}\n"
+
+        option_prompt = option_prompt.rstrip("\n")
+        user_prompt = f"{user_prompt}\n{option_prompt}"
+
+    if pre_prompt:
+        user_prompt = f"{pre_prompt}\n{user_prompt}"
+
+    if post_prompt:
+        user_prompt = f"{user_prompt}\n{post_prompt}"
+    return user_prompt
+
+
+OPEN_CONVS_PROMPT = """{PRE}
+{FIRST}
+{POST}
+"""
+
+
+def mix_evals_video2text_doc_to_text_open_convs(doc, model_specific_prompt_kwargs=None):
+    if model_specific_prompt_kwargs is None:
+        model_specific_prompt_kwargs = {}
+    pre_prompt = ""
+    post_prompt = ""
+    if "pre_prompt" in model_specific_prompt_kwargs:
+        pre_prompt = model_specific_prompt_kwargs["pre_prompt"]
+    if "post_prompt" in model_specific_prompt_kwargs:
+        post_prompt = model_specific_prompt_kwargs["post_prompt"]
+
+    filtered_first_turn = re.sub(r"<video_[0-9]+>", "", doc["first_turn_user_prompt"])
+    return OPEN_CONVS_PROMPT.format(
+        PRE=pre_prompt,
+        POST=post_prompt,
+        FIRST=filtered_first_turn,
+    )
+
+
+MODEL_CONVS_PROMPT = """{FIRST}
+{MODEL_RESPONSE}
+{PRE}
+{SECOND}
+{POST}
+"""
+
+
+def mix_evals_video2text_doc_to_text_open_2nd_convs(doc, model_specific_prompt_kwargs=None):
+    if model_specific_prompt_kwargs is None:
+        model_specific_prompt_kwargs = {}
+    pre_prompt = ""
+    post_prompt = ""
+    if "pre_prompt" in model_specific_prompt_kwargs:
+        pre_prompt = model_specific_prompt_kwargs["pre_prompt"]
+    if "post_prompt" in model_specific_prompt_kwargs:
+        post_prompt = model_specific_prompt_kwargs["post_prompt"]
+
+    return MODEL_CONVS_PROMPT.format(
+        PRE=pre_prompt,
+        POST=post_prompt,
+        FIRST=doc["first_turn_user_prompt"],
+        SECOND=doc["second_turn_user_prompt"],
+        MODEL_RESPONSE=doc["model_response"],
+    )
+
+
+def mix_evals_video2text_process_results_open_convs(doc, result):
+    pred = result[0]
+    return {"submission": {"pred": pred, "question_idx": doc["question_index"], "first_turn_video_caption": doc["first_turn_video_caption"], "target": ""}}
+
+
+def mix_evals_video2text_process_results_freeform(doc, result):
+    pred = result[0]
+    ground_truth_str = ", ".join([f'"{gt}"' for gt in doc["target"]])
+    ground_truth_str = f"[{ground_truth_str}]"
+    content = eval_prompt.format(model_response=pred, ground_truth=ground_truth_str)
+    eval_answer, model_name = get_eval(model_response=pred, ground_truth=ground_truth_str, max_tokens=1024)
+    return {
+        "submission": {"pred": pred, "question_idx": doc["question_index"], "target": doc["target"], "eval_answer": eval_answer, "gpt_prompt": content},
+        "gpt_eval": {"pred": pred, "question_idx": doc["question_index"], "target": doc["target"], "eval_answer": eval_answer, "gpt_prompt": content},
+    }
+
+
+def mix_evals_video2text_aggregate_submissions(results, args, task):
+    now_date_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+    submission_file_name = f"mix_evals_video2text_{task}-{now_date_time}.json"
+    path = file_utils.generate_submission_file(submission_file_name, args)
+    with open(path, "w") as f:
+        json.dump(results, f)
+    eval_logger.info(f"Submission file saved to {path}")
+
+
+def mix_evals_video2text_gpt_eval(results, args):
+    score = 0
+    for result in results:
+        eval_answer = result["eval_answer"]
+        eval_score = re.search(r"([0-9.]+)", eval_answer).group(1)
+        try:
+            eval_score = float(eval_score)
+        except Exception as e:
+            eval_logger.error(f"Error parsing eval_score: {e}")
+            eval_score = 0.0
+        score += eval_score
+
+    return score / len(results)
+
+
+# Factory into different aggregate
+def mix_evals_video2text_aggregate_gen(results, args):
+    mix_evals_video2text_aggregate_submissions(results, args, "OpenConvs")
+
+
+class MultiChoiceRegexFilter(ExtendedRegexFilter):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def apply(self, resps, docs):
+        filtered_resps = []
+
+        for r, doc in zip(resps, docs):
+            # Regex to directly extract the option letter from the model response
+            option_letter_regex = re.compile(r"\b([A-Z])\.\s+([^\n]*)")
+
+            # Process each response
+            filtered = []
+            for resp in r:
+                # Try to match the option letter at the start of the response
+                match = option_letter_regex.match(resp)
+                if match:
+                    # If a match is found, append the matched letter
+                    filtered.append(match.group(1))
+                else:
+                    # If no match, return the original response
+                    filtered.append(resp)
+
+            # Assuming we need the first response that matches or the original response
+            filtered_resps.append(filtered[0])
+
+        return filtered_resps
diff --git a/lmms_eval/tasks/mmupd/utils.py b/lmms_eval/tasks/mmupd/utils.py
index bfc724a5..818e8cb1 100644
--- a/lmms_eval/tasks/mmupd/utils.py
+++ b/lmms_eval/tasks/mmupd/utils.py
@@ -30,6 +30,9 @@
 elif API_TYPE == "azure":
     API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
     API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
+else:
+    API_URL = "YOUR_API_URL"
+    API_KEY = "YOUR_API_KEY"
 
 
 mmupd_evaluator = MMUPD_Evaluator(sys_prompt=config["metadata"]["sys_prompt"], API_KEY=API_KEY, API_URL=API_URL, model_version=GPT_EVAL_MODEL_NAME)
diff --git a/pyproject.toml b/pyproject.toml
index c65d3b8c..6e6ebe00 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -63,6 +63,7 @@ dependencies = [
     "decord",
     "zss",
     "pywsd",
+    "rouge",
 ]
 
 [tool.setuptools.packages.find]

From 8e27fd19eb573e44e75b2858ee6447743021191e Mon Sep 17 00:00:00 2001
From: Pu Fanyi <FPU001@e.ntu.edu.sg>
Date: Fri, 5 Jul 2024 20:47:58 -0700
Subject: [PATCH 14/32] [Small Typo] A small typo in LiveBench (#133)

* chore: Update lmms-eval to support video evaluations for LLaVA models

* lint
---
 lmms_eval/evaluator.py                     |  7 +----
 lmms_eval/models/longva.py                 |  2 +-
 lmms_eval/tasks/websrc/utils.py            |  8 ------
 lmms_eval/tasks/wild_vision_bench/utils.py | 30 ++++++++++------------
 4 files changed, 15 insertions(+), 32 deletions(-)

diff --git a/lmms_eval/evaluator.py b/lmms_eval/evaluator.py
index 6788467e..0e7d05c9 100755
--- a/lmms_eval/evaluator.py
+++ b/lmms_eval/evaluator.py
@@ -325,12 +325,7 @@ def evaluate(
             # hack: remove image columns to speed avoid loading images and speed up postprocessing
             # reason: doc_iterator will actually load image if it's in the doc.
             docs = task.test_docs() if task.has_test_docs() else task.validation_docs()
-            if "d170" not in task_name \
-                and "dc100" not in task_name \
-                and "dc200" not in task_name \
-                and "llava_wilder" not in task_name \
-                and "livebench" not in task_name \
-                and "wildvision" not in task_name:
+            if "d170" not in task_name and "dc100" not in task_name and "dc200" not in task_name and "llava_wilder" not in task_name and "live_bench" not in task_name and "wildvision" not in task_name:
                 remove_cols = []
                 features = docs.features
                 # If it is an Image instance or a Sequence of Image instance. Remove it
diff --git a/lmms_eval/models/longva.py b/lmms_eval/models/longva.py
index c599d344..c5bf6861 100644
--- a/lmms_eval/models/longva.py
+++ b/lmms_eval/models/longva.py
@@ -458,4 +458,4 @@ def _collate(x):
         res = re_ords.get_original(res)
 
         pbar.close()
-        return res
\ No newline at end of file
+        return res
diff --git a/lmms_eval/tasks/websrc/utils.py b/lmms_eval/tasks/websrc/utils.py
index 7286cd62..6e2584a4 100644
--- a/lmms_eval/tasks/websrc/utils.py
+++ b/lmms_eval/tasks/websrc/utils.py
@@ -63,15 +63,7 @@ def websrc_test_aggregate_results_for_submission(results, args):
         for result in results:
             out.update(result)
         json.dump(out, f, indent=4)
-<<<<<<< HEAD
-<<<<<<< HEAD
     eval_logger.info(f"Results saved to {path}.")
-=======
-    lmms_logger.info(f"Results saved to {path}.")
->>>>>>> internal_main_dev
-=======
-    eval_logger.info(f"Results saved to {path}.")
->>>>>>> internal_main_dev
 
 
 def websrc_aggregate_results(results):
diff --git a/lmms_eval/tasks/wild_vision_bench/utils.py b/lmms_eval/tasks/wild_vision_bench/utils.py
index bb426557..b7ac6c61 100644
--- a/lmms_eval/tasks/wild_vision_bench/utils.py
+++ b/lmms_eval/tasks/wild_vision_bench/utils.py
@@ -69,6 +69,7 @@
 
 prompt_template = "<|User Prompt|>\n{question_1}\n\n<|The Start of Assistant A's Answer|>\n{answer_1}\n<|The End of Assistant A's Answer|>\n\n<|The Start of Assistant B's Answer|>\n{answer_2}\n<|The End of Assistant B's Answer|>"
 
+
 def get_chat_response(base64_image, prompt, max_retries=5, wait_time=10):
     headers = {
         "Authorization": f"Bearer {API_KEY}",
@@ -83,13 +84,9 @@ def get_chat_response(base64_image, prompt, max_retries=5, wait_time=10):
                 "role": "user",
                 "content": [
                     {"type": "text", "text": prompt},
-                    {"type": "image_url",
-                        "image_url" : {
-                            "url" : f"data:image/jpeg;base64, {base64_image}"
-                            }
-                    },
+                    {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64, {base64_image}"}},
                 ],
-            }
+            },
         ],
         "max_tokens": 1024,
         "temperature": 0.0,
@@ -111,12 +108,12 @@ def get_chat_response(base64_image, prompt, max_retries=5, wait_time=10):
             return "", GPT_EVAL_MODEL_NAME
 
 
-
 def image_to_base64(pil_image):
     buffered = BytesIO()
     pil_image.save(buffered, format="PNG")
     return base64.b64encode(buffered.getvalue()).decode("utf-8")
 
+
 def get_score(judgement, pattern, pairwise=True):
     matches = pattern.findall(judgement)
     matches = [m for m in matches if m != ""]
@@ -129,8 +126,9 @@ def get_score(judgement, pattern, pairwise=True):
     else:
         return None, False
 
+
 def wild_vision_doc_to_visual(doc):
-    return [doc["image"].convert('RGB')]
+    return [doc["image"].convert("RGB")]
 
 
 def wild_vision_doc_to_text(doc, model_specific_prompt_kwargs=None):
@@ -141,6 +139,7 @@ def wild_vision_doc_to_text(doc, model_specific_prompt_kwargs=None):
         question = f"{question}{model_specific_prompt_kwargs['post_prompt']}"
     return question
 
+
 def wild_vision_doc_to_target(doc):
     return doc[BASELINE_MODEL_NAME]
 
@@ -151,18 +150,18 @@ def wild_vision_process_results(doc, results):
     base64_image = image_to_base64(doc["image"])
     resps, gpt_name = get_chat_response(base64_image, user_prompt)
     score, _ = get_score(resps, pattern=re.compile("\[\[([AB<>=]+)\]\]"))
-    
+
     if score is None:
         score = resps
-    
+
     if "A>B" in score:
         final_score = -1
-        judgement = "Worse" #Baseline better
+        judgement = "Worse"  # Baseline better
     elif "A>>B" in score:
         final_score = -2
         judgement = "Worse++"
     elif "A=B" in score:
-        final_score = 0 
+        final_score = 0
         judgement = "Tie"
     elif "B>A" in score:
         final_score = 1
@@ -174,15 +173,12 @@ def wild_vision_process_results(doc, results):
         final_score = 0
         judgement = "Unclear"
 
-
-    return {"gpt_eval_score" : {"question" : doc["instruction"], "score" : final_score, "gpt_resps" : resps, "ans_1" : doc[BASELINE_MODEL_NAME], "ans_2" : pred, "filtered_resps" : score, "judgement" : judgement}}
+    return {"gpt_eval_score": {"question": doc["instruction"], "score": final_score, "gpt_resps": resps, "ans_1": doc[BASELINE_MODEL_NAME], "ans_2": pred, "filtered_resps": score, "judgement": judgement}}
 
 
 def wild_vision_aggregation(results):
     score = 0
     for res in results:
         score += res["score"]
-    
-    return score / len(results)
-
 
+    return score / len(results)

From 41f831b639d4a5b7b3b920fadcc119bd649b4ccc Mon Sep 17 00:00:00 2001
From: Kaichen Zhang - NTU <kaichenzhang358@outlook.com>
Date: Sat, 6 Jul 2024 12:26:30 +0800
Subject: [PATCH 15/32] Add vibe eval (#134)

---
 lmms_eval/tasks/vibe_eval/utils.py       | 201 +++++++++++++++++++++++
 lmms_eval/tasks/vibe_eval/vibe_eval.yaml |  35 ++++
 2 files changed, 236 insertions(+)
 create mode 100644 lmms_eval/tasks/vibe_eval/utils.py
 create mode 100644 lmms_eval/tasks/vibe_eval/vibe_eval.yaml

diff --git a/lmms_eval/tasks/vibe_eval/utils.py b/lmms_eval/tasks/vibe_eval/utils.py
new file mode 100644
index 00000000..5a64cce6
--- /dev/null
+++ b/lmms_eval/tasks/vibe_eval/utils.py
@@ -0,0 +1,201 @@
+from enum import Enum
+from dataclasses import dataclass
+from typing import Optional, List
+from pathlib import Path
+import yaml
+
+from reka import ChatMessage
+from reka.client import Reka
+
+import re
+import os
+from copy import deepcopy
+
+REKA_API_KEY = os.getenv("REKA_API_KEY", "YOUR_API_KEY")
+
+with open(Path(__file__).parent / "vibe_eval.yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+
+    config = yaml.safe_load("".join(safe_data))
+
+EVALUATOR_NAME = config["metadata"]["evaluator"]
+
+_PROMPT_WITH_IMAGE = """\
+[Question]
+{prompt}
+
+[Assistant Response]
+{generation}
+
+[Ground Truth Response]
+{reference}
+
+[System]
+Rate whether the assistant response correctly matches the ground truth, in regards to the image above.
+The rating should be 1-5, where 1 is incorrect and 5 is correct.
+Your response should be in the format:
+Explanation: (your explanation)
+Rating: (int)"""
+
+_PROMPT_WITH_NO_IMAGE = """\
+[Question]
+{prompt}
+
+[Assistant Response]
+{generation}
+
+[Ground Truth Response]
+{reference}
+
+[System]
+Rate whether the assistant response correctly matches the ground truth, it's about an image shared by the user.
+The rating should be 1-5, where 1 is incorrect and 5 is correct.
+Your response should be in the format:
+Explanation: (your explanation)
+Rating: (int)"""
+
+@dataclass
+class Example:
+    """An example loaded from vibe-eval, stored as jsonl in the repo."""
+
+    example_id: str
+    category: str
+    prompt: str
+    reference: str
+    media_filename: str
+    media_url: str
+
+    # The fields below are not stored in the dataset, but are populated by this script.
+    generation: Optional[str] = None
+    score: Optional[int] = None
+    evaluator_explanation: Optional[str] = None
+
+class Evaluator(Enum):
+    # Use Reka Core (including image input).
+    REKA_CORE = "reka-core"
+
+    # Use Reka Core, only using text input.
+    REKA_CORE_TEXT = "reka-core-text"
+
+def make_evaluator_prompt(example: Example, include_image: bool) -> str:
+    return (_PROMPT_WITH_IMAGE if include_image else _PROMPT_WITH_NO_IMAGE).format(
+        prompt=example.prompt,
+        reference=example.reference,
+        generation=example.generation,
+    )
+
+def evaluate(example: Example, evaluator: Evaluator) -> Example:
+    """Evaluates the generation and populates the score and explanation fields."""
+    include_image = evaluator == Evaluator.REKA_CORE
+    evaluator_prompt = make_evaluator_prompt(example, include_image=include_image)
+    client = Reka(api_key=REKA_API_KEY)
+    content = [
+        {"type": "text", "text": evaluator_prompt},
+    ]
+    if include_image:
+        content.append({"type": "image_url", "image_url": example.media_url})
+    evaluator_response = client.chat.create(
+        messages=[
+            ChatMessage(
+                content=content,
+                role="user",
+            )
+        ],
+        model="reka-core",
+        temperature=0.4,
+        max_tokens=1024,
+    )
+    evaluator_response = evaluator_response.responses[0].message.content
+    # evaluator_response = reka.chat(
+        # human=evaluator_prompt,
+        # media_url=example.media_url if include_image else None,
+        # temperature=0.4,
+        # model_name="reka-core-20240415",
+        # request_output_len=1024,
+    # )["text"]
+    re_match = re.search(r"Rating:\s*([1-5])", evaluator_response)
+    if re_match is None:
+        example.score = 0
+        example.evaluator_explanation = evaluator_response
+        return example
+    example.score = int(re_match.group(1))
+    example.evaluator_explanation = evaluator_response
+    return example
+
+def vibe_doc_to_visual(doc):
+    return [doc["image"].convert("RGB")]
+
+def vibe_doc_to_text(doc, model_specific_prompt_kwargs=None):
+    question = doc["prompt"].strip()
+    if "pre_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["pre_prompt"] != "":
+        question = f"{model_specific_prompt_kwargs['pre_prompt']}{question}"
+    if "post_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["post_prompt"] != "":
+        question = f"{question}{model_specific_prompt_kwargs['post_prompt']}"
+    return question
+
+def vibe_process_results(doc, results):
+    example_id = doc["example_id"]
+    category = doc["category"]
+    prompt = doc["prompt"]
+    reference = doc["reference"]
+    media_filename = doc["media_url"]
+    media_url = doc["media_url"]
+    generation = results[0]
+    example = Example(
+        example_id=example_id,
+        category=category,
+        prompt=prompt,
+        reference=reference,
+        media_filename=media_filename,
+        media_url=media_url,
+        generation=generation
+    )
+
+    evaluator = Evaluator.REKA_CORE if EVALUATOR_NAME == "reka-core" else Evaluator.REKA_CORE_TEXT 
+    
+    example = evaluate(example, evaluator=evaluator)
+    data_dict = {
+        "score" : example.score,
+        "evaluator_explanation" : example.evaluator_explanation,
+        "prompt" : example.prompt,
+        "generation" : example.generation,
+        "media_url" : example.media_url,
+        "category" : example.category,
+    }
+
+    return {
+        "hard" : deepcopy(data_dict),
+        "normal" : deepcopy(data_dict),
+        "all" : deepcopy(data_dict),
+    }
+
+def _mean(scores: List[int]) -> float:
+    """Scale from 1-5 to 0-100 and compute means."""
+    return sum(25 * (score - 1) for score in scores) / len(scores)
+
+
+def vibe_aggregation_results(results, category):
+    score = []
+    for res in results:
+        if category in res["category"] or category == "all":
+            score.append(res["score"])
+    
+    aggregate_scores = _mean(score)
+    return aggregate_scores
+
+
+def vibe_aggregation_results_normal(results):
+    return vibe_aggregation_results(results, "normal")
+
+
+def vibe_aggregation_results_hard(results):
+    return vibe_aggregation_results(results, "hard")
+
+
+def vibe_aggregation_results_all(results):
+    return vibe_aggregation_results(results, "all")
\ No newline at end of file
diff --git a/lmms_eval/tasks/vibe_eval/vibe_eval.yaml b/lmms_eval/tasks/vibe_eval/vibe_eval.yaml
new file mode 100644
index 00000000..ed7f31c6
--- /dev/null
+++ b/lmms_eval/tasks/vibe_eval/vibe_eval.yaml
@@ -0,0 +1,35 @@
+dataset_path: RekaAI/VibeEval
+dataset_kwargs:
+  token: True
+task: "vibe_eval"
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.vibe_doc_to_visual
+doc_to_text: !function utils.vibe_doc_to_text
+doc_to_target: "reference"
+generation_kwargs:
+  max_new_tokens: 1024
+  temperature: 0
+  top_p: 1.0
+  num_beams: 1
+  do_sample: false
+# The return value of process_results will be used by metrics
+process_results: !function utils.vibe_process_results
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: hard
+    aggregation: !function utils.vibe_aggregation_results_hard
+    higher_is_better: true
+  - metric: normal
+    aggregation: !function utils.vibe_aggregation_results_normal
+    higher_is_better: true
+  - metric: all
+    aggregation: !function utils.vibe_aggregation_results_all
+    higher_is_better: true
+metadata:
+  evaluator: "reka-core-text"
+
+model_specific_prompt_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: ""
\ No newline at end of file

From b838e64691a511f54823c550c510d2cd7b256a2a Mon Sep 17 00:00:00 2001
From: Pu Fanyi <FPU001@e.ntu.edu.sg>
Date: Sat, 6 Jul 2024 17:50:14 -0700
Subject: [PATCH 16/32] InternVL2 (#135)

* internvl2

* fix some bugs

* fix

* lint
---
 lmms_eval/models/__init__.py       |   1 +
 lmms_eval/models/internvl2.py      | 236 +++++++++++++++++++++++++++++
 lmms_eval/tasks/vibe_eval/utils.py |  54 +++----
 3 files changed, 264 insertions(+), 27 deletions(-)
 create mode 100644 lmms_eval/models/internvl2.py

diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py
index 17100e95..46fd92e8 100755
--- a/lmms_eval/models/__init__.py
+++ b/lmms_eval/models/__init__.py
@@ -25,6 +25,7 @@
     "llava_sglang": "LlavaSglang",
     "idefics2": "Idefics2",
     "internvl": "InternVLChat",
+    "internvl2": "InternVL2",
     "gemini_api": "GeminiAPI",
     "reka": "Reka",
     "from_log": "FromLog",
diff --git a/lmms_eval/models/internvl2.py b/lmms_eval/models/internvl2.py
new file mode 100644
index 00000000..2763e935
--- /dev/null
+++ b/lmms_eval/models/internvl2.py
@@ -0,0 +1,236 @@
+from typing import List, Tuple
+from lmms_eval.api.instance import Instance
+from decord import VideoReader, cpu
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+import numpy as np
+from transformers import AutoModel, AutoTokenizer
+from lmms_eval.api.registry import register_model
+from accelerate import Accelerator, DistributedType
+from lmms_eval.api.model import lmms
+from tqdm import tqdm
+import logging
+
+eval_logger = logging.getLogger("eval_logger")
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+DEFAULT_GEN_KWARGS = dict(
+    num_beams=1,
+    max_new_tokens=1024,
+    do_sample=False,
+)
+
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img), T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=MEAN, std=STD)])
+    return transform
+
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float("inf")
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+
+def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set((i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = ((i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size)
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+
+def load_image(image, input_size=448, max_num=6):
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+
+def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
+    if bound:
+        start, end = bound[0], bound[1]
+    else:
+        start, end = -100000, 100000
+    start_idx = max(first_idx, round(start * fps))
+    end_idx = min(round(end * fps), max_frame)
+    seg_size = float(end_idx - start_idx) / num_segments
+    frame_indices = np.array([int(start_idx + (seg_size / 2) + np.round(seg_size * idx)) for idx in range(num_segments)])
+    return frame_indices
+
+
+def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32):
+    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
+    max_frame = len(vr) - 1
+    fps = float(vr.get_avg_fps())
+
+    pixel_values_list, num_patches_list = [], []
+    transform = build_transform(input_size=input_size)
+    frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
+    for frame_index in frame_indices:
+        img = Image.fromarray(vr[frame_index].asnumpy()).convert("RGB")
+        img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
+        pixel_values = [transform(tile) for tile in img]
+        pixel_values = torch.stack(pixel_values)
+        num_patches_list.append(pixel_values.shape[0])
+        pixel_values_list.append(pixel_values)
+    pixel_values = torch.cat(pixel_values_list)
+    return pixel_values, num_patches_list
+
+
+from datetime import timedelta
+from accelerate.state import AcceleratorState
+from accelerate.utils import InitProcessGroupKwargs
+
+
+@register_model("internvl2")
+class InternVL2(lmms):
+    def __init__(
+        self,
+        pretrained: str = "OpenGVLab/InternVL2-2B",
+        modality: str = "image",
+        device: str = "cuda:0",
+        device_map: str = "cuda:0",
+        batch_size: str = "1",
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.path = pretrained
+        self.model = AutoModel.from_pretrained(self.path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True).eval().cuda()
+        self.tokenizer = AutoTokenizer.from_pretrained(self.path, trust_remote_code=True)
+
+        batch_size = int(batch_size)
+        assert batch_size == 1, f"Batch size should be 1 for InternVL2, but got {batch_size}."
+        self.batch_size_per_gpu = batch_size
+
+        accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52))
+        accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs])
+        if accelerator.num_processes > 1:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            self._device = torch.device(device)
+            self.device_map = device_map
+        else:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+
+        if accelerator.num_processes > 1:
+            assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
+            # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
+            # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works
+            # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work.
+            if accelerator.distributed_type == DistributedType.DEEPSPEED:
+                kwargs = {
+                    "train_micro_batch_size_per_gpu": self.batch_size_per_gpu,
+                    "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes,
+                }
+                AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs)
+                eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0")
+
+            if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED:
+                self._model = accelerator.prepare(self.model)
+            else:
+                self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
+            self.accelerator = accelerator
+            if self.accelerator.is_local_main_process:
+                eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
+            self._rank = self.accelerator.local_process_index
+            self._world_size = self.accelerator.num_processes
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism")
+            self._rank = 0
+            self._word_size = 1
+        else:
+            eval_logger.info(f"Using single device: {self._device}")
+            self.model.to(self._device)
+            self._rank = 0
+            self._world_size = 1
+
+        self.device = self._device
+        self.modality = modality
+
+    def flatten(self, input):
+        new_list = []
+        for i in input:
+            for j in i:
+                new_list.append(j)
+        return new_list
+
+    def generate_until(self, requests) -> List[str]:
+        res = []
+        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
+
+        for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
+            if "until" in gen_kwargs:
+                gen_kwargs.pop("until")
+            for k, v in DEFAULT_GEN_KWARGS.items():
+                if k not in gen_kwargs:
+                    gen_kwargs[k] = v
+
+            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
+            visuals = self.flatten(visuals)
+            if self.modality == "image":
+                visuals = [load_image(visual).to(torch.bfloat16).cuda() for visual in visuals]
+                pixel_values = torch.cat(visuals, dim=0)
+                num_patches_list = [visual.size(0) for visual in visuals]
+                if visuals:
+                    image_tokens = ["<image>"] * len(visuals)
+                    image_tokens = " ".join(image_tokens)
+                    contexts = image_tokens + "\n" + contexts
+                response, history = self.model.chat(self.tokenizer, pixel_values, contexts, gen_kwargs, num_patches_list=num_patches_list, history=None, return_history=True)
+            elif self.modality == "video":
+                assert len(visuals) == 1, f"Only one video is supported, but got {len(visuals)} videos."
+                video_path = visuals[0]
+                pixel_values, num_patches_list = load_video(video_path, num_segments=8, max_num=1)
+                pixel_values = pixel_values.to(torch.bfloat16).cuda()
+                video_prefix = "".join([f"Frame{i+1}: <image>\n" for i in range(len(num_patches_list))])
+                question = video_prefix + contexts
+                response, history = self.model.chat(self.tokenizer, pixel_values, question, gen_kwargs, num_patches_list=num_patches_list, history=None, return_history=True)
+            res.append(response)
+            pbar.update(1)
+        pbar.close()
+        return res
+
+    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        assert False, "Not implemented yet."
diff --git a/lmms_eval/tasks/vibe_eval/utils.py b/lmms_eval/tasks/vibe_eval/utils.py
index 5a64cce6..9e3b3c56 100644
--- a/lmms_eval/tasks/vibe_eval/utils.py
+++ b/lmms_eval/tasks/vibe_eval/utils.py
@@ -59,6 +59,7 @@
 Explanation: (your explanation)
 Rating: (int)"""
 
+
 @dataclass
 class Example:
     """An example loaded from vibe-eval, stored as jsonl in the repo."""
@@ -75,6 +76,7 @@ class Example:
     score: Optional[int] = None
     evaluator_explanation: Optional[str] = None
 
+
 class Evaluator(Enum):
     # Use Reka Core (including image input).
     REKA_CORE = "reka-core"
@@ -82,6 +84,7 @@ class Evaluator(Enum):
     # Use Reka Core, only using text input.
     REKA_CORE_TEXT = "reka-core-text"
 
+
 def make_evaluator_prompt(example: Example, include_image: bool) -> str:
     return (_PROMPT_WITH_IMAGE if include_image else _PROMPT_WITH_NO_IMAGE).format(
         prompt=example.prompt,
@@ -89,6 +92,7 @@ def make_evaluator_prompt(example: Example, include_image: bool) -> str:
         generation=example.generation,
     )
 
+
 def evaluate(example: Example, evaluator: Evaluator) -> Example:
     """Evaluates the generation and populates the score and explanation fields."""
     include_image = evaluator == Evaluator.REKA_CORE
@@ -112,11 +116,11 @@ def evaluate(example: Example, evaluator: Evaluator) -> Example:
     )
     evaluator_response = evaluator_response.responses[0].message.content
     # evaluator_response = reka.chat(
-        # human=evaluator_prompt,
-        # media_url=example.media_url if include_image else None,
-        # temperature=0.4,
-        # model_name="reka-core-20240415",
-        # request_output_len=1024,
+    # human=evaluator_prompt,
+    # media_url=example.media_url if include_image else None,
+    # temperature=0.4,
+    # model_name="reka-core-20240415",
+    # request_output_len=1024,
     # )["text"]
     re_match = re.search(r"Rating:\s*([1-5])", evaluator_response)
     if re_match is None:
@@ -127,9 +131,11 @@ def evaluate(example: Example, evaluator: Evaluator) -> Example:
     example.evaluator_explanation = evaluator_response
     return example
 
+
 def vibe_doc_to_visual(doc):
     return [doc["image"].convert("RGB")]
 
+
 def vibe_doc_to_text(doc, model_specific_prompt_kwargs=None):
     question = doc["prompt"].strip()
     if "pre_prompt" in model_specific_prompt_kwargs and model_specific_prompt_kwargs["pre_prompt"] != "":
@@ -138,6 +144,7 @@ def vibe_doc_to_text(doc, model_specific_prompt_kwargs=None):
         question = f"{question}{model_specific_prompt_kwargs['post_prompt']}"
     return question
 
+
 def vibe_process_results(doc, results):
     example_id = doc["example_id"]
     category = doc["category"]
@@ -146,34 +153,27 @@ def vibe_process_results(doc, results):
     media_filename = doc["media_url"]
     media_url = doc["media_url"]
     generation = results[0]
-    example = Example(
-        example_id=example_id,
-        category=category,
-        prompt=prompt,
-        reference=reference,
-        media_filename=media_filename,
-        media_url=media_url,
-        generation=generation
-    )
+    example = Example(example_id=example_id, category=category, prompt=prompt, reference=reference, media_filename=media_filename, media_url=media_url, generation=generation)
+
+    evaluator = Evaluator.REKA_CORE if EVALUATOR_NAME == "reka-core" else Evaluator.REKA_CORE_TEXT
 
-    evaluator = Evaluator.REKA_CORE if EVALUATOR_NAME == "reka-core" else Evaluator.REKA_CORE_TEXT 
-    
     example = evaluate(example, evaluator=evaluator)
     data_dict = {
-        "score" : example.score,
-        "evaluator_explanation" : example.evaluator_explanation,
-        "prompt" : example.prompt,
-        "generation" : example.generation,
-        "media_url" : example.media_url,
-        "category" : example.category,
+        "score": example.score,
+        "evaluator_explanation": example.evaluator_explanation,
+        "prompt": example.prompt,
+        "generation": example.generation,
+        "media_url": example.media_url,
+        "category": example.category,
     }
 
     return {
-        "hard" : deepcopy(data_dict),
-        "normal" : deepcopy(data_dict),
-        "all" : deepcopy(data_dict),
+        "hard": deepcopy(data_dict),
+        "normal": deepcopy(data_dict),
+        "all": deepcopy(data_dict),
     }
 
+
 def _mean(scores: List[int]) -> float:
     """Scale from 1-5 to 0-100 and compute means."""
     return sum(25 * (score - 1) for score in scores) / len(scores)
@@ -184,7 +184,7 @@ def vibe_aggregation_results(results, category):
     for res in results:
         if category in res["category"] or category == "all":
             score.append(res["score"])
-    
+
     aggregate_scores = _mean(score)
     return aggregate_scores
 
@@ -198,4 +198,4 @@ def vibe_aggregation_results_hard(results):
 
 
 def vibe_aggregation_results_all(results):
-    return vibe_aggregation_results(results, "all")
\ No newline at end of file
+    return vibe_aggregation_results(results, "all")

From b9ce5c0b24b9797527cbdab28bcd5bae71c142f6 Mon Sep 17 00:00:00 2001
From: Kaichen Zhang - NTU <kaichenzhang358@outlook.com>
Date: Sun, 7 Jul 2024 12:41:41 +0800
Subject: [PATCH 17/32] Update videomme_w_subtitle.yaml

---
 lmms_eval/tasks/videomme/videomme_w_subtitle.yaml | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml
index b7724748..cfa5f070 100644
--- a/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml
+++ b/lmms_eval/tasks/videomme/videomme_w_subtitle.yaml
@@ -26,11 +26,8 @@ metric_list:
 model_specific_prompt_kwargs:
   default:
     frame_num: 32
-<<<<<<< HEAD
     pre_prompt: ""
     post_prompt: "\nAnswer the question using a single word or phrase."
-=======
->>>>>>> internal_main_dev
   gemini_api:
     gemini_api_flag: "full subtitle"
   # gpt4v:

From a4d59de22a956ab4af6f4584527a9812ce43bb0f Mon Sep 17 00:00:00 2001
From: kcz358 <kaichenzhang358@outlook.com>
Date: Mon, 8 Jul 2024 02:39:06 +0000
Subject: [PATCH 18/32] Fix gen kwargs image aspect ration in internvl2

---
 lmms_eval/models/internvl2.py                               | 1 +
 lmms_eval/models/{xcomposer2_4khd.py => xcomposer2_4KHD.py} | 0
 2 files changed, 1 insertion(+)
 rename lmms_eval/models/{xcomposer2_4khd.py => xcomposer2_4KHD.py} (100%)

diff --git a/lmms_eval/models/internvl2.py b/lmms_eval/models/internvl2.py
index 2763e935..1f5a0185 100644
--- a/lmms_eval/models/internvl2.py
+++ b/lmms_eval/models/internvl2.py
@@ -204,6 +204,7 @@ def generate_until(self, requests) -> List[str]:
         for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
             if "until" in gen_kwargs:
                 gen_kwargs.pop("until")
+            image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio", None)
             for k, v in DEFAULT_GEN_KWARGS.items():
                 if k not in gen_kwargs:
                     gen_kwargs[k] = v
diff --git a/lmms_eval/models/xcomposer2_4khd.py b/lmms_eval/models/xcomposer2_4KHD.py
similarity index 100%
rename from lmms_eval/models/xcomposer2_4khd.py
rename to lmms_eval/models/xcomposer2_4KHD.py

From 266bc700231138940d708d64e295a63fb5624063 Mon Sep 17 00:00:00 2001
From: kcz358 <kaichenzhang358@outlook.com>
Date: Mon, 8 Jul 2024 02:39:28 +0000
Subject: [PATCH 19/32] Fix llava_wilder bug

---
 lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml | 6 +++---
 lmms_eval/tasks/llava_wilder/utils.py                | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml b/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml
index 1c493673..1d112ee2 100644
--- a/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml
+++ b/lmms_eval/tasks/llava_wilder/llava_wilder_small.yaml
@@ -1,8 +1,8 @@
-dataset_path: lmms-lab/llava-bench-wilder
+dataset_path: lmms-lab/LLaVA-Bench-Wilder
 dataset_kwargs:
   token: True
 task: "llava_wilder_small"
-test_split: small
+test_split: test 
 model_specific_prompt_kwargs:
   default:
     pre_prompt: ""
@@ -10,4 +10,4 @@ model_specific_prompt_kwargs:
   xcomposer2_4khd:
     pre_prompt: "[UNUSED_TOKEN_146]user\nQuestion: "
     post_prompt: "[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
-include: _default_template_wilder_yaml
\ No newline at end of file
+include: _default_template_wilder_yaml
diff --git a/lmms_eval/tasks/llava_wilder/utils.py b/lmms_eval/tasks/llava_wilder/utils.py
index 96a020bc..04bb8273 100644
--- a/lmms_eval/tasks/llava_wilder/utils.py
+++ b/lmms_eval/tasks/llava_wilder/utils.py
@@ -119,8 +119,8 @@ def llava_process_results(doc, result):
         a dictionary with key: metric name (in this case coco_bleu), value: metric value
     """
     try:
-        question = doc.get("question", "")
-        ans1 = doc.get("answer", "")
+        question = doc.get("Question", "")
+        ans1 = doc.get("Answer", "")
         ans2 = result[0] if result else ""
         content = f"[Question]\n{question}\n\n" + f"[Assistant 1]\n{ans1}\n\n[End of Assistant 1]\n\n" + f"[Assistant 2]\n{ans2}\n\n[End of Assistant 2]\n\n" f"[System]\n{judge_rules}\n\n"
         visuals = llava_doc_to_visual(doc)
@@ -148,7 +148,7 @@ def llava_doc_to_text(doc, model_specific_prompt_kwargs=None):
         model_specific_prompt_kwargs = {}
     pre_prompt = model_specific_prompt_kwargs.get("pre_prompt", "")
     post_prompt = model_specific_prompt_kwargs.get("post_prompt", "")
-    return f"{pre_prompt}{doc['question']}{post_prompt}"
+    return f"{pre_prompt}{doc['Question']}{post_prompt}"
 
 
 def llava_all_aggregation(results):

From 6b7f253b24711ce50b710ccec15172fbace03f4f Mon Sep 17 00:00:00 2001
From: Li Bo <drluodian@gmail.com>
Date: Mon, 8 Jul 2024 10:47:35 +0800
Subject: [PATCH 20/32] Dev/interleave bench (#136)

* feat: Update LMMS evaluation configuration and models

- Update `activitynetqa_generation.yaml` to remove `dataset_name` field and update `task` field to "activitynetqa"
- Update `utils.py` to add default values for `API_URL` and `API_KEY` when `API_TYPE` is not "openai" or "azure"
- Update `batch_gpt4.py` and `gpt4v.py` to rename `max_frames_for_video` parameter to `max_frames_num`
- Update `reka.py` to rename `max_frames_for_video` parameter to `max_frames_num` and add support for `continual_mode` with a persistent response cache

This commit updates the LMMS evaluation configuration and models to improve compatibility and add new features.

* Update LMMS evaluation configuration and models

* Update LMMS evaluation configuration and models

* feat: Update LMMS evaluation configuration and models

- Update `activitynetqa_generation.yaml` to remove `dataset_name` field and update `task` field to "activitynetqa"
- Update `utils.py` to add default values for `API_URL` and `API_KEY` when `API_TYPE` is not "openai" or "azure"
- Update `batch_gpt4.py` and `gpt4v.py` to rename `max_frames_for_video` parameter to `max_frames_num`
- Update `reka.py` to rename `max_frames_for_video` parameter to `max_frames_num` and add support for `continual_mode` with a persistent response cache

This commit updates the LMMS evaluation configuration and models to improve compatibility and add new features.

* Refactor error handling in GPT4V model evaluation

* Refactor error handling in GPT4V model evaluation

* Refactor video decoding backend to use "decord" instead of "pyav"

* Refactor image aspect ratio handling in Llava_OneVision model

* Refactor GPT4V model to fix bug in visuals encoding

* add exception for azure gpt

* feat: fix bugs

* feat: update

* Refactor image aspect ratio handling in Llava_OneVision model

* Refactor image aspect ratio handling in Llava_OneVision model

* update interleave bench
---
 lmms_eval/models/llava.py                     |   2 +-
 lmms_eval/models/llava_onevision.py           |  95 ++--
 lmms_eval/models/xcomposer2_4KHD.py           | 294 -----------
 .../llava_interleave_bench/in_domain.yaml     |   9 +-
 .../interleave_bench.yaml                     |   5 +
 .../multi_view_in_domain.yaml                 |  15 +-
 .../llava_interleave_bench/out_of_domain.yaml |   9 +-
 .../tasks/llava_interleave_bench/utils.py     | 456 ++++++++++--------
 lmms_eval/utils.py                            |   9 +
 9 files changed, 329 insertions(+), 565 deletions(-)
 delete mode 100644 lmms_eval/models/xcomposer2_4KHD.py
 create mode 100644 lmms_eval/tasks/llava_interleave_bench/interleave_bench.yaml

diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py
index 6de4c8f8..7528e356 100755
--- a/lmms_eval/models/llava.py
+++ b/lmms_eval/models/llava.py
@@ -97,7 +97,7 @@ def __init__(
             self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args)
         self._config = self._model.config
         self.model.eval()
-        self.model.tie_weights()
+
         self.truncation = truncation
         self.batch_size_per_gpu = int(batch_size)
         self.conv_template = conv_template
diff --git a/lmms_eval/models/llava_onevision.py b/lmms_eval/models/llava_onevision.py
index 5cfbb538..3f27c803 100644
--- a/lmms_eval/models/llava_onevision.py
+++ b/lmms_eval/models/llava_onevision.py
@@ -1,29 +1,23 @@
-from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
-from accelerate.state import AcceleratorState
-from transformers import AutoConfig
-
 import math
-import torch
-import transformers
 import re
-
-torch.backends.cuda.matmul.allow_tf32 = True
-
-from tqdm import tqdm
-from datetime import timedelta
-from decord import VideoReader, cpu
-import numpy as np
-
 import copy
-import PIL
-from typing import List, Optional, Union, Tuple
-from packaging import version
-import warnings
+import json
 import logging
+import warnings
+from datetime import timedelta
+from typing import List, Optional, Union, Tuple
+import PIL
 
-warnings.filterwarnings("ignore")
+import numpy as np
+import torch
+import transformers
+from tqdm import tqdm
+from packaging import version
+from decord import VideoReader, cpu
 
-eval_logger = logging.getLogger("lmms-eval")
+from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
+from accelerate.state import AcceleratorState
+from transformers import AutoConfig
 
 from lmms_eval import utils
 from lmms_eval.api.instance import Instance
@@ -31,27 +25,46 @@
 from lmms_eval.api.registry import register_model
 from lmms_eval.models.model_utils.load_video import read_video_pyav
 
+# Suppress warnings
+warnings.filterwarnings("ignore")
+
+# Configure logging
+eval_logger = logging.getLogger("lmms-eval")
+
+# Enable TF32 for CUDA
+torch.backends.cuda.matmul.allow_tf32 = True
+
+# Import LLaVA modules
 try:
     from llava.model.builder import load_pretrained_model
-    from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token, KeywordsStoppingCriteria
-    from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+    from llava.mm_utils import (
+        get_model_name_from_path,
+        process_images,
+        tokenizer_image_token,
+        KeywordsStoppingCriteria,
+    )
+    from llava.constants import (
+        IMAGE_TOKEN_INDEX,
+        DEFAULT_IMAGE_TOKEN,
+        DEFAULT_IM_START_TOKEN,
+        DEFAULT_IM_END_TOKEN,
+        IGNORE_INDEX,
+    )
     from llava.conversation import conv_templates, SeparatorStyle
+except ImportError as e:
+    eval_logger.debug(f"LLaVA is not installed. Please install LLaVA to use this model.\nError: {e}")
 
-except Exception as e:
-    eval_logger.debug("LLaVA is not installed. Please install LLaVA to use this model.\nError: %s" % e)
-
+# Import LLaVA-vid modules
 try:
     from llavavid.model.language_model.llava_qwen import LlavaQwenConfig
     from llavavid.model.language_model.llava_llama import LlavaConfig
 
     AutoConfig.register("llava_qwen", LlavaQwenConfig)
     AutoConfig.register("llava_llama", LlavaConfig)
-except Exception as e:
-    eval_logger.debug("")
-# inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5
-# if is_flash_attn_2_available:
-#     best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating
+except ImportError as e:
+    eval_logger.debug(f"LLaVA-vid is not installed. Error: {e}")
 
+# Determine best attention implementation
 if version.parse(torch.__version__) >= version.parse("2.1.2"):
     best_fit_attn_implementation = "sdpa"
 else:
@@ -146,7 +159,6 @@ def __init__(
 
         self._config = self._model.config
         self.model.eval()
-        self.model.tie_weights()
         self.truncation = truncation
         self.batch_size_per_gpu = int(batch_size)
         self.conv_template = conv_template
@@ -484,10 +496,23 @@ def _collate(x):
                     conv = copy.deepcopy(conv_templates[self.conv_template])
                 else:
                     conv = conv_templates[self.conv_template].copy()
-                conv.append_message(conv.roles[0], question)
-                conv.append_message(conv.roles[1], None)
-                prompt_question = conv.get_prompt()
-                question_input.append(prompt_question)
+
+                if utils.is_json(question):  # conversational question input
+                    question = json.loads(question)
+                    for idx, item in enumerate(question):
+                        role = conv.roles[idx % 2]
+                        message = item["value"]
+                        conv.append_message(role, message)
+
+                    assert len(conv.messages) % 2 == 1
+                    conv.append_message(conv.roles[1], None)
+                    prompt_question = conv.get_prompt()
+                    question_input.append(prompt_question)
+                else:  # only simple string for question
+                    conv.append_message(conv.roles[0], question)
+                    conv.append_message(conv.roles[1], None)
+                    prompt_question = conv.get_prompt()
+                    question_input.append(prompt_question)
 
             # preconfigure gen_kwargs with defaults
             if "max_new_tokens" not in gen_kwargs:
diff --git a/lmms_eval/models/xcomposer2_4KHD.py b/lmms_eval/models/xcomposer2_4KHD.py
deleted file mode 100644
index e741f637..00000000
--- a/lmms_eval/models/xcomposer2_4KHD.py
+++ /dev/null
@@ -1,294 +0,0 @@
-from multiprocessing import context
-import torch
-from transformers import AutoModel, AutoTokenizer
-from PIL import Image
-import numpy as np
-import torchvision.transforms as transforms
-from datetime import timedelta
-
-from lmms_eval import utils
-from lmms_eval.api.instance import Instance
-from lmms_eval.api.model import lmms
-from lmms_eval.api.registry import register_model
-from lmms_eval.utils import stop_sequences_criteria
-
-from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
-from accelerate.state import AcceleratorState
-
-from typing import Optional, Sequence, List, Tuple, Union
-import re
-from tqdm import tqdm
-
-pattern = re.compile(r"[A-Z]")
-
-from loguru import logger as eval_logger
-
-meta_instruction = """You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔).
-- InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed\
- by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
-- InternLM-XComposer (浦语·灵笔) can understand and communicate fluently in the language chosen by\
- the user such as English and 中文.
-- InternLM-XComposer (浦语·灵笔) is capable of comprehending and articulating responses\
- effectively based on the provided image."""
-
-
-@register_model("xcomposer2_4khd")
-class XComposer2_4KHD(lmms):
-    def __init__(
-        self,
-        pretrained: str = "internlm/internlm-xcomposer2-4khd-7b",
-        device: Optional[str] = "cuda:0",
-        batch_size: Optional[Union[int, str]] = 1,
-        device_map="cuda:0",
-        need_bos: bool = True,
-        padding: bool = False,
-        half: bool = False,
-        **kwargs,
-    ) -> None:
-        super().__init__()
-
-        accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52))
-        accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs])
-        if accelerator.num_processes > 1:
-            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
-            self.device_map = f"cuda:{accelerator.local_process_index}"
-        elif accelerator.num_processes == 1 and device_map == "auto":
-            self._device = torch.device(device)
-            self.device_map = device_map
-        else:
-            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
-            self.device_map = f"cuda:{accelerator.local_process_index}"
-
-        self.pretrained = pretrained
-        self.need_bos = need_bos
-        self.padding = padding
-        self._model = AutoModel.from_pretrained(self.pretrained, device_map=self.device_map, trust_remote_code=True)
-        self._tokenizer = AutoTokenizer.from_pretrained(self.pretrained, trust_remote_code=True)
-        self.model.tokenizer = self.tokenizer
-        self.batch_size_per_gpu = batch_size
-
-        if accelerator.num_processes > 1:
-            assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
-            # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
-            # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works
-            # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work.
-            if accelerator.distributed_type == DistributedType.DEEPSPEED:
-                kwargs = {
-                    "train_micro_batch_size_per_gpu": self.batch_size_per_gpu,
-                    "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes,
-                }
-                AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs)
-                eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0")
-            if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED:
-                self._model = accelerator.prepare(self.model)
-            else:
-                self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
-            self.accelerator = accelerator
-            if self.accelerator.is_local_main_process:
-                eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
-            self._rank = self.accelerator.local_process_index
-            self._world_size = self.accelerator.num_processes
-        elif accelerator.num_processes == 1 and device_map == "auto":
-            eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism")
-            self._rank = 0
-            self._word_size = 1
-        else:
-            eval_logger.info(f"Using single device: {self._device}")
-            self.model.to(self._device)
-            self._rank = 0
-            self._world_size = 1
-
-    @property
-    def config(self):
-        # return the associated transformers.AutoConfig for the given pretrained model.
-        return self._config
-
-    @property
-    def tokenizer(self):
-        return self._tokenizer
-
-    @property
-    def model(self):
-        # returns the model, unwrapping it if using Accelerate
-        if hasattr(self, "accelerator"):
-            return self.accelerator.unwrap_model(self._model)
-        else:
-            return self._model
-
-    @property
-    def batch_size(self):
-        return self.batch_size_per_gpu
-
-    @property
-    def device(self):
-        return self._device
-
-    @property
-    def rank(self):
-        return self._rank
-
-    @property
-    def world_size(self):
-        return self._world_size
-
-    def flatten(self, input):
-        new_list = []
-        for i in input:
-            for j in i:
-                new_list.append(j)
-        return new_list
-
-    def generate_until(self, requests) -> List[str]:
-        res = []
-        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
-
-        for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
-            # encode, pad, and truncate contexts for this batch
-            if "[UNUSED_TOKEN_146]" not in contexts:
-                contexts = f"[UNUSED_TOKEN_146]user\n{contexts}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
-            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
-            visuals = self.flatten(visuals)
-
-            if "hd_num" not in gen_kwargs:
-                if listinstr(["docvqa_test", "infovqa_test"], task.lower()):
-                    self.model.hd_num = 65
-                elif listinstr(["docvqa_val", "infovqa_val", "OCRBench"], task.lower()):
-                    self.model.hd_num = 55
-                elif listinstr(["mmmu", "mmbench", "mmvet"], task.lower()):
-                    self.model.hd_num = 16
-                else:
-                    self.model.hd_num = 25
-            else:
-                self.model.hd_num = gen_kwargs.pop("hd_num")
-
-            pt1 = 0
-            embeds = []
-            im_mask = []
-            images_loc = [0]
-            need_bos = self.need_bos
-            padding = self.padding
-            for i, pts in enumerate(images_loc + [len(contexts)]):
-                subtext = contexts[pt1:pts]
-                if need_bos or len(subtext) > 0:
-                    text_embeds = self.model.encode_text(subtext, add_special_tokens=need_bos).to(self.device)
-                    embeds.append(text_embeds)
-                    im_mask.append(torch.zeros(text_embeds.shape[:2]).to(self.device))
-                    need_bos = False
-                if i < len(visuals):
-                    image = visuals[i]
-
-                    image = HD_transform(image, im_num=self.model.hd_num)
-                    image = self.model.vis_processor(image).unsqueeze(0).to(self.device)
-                    image_embeds = self.model.encode_img(image)
-                    embeds.append(image_embeds)
-                    im_mask.append(torch.ones(image_embeds.shape[:2]).to(self.device))
-                pt1 = pts
-            embeds = torch.cat(embeds, dim=1)
-            im_mask = torch.cat(im_mask, dim=1)
-            im_mask = im_mask.bool()
-
-            if "max_new_tokens" not in gen_kwargs:
-                gen_kwargs["max_new_tokens"] = 1024
-            if "temperature" not in gen_kwargs:
-                gen_kwargs["temperature"] = 0
-            if "top_p" not in gen_kwargs:
-                gen_kwargs["top_p"] = None
-            if "num_beams" not in gen_kwargs:
-                gen_kwargs["num_beams"] = 1
-            if "do_sample" not in gen_kwargs:
-                gen_kwargs["do_sample"] = False
-            if "repetition_penalty" not in gen_kwargs:
-                gen_kwargs["repetition_penalty"] = 1.0
-
-            outputs = self.model.generate(
-                inputs_embeds=embeds,
-                im_mask=im_mask,
-                temperature=gen_kwargs["temperature"],
-                max_new_tokens=gen_kwargs["max_new_tokens"],
-                num_beams=gen_kwargs["num_beams"],
-                do_sample=gen_kwargs["do_sample"],
-                repetition_penalty=gen_kwargs["repetition_penalty"],
-            )
-            output_token = outputs[0]
-            if output_token[0] == 0 or output_token[0] == 1:
-                output_token = output_token[1:]
-            output_text = self.model.tokenizer.decode(output_token, add_special_tokens=False)
-            output_text = output_text.split("[UNUSED_TOKEN_145]")[0].strip()
-            output_text = output_text.split("<|im_end|>")[0].strip()
-            # if DATASET_TYPE(task) == "multi-choice":
-            #     output_text = pattern.findall(output_text)
-            #     if len(output_text) == 0:
-            #         print("Error:", output_text)
-            #         output_text = "Z"
-            #     if type(output_text) == list:
-            #         output_text = output_text[0]
-            res.append(output_text)
-            pbar.update(1)
-        pbar.close()
-        return res
-
-    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
-        return super().loglikelihood(requests)
-
-
-def padding_336(b):
-    width, height = b.size
-    tar = int(np.ceil(height / 336) * 336)
-    top_padding = int((tar - height) / 2)
-    bottom_padding = tar - height - top_padding
-    left_padding = 0
-    right_padding = 0
-    b = transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255, 255, 255])
-
-    return b
-
-
-def HD_transform(img, im_num=16):
-    width, height = img.size
-    trans = False
-    if width < height:
-        img = img.transpose(Image.TRANSPOSE)
-        trans = True
-        width, height = img.size
-    ratio = width / height
-    scale = 1
-    while scale * np.ceil(scale / ratio) <= im_num:
-        scale += 1
-    scale -= 1
-    new_w = int(scale * 336)
-    new_h = int(new_w / ratio)
-
-    img = transforms.functional.resize(
-        img,
-        [new_h, new_w],
-    )
-    img = padding_336(img)
-    width, height = img.size
-    assert width * height <= im_num * 336 * 336
-    if trans:
-        img = img.transpose(Image.TRANSPOSE)
-
-    return img
-
-
-def listinstr(lst, s):
-    assert isinstance(lst, list)
-    for item in lst:
-        if item in s:
-            return True
-    return False
-
-
-def DATASET_TYPE(dataset):
-    # Dealing with Custom Dataset
-    dataset = dataset.lower()
-    if listinstr(["mmbench", "seedbench", "ccbench", "mmmu", "scienceqa", "ai2d", "mmstar"], dataset):
-        return "multi-choice"
-    elif listinstr(["mme", "hallusion"], dataset):
-        return "Y/N"
-    elif "coco" in dataset:
-        return "Caption"
-    elif listinstr(["ocrvqa", "textvqa", "chartqa", "mathvista", "docvqa", "infovqa", "llavabench", "mmvet", "ocrbench"], dataset):
-        return "VQA"
-    else:
-        return "QA"
diff --git a/lmms_eval/tasks/llava_interleave_bench/in_domain.yaml b/lmms_eval/tasks/llava_interleave_bench/in_domain.yaml
index 86255d95..9a6bf8e4 100644
--- a/lmms_eval/tasks/llava_interleave_bench/in_domain.yaml
+++ b/lmms_eval/tasks/llava_interleave_bench/in_domain.yaml
@@ -10,11 +10,8 @@ doc_to_text: !function utils.doc_to_text
 process_results: !function utils.interleave_process_results
 
 metric_list:
-  - metric: mcq_acc
-    aggregation: !function utils.mcq_acc
-    higher_is_better: true
-  - metric: oe_rogue
-    aggregation: !function utils.oe_rogue
+  - metric: overall_score
+    aggregation: !function utils.overall_score
     higher_is_better: true
 
 generation_kwargs:
@@ -26,4 +23,4 @@ generation_kwargs:
 model_specific_prompt_kwargs:
   default:
     oe_post_prompt: ""
-    mcq_post_prompt: "Answer with the option's letter from the given choices directly."
\ No newline at end of file
+    mcq_post_prompt: ""
diff --git a/lmms_eval/tasks/llava_interleave_bench/interleave_bench.yaml b/lmms_eval/tasks/llava_interleave_bench/interleave_bench.yaml
new file mode 100644
index 00000000..2d68410b
--- /dev/null
+++ b/lmms_eval/tasks/llava_interleave_bench/interleave_bench.yaml
@@ -0,0 +1,5 @@
+group: llava_interleave_bench
+task:
+- llava_interleave_bench_in_domain
+- llava_interleave_bench_out_domain
+- llava_interleave_bench_multi_view
\ No newline at end of file
diff --git a/lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml b/lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml
index f478caeb..5b3dec18 100644
--- a/lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml
+++ b/lmms_eval/tasks/llava_interleave_bench/multi_view_in_domain.yaml
@@ -2,28 +2,25 @@ dataset_path: lmms-lab/LLaVA-NeXT-Interleave-Bench
 dataset_name: multi_view_in_domain
 dataset_kwargs:
   token: True
-task: "llava_interleave_bench_multi_view_in_domain"
+task: "llava_interleave_bench_multi_view"
 test_split: test
 doc_to_target: "answer"
 doc_to_visual: !function utils.doc_to_visual
-doc_to_text: !function utils.doc_to_text
+doc_to_text: !function utils.doc_to_text_conversation
 process_results: !function utils.interleave_process_results
 
 metric_list:
-  - metric: mcq_acc
-    aggregation: !function utils.mcq_acc
-    higher_is_better: true
-  - metric: oe_rogue
-    aggregation: !function utils.oe_rogue
+  - metric: overall_score
+    aggregation: !function utils.overall_score
     higher_is_better: true
 
 generation_kwargs:
   max_new_tokens: 16
   temperature: 0
   do_sample: False
-  image_aspect_ratio: "original" # for multi-image, we treat each image as original aspect ratio without anyres strategy.
+  image_aspect_ratio: "pad" # for multi-image, we treat each image as original aspect ratio without anyres strategy.
 
 model_specific_prompt_kwargs:
   default:
     oe_post_prompt: ""
-    mcq_post_prompt: "Answer with the option's letter from the given choices directly."
\ No newline at end of file
+    mcq_post_prompt: ""
diff --git a/lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml b/lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml
index 62601c85..47c3a951 100644
--- a/lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml
+++ b/lmms_eval/tasks/llava_interleave_bench/out_of_domain.yaml
@@ -10,11 +10,8 @@ doc_to_text: !function utils.doc_to_text
 process_results: !function utils.interleave_process_results
 
 metric_list:
-  - metric: mcq_acc
-    aggregation: !function utils.mcq_acc
-    higher_is_better: true
-  - metric: oe_rogue
-    aggregation: !function utils.oe_rogue
+  - metric: overall_score
+    aggregation: !function utils.overall_score
     higher_is_better: true
 
 generation_kwargs:
@@ -26,4 +23,4 @@ generation_kwargs:
 model_specific_prompt_kwargs:
   default:
     oe_post_prompt: ""
-    mcq_post_prompt: "Answer with the option's letter from the given choices directly."
\ No newline at end of file
+    mcq_post_prompt: ""
diff --git a/lmms_eval/tasks/llava_interleave_bench/utils.py b/lmms_eval/tasks/llava_interleave_bench/utils.py
index 1b95dc5f..77175600 100644
--- a/lmms_eval/tasks/llava_interleave_bench/utils.py
+++ b/lmms_eval/tasks/llava_interleave_bench/utils.py
@@ -1,18 +1,27 @@
 import os
 import re
-import sys
-import datetime
-import json
 import requests
 import time
+import json
 
-from lmms_eval.filters.extraction import ExtendedRegexFilter
-
+from collections import defaultdict
+from rouge import Rouge
 import yaml
 from pathlib import Path
 from loguru import logger as eval_logger
 from PIL import Image
 
+spot_the_diff = ["Spot-the-Diff", "Birds-to-Words", "CLEVR-Change"]
+image_edit_instruct = ["IEdit", "HQ-Edit", "MagicBrush"]
+visual_story_telling = ["AESOP", "FlintstonesSV", "PororoSV", "VIST"]
+visual_cloze = ["COMICS_Dialogue", "RecipeQA_VisualCloze"]
+text_rich_vqa = ["WebQA", "TQA", "OCR-VQA", "DocVQA"]
+multi_image_vqa = ["MIT-States_StateCoherence", "MIT-States_PropertyCoherence", "VISION", "RecipeQA_ImageCoherence"]
+
+puzzle = ["RAVEN"]
+nlrv2 = ["NLVR2_Mantis"]
+qbench = ["QBench"]
+
 
 def doc_to_visual(doc):
     max_visual_count = 16
@@ -23,11 +32,11 @@ def doc_to_visual(doc):
             if image is None:
                 continue  # Skip this image if it's None
             if isinstance(image, Image.Image):
-                visuals.append(image.copy())
+                visuals.append(image.copy().convert("RGB"))
             else:
                 try:
                     # If the image is not already a PIL Image, try to open it
-                    visuals.append(Image.open(image))
+                    visuals.append(Image.open(image).convert("RGB"))
                 except Exception as e:
                     print(f"Error opening image_{i}: {e}")
                     # Optionally, you can add a placeholder image or just continue
@@ -61,6 +70,20 @@ def doc_to_text(doc, model_specific_prompt_kwargs=None):
     return user_prompt
 
 
+def doc_to_text_conversation(doc, model_specific_prompt_kwargs=None):
+    if model_specific_prompt_kwargs is None:
+        model_specific_prompt_kwargs = {}
+
+    conversations = doc["conversations"]
+
+    if isinstance(conversations, list):
+        user_prompt = json.dumps(conversations)
+    else:
+        user_prompt = conversations
+
+    return user_prompt
+
+
 def doc_to_text_multi_turn(doc, model_specific_prompt_kwargs=None):
     if model_specific_prompt_kwargs is None:
         model_specific_prompt_kwargs = {}
@@ -71,18 +94,22 @@ def doc_to_text_multi_turn(doc, model_specific_prompt_kwargs=None):
 def interleave_process_results(doc, results):
     pred = results[0]
     sample_id = doc["sample_id"]
-    model_response = {"sample_id": sample_id, "subtask": doc["sample_id"], "question_type": doc["question_type"], "answer": doc["answer"], "parsed_pred": pred}
+
+    if doc["question_type"] == "multi-choice":
+        score = mcq_acc(doc["answer"], pred)
+        model_response = {"sample_id": sample_id, "sub_task": doc["sub_task"], "question_type": doc["question_type"], "answer": doc["answer"], "parsed_pred": pred, "score": score}
+    elif doc["question_type"] == "open-ended":
+        score = oe_rogue(doc["answer"], pred)
+        model_response = {"sample_id": sample_id, "sub_task": doc["sub_task"], "question_type": doc["question_type"], "answer": doc["answer"], "parsed_pred": pred, "score": score}
+    else:
+        raise ValueError(f"Unknown question type: {doc['question_type']}")
+
     return {
-        "mcq_acc": model_response,
-        "oe_rogue": model_response,
-        # "in_domain_oe_gpt_eval": in_domain_acc,
+        "overall_score": model_response,
     }
 
 
-def mcq_acc(results, args):
-    correct_count = 0
-    total_count = len(results)
-
+def mcq_acc(answer, pred):
     periodStrip = re.compile("(?!<=\d)(\.)(?!\d)")
     commaStrip = re.compile("(\d)(\,)(\d)")
     punct = [";", r"/", "[", "]", '"', "{", "}", "(", ")", "=", "+", "\\", "_", "-", ">", "<", "@", "`", ",", "?", "!"]
@@ -123,209 +150,210 @@ def process(answer):
 
             return answer
 
-    # image_choice_dataset_list = ["recipeqa-RecipeQA_VisualCloze", "RecipeQA_ImageCoherence", "COMICS_Panel"]
-    mcq_eval_result_list = []
-    mcq_eval_result_list_detail = defaultdict(list)
+    pred = process(pred)
+    answer = process(answer)
 
-    for result in results:
-        if result["question_type"] == "multi-choice":
-            pred = process(result["parsed_pred"])
-            answer = process(result["answer"])
-
-            if pred == answer:
-                score = 1
-            else:
-                score = 0
-
-            mcq_eval_result_list_detail[result["sub_task"]].append(score)
-            mcq_eval_result_list[result["sub_task"]] = mcq_eval_result_list_detail[result["sub_task"]]
-
-    overall_accuracy = sum(mcq_eval_result_list) / len(mcq_eval_result_list)
-    for sub_task in mcq_eval_result_list:
-        sub_task_accuracy = sum(mcq_eval_result_list[sub_task]) / len(mcq_eval_result_list[sub_task]) * 100.0
-        eval_logger.info(f"Multi-Choice Sub-Task {sub_task} - accuracy: {sub_task_accuracy}")
-    return overall_accuracy
+    if pred == answer:
+        score = 1
+    else:
+        score = 0
 
-
-from rouge import Rouge
-import numpy as np
+    return score
 
 
-def oe_rogue(results, args):
+def oe_rogue(answer, pred):
     rouge = Rouge()
-    oe_eval_result_list = []
-    oe_eval_result_list_detail = defaultdict(list)
-
-    for result in results:
-        if result["question_type"] == "open-ended":
-            pred = result["parsed_pred"]
-            answer = result["answer"]
-
-            if answer == "":
-                continue
-
-            if pred == "":
-                score = 0
-            else:
-                if len(pred) > 512:
-                    pred = pred[:512]
-                score = rouge.get_scores(pred, answer)[0]["rouge-l"]["f"]
-
-            oe_eval_result_list.append(score)
-            oe_eval_result_list_detail[result["sub_task"]].append(score)
-
-    average_rouge_score = np.mean(oe_eval_result_list) if oe_eval_result_list else 0
-    for sub_task in oe_eval_result_list_detail:
-        sub_task_rouge_score = np.mean(oe_eval_result_list_detail[sub_task]) if oe_eval_result_list_detail[sub_task] else 0
-        eval_logger.info(f"Open-Ended Sub-Task {sub_task} - ROUGE-L: {sub_task_rouge_score}")
-    return average_rouge_score * 100.0
-
-
-EVAL_PROMPT = """
-[Question]
-{question}
-
-[Assistant Response]
-{model_response}
-
-[Ground Truth Response]
-{ground_truth}
-
-[System]
-Rate whether the assistant response correctly matches the ground truth, it's about a question towards a sequence of images shared by the user.
-The rating should be 1-5, where 1 is incorrect and 5 is correct.
-Your response should be in the format:
-Explanation: (your explanation)
-Rating: (int)
-"""
-
-NUM_SECONDS_TO_SLEEP = 5
-dir_path = os.path.dirname(os.path.realpath(__file__))
-with open(Path(__file__).parent / "_default_template_interleave_yaml", "r") as f:
-    raw_data = f.readlines()
-    safe_data = []
-    for i, line in enumerate(raw_data):
-        # remove function definition since yaml load cannot handle it
-        if "!function" not in line:
-            safe_data.append(line)
-
-    config = yaml.safe_load("".join(safe_data))
-
-GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"]
-API_TYPE = config["metadata"]["api_type"]
-
-if API_TYPE == "openai":
-    API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
-    API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json",
-    }
-elif API_TYPE == "azure":
-    API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
-    API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
-    headers = {
-        "api-key": API_KEY,
-        "Content-Type": "application/json",
-    }
-else:
-    API_URL = ""
-    API_KEY = ""
-
-
-def get_chat_response(prompt, max_retries=5, wait_time=10):
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json",
-    }
-
-    payload = {
-        "model": GPT_EVAL_MODEL_NAME,
-        "messages": [
-            {
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": prompt},
-                ],
-            }
-        ],
-        "max_tokens": 1024,
-        "temperature": 0.0,
+    if pred == "":
+        score = 0
+    else:
+        if len(pred) > 512:
+            pred = pred[:512]
+        score = rouge.get_scores(pred, answer)[0]["rouge-l"]["f"]
+
+    return score
+
+
+def overall_score(results):
+    categories = {
+        "Spot-the-Diff": spot_the_diff,
+        "Image-Edit": image_edit_instruct,
+        "Visual-Story-Telling": visual_story_telling,
+        "Visual-Cloze": visual_cloze,
+        "Text-Rich-VQA": text_rich_vqa,
+        "Multi-Image-VQA": multi_image_vqa,
+        "Puzzle": puzzle,
+        "NLVR2": nlrv2,
+        "QBench": qbench,
     }
 
-    for attempt in range(max_retries):
-        try:
-            response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
-            response.raise_for_status()
-            response_data = response.json()
-            return response_data["choices"][0]["message"]["content"], GPT_EVAL_MODEL_NAME
-        except requests.exceptions.RequestException as e:
-            eval_logger.warning(f"Request failed on attempt {attempt+1}: {e}")
-            time.sleep(wait_time)
-            if attempt == max_retries - 1:
-                eval_logger.error(f"Failed to get response after {max_retries} attempts")
-                return "", GPT_EVAL_MODEL_NAME
-        except Exception as e:
-            eval_logger.error(f"Error on attempt {attempt+1}: {e}")
-            return "", GPT_EVAL_MODEL_NAME
-
-
-def in_domain_oe_gpt_eval(results, args):
-    total_score = 0
-    available_count = 0
-    for result in results:
-        if result["question_type"] == "open-ended":
-            question = result["question"]
-            model_response = result["parsed_pred"]
-            ground_truth = result["answer"]
-            content = EVAL_PROMPT.format(question=question, model_response=model_response, ground_truth=ground_truth)
-            result["gpt_eval_input"] = content
-            model_output, model_name = get_chat_response(content)
-            try:
-                explanation = re.search(r"Explanation: (.*)\n", model_output).group(1)
-                rating = re.search(r"Rating: (\d+)\n", model_output).group(1)
-                result["gpt_eval_explanation"] = explanation
-                result["gpt_eval_rating"] = rating
-                result["gpt_eval_model_name"] = model_name
-            except:
-                eval_logger.error(f"Error on evaluating {result['sample_id']}. Results: {results}")
-                result["gpt_eval_explanation"] = ""
-                result["gpt_eval_rating"] = 0
-                result["gpt_eval_model_name"] = model_name
-
-            total_score += result["gpt_eval_rating"]
-            available_count += 1
-
-        elif result["question_type"] == "multi-choice":
-            pass
-
-    return (total_score / available_count) * 20.0 if available_count > 0 else 0
-
-
-# class MultiChoiceRegexFilter(ExtendedRegexFilter):
-#     def __init__(self, *args, **kwargs):
-#         super().__init__(*args, **kwargs)
-
-#     def apply(self, resps, docs):
-#         filtered_resps = []
-
-#         for r, doc in zip(resps, docs):
-#             # Regex to directly extract the option letter from the model response
-#             option_letter_regex = re.compile(r"\b([A-Z])\.\s+([^\n]*)")
-
-#             # Process each response
-#             filtered = []
-#             for resp in r:
-#                 # Try to match the option letter at the start of the response
-#                 match = option_letter_regex.match(resp)
-#                 if match:
-#                     # If a match is found, append the matched letter
-#                     filtered.append(match.group(1))
-#                 else:
-#                     # If no match, return the original response
-#                     filtered.append(resp)
-
-#             # Assuming we need the first response that matches or the original response
-#             filtered_resps.append(filtered[0])
-
-#         return filtered_resps
+    category_scores = {}
+
+    eval_logger.info(f"Evaluation Sub-Task Results:")
+    for category, subtasks in categories.items():
+        score = 0
+        count = 0
+        for result in results:
+            if result["sub_task"] in subtasks:
+                count += 1
+                score += result["score"]
+        if count > 0:
+            avg_score = score / count
+            category_scores[category] = avg_score
+            eval_logger.info(f"{category}: {avg_score:.3f}")
+
+    # Calculate overall score
+    total_score = sum(category_scores.values())
+    num_categories = len(category_scores)
+    overall_score = total_score / num_categories if num_categories > 0 else 0
+
+    return overall_score
+
+
+# EVAL_PROMPT = """
+# [Question]
+# {question}
+
+# [Assistant Response]
+# {model_response}
+
+# [Ground Truth Response]
+# {ground_truth}
+
+# [System]
+# Rate whether the assistant response correctly matches the ground truth, it's about a question towards a sequence of images shared by the user.
+# The rating should be 1-5, where 1 is incorrect and 5 is correct.
+# Your response should be in the format:
+# Explanation: (your explanation)
+# Rating: (int)
+# """
+
+# NUM_SECONDS_TO_SLEEP = 5
+# dir_path = os.path.dirname(os.path.realpath(__file__))
+# with open(Path(__file__).parent / "_default_template_interleave_yaml", "r") as f:
+#     raw_data = f.readlines()
+#     safe_data = []
+#     for i, line in enumerate(raw_data):
+#         # remove function definition since yaml load cannot handle it
+#         if "!function" not in line:
+#             safe_data.append(line)
+
+#     config = yaml.safe_load("".join(safe_data))
+
+# GPT_EVAL_MODEL_NAME = config["metadata"]["gpt_eval_model_name"]
+# API_TYPE = config["metadata"]["api_type"]
+
+# if API_TYPE == "openai":
+#     API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
+#     API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
+#     headers = {
+#         "Authorization": f"Bearer {API_KEY}",
+#         "Content-Type": "application/json",
+#     }
+# elif API_TYPE == "azure":
+#     API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
+#     API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
+#     headers = {
+#         "api-key": API_KEY,
+#         "Content-Type": "application/json",
+#     }
+# else:
+#     API_URL = ""
+#     API_KEY = ""
+
+
+# def get_chat_response(prompt, max_retries=5, wait_time=10):
+#     headers = {
+#         "Authorization": f"Bearer {API_KEY}",
+#         "Content-Type": "application/json",
+#     }
+
+#     payload = {
+#         "model": GPT_EVAL_MODEL_NAME,
+#         "messages": [
+#             {
+#                 "role": "user",
+#                 "content": [
+#                     {"type": "text", "text": prompt},
+#                 ],
+#             }
+#         ],
+#         "max_tokens": 1024,
+#         "temperature": 0.0,
+#     }
+
+#     for attempt in range(max_retries):
+#         try:
+#             response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
+#             response.raise_for_status()
+#             response_data = response.json()
+#             return response_data["choices"][0]["message"]["content"], GPT_EVAL_MODEL_NAME
+#         except requests.exceptions.RequestException as e:
+#             eval_logger.warning(f"Request failed on attempt {attempt+1}: {e}")
+#             time.sleep(wait_time)
+#             if attempt == max_retries - 1:
+#                 eval_logger.error(f"Failed to get response after {max_retries} attempts")
+#                 return "", GPT_EVAL_MODEL_NAME
+#         except Exception as e:
+#             eval_logger.error(f"Error on attempt {attempt+1}: {e}")
+#             return "", GPT_EVAL_MODEL_NAME
+
+
+# def in_domain_oe_gpt_eval(results, args):
+#     total_score = 0
+#     available_count = 0
+#     for result in results:
+#         if result["question_type"] == "open-ended":
+#             question = result["question"]
+#             model_response = result["parsed_pred"]
+#             ground_truth = result["answer"]
+#             content = EVAL_PROMPT.format(question=question, model_response=model_response, ground_truth=ground_truth)
+#             result["gpt_eval_input"] = content
+#             model_output, model_name = get_chat_response(content)
+#             try:
+#                 explanation = re.search(r"Explanation: (.*)\n", model_output).group(1)
+#                 rating = re.search(r"Rating: (\d+)\n", model_output).group(1)
+#                 result["gpt_eval_explanation"] = explanation
+#                 result["gpt_eval_rating"] = rating
+#                 result["gpt_eval_model_name"] = model_name
+#             except:
+#                 eval_logger.error(f"Error on evaluating {result['sample_id']}. Results: {results}")
+#                 result["gpt_eval_explanation"] = ""
+#                 result["gpt_eval_rating"] = 0
+#                 result["gpt_eval_model_name"] = model_name
+
+#             total_score += result["gpt_eval_rating"]
+#             available_count += 1
+
+#         elif result["question_type"] == "multi-choice":
+#             pass
+
+#     return (total_score / available_count) * 20.0 if available_count > 0 else 0
+
+
+# # class MultiChoiceRegexFilter(ExtendedRegexFilter):
+# #     def __init__(self, *args, **kwargs):
+# #         super().__init__(*args, **kwargs)
+
+# #     def apply(self, resps, docs):
+# #         filtered_resps = []
+
+# #         for r, doc in zip(resps, docs):
+# #             # Regex to directly extract the option letter from the model response
+# #             option_letter_regex = re.compile(r"\b([A-Z])\.\s+([^\n]*)")
+
+# #             # Process each response
+# #             filtered = []
+# #             for resp in r:
+# #                 # Try to match the option letter at the start of the response
+# #                 match = option_letter_regex.match(resp)
+# #                 if match:
+# #                     # If a match is found, append the matched letter
+# #                     filtered.append(match.group(1))
+# #                 else:
+# #                     # If no match, return the original response
+# #                     filtered.append(resp)
+
+# #             # Assuming we need the first response that matches or the original response
+# #             filtered_resps.append(filtered[0])
+
+# #         return filtered_resps
diff --git a/lmms_eval/utils.py b/lmms_eval/utils.py
index 80231c9c..40e71b22 100755
--- a/lmms_eval/utils.py
+++ b/lmms_eval/utils.py
@@ -2,6 +2,7 @@
 import re
 import sys
 import yaml
+import json
 import inspect
 import pathlib
 import functools
@@ -41,6 +42,14 @@
 SPACING = " " * 47
 
 
+def is_json(string):
+    try:
+        json.loads(string)
+        return True
+    except json.JSONDecodeError:
+        return False
+
+
 def escaped_split(text, sep_char, maxsplit=-1):
     """Split text into a list on occurrences of the given separation
     character `sep_char`. The separation character may be escaped by a

From 252f72fe757ed1bb828e5d0de8301f1790ddd468 Mon Sep 17 00:00:00 2001
From: Yan Shu <570533048@qq.com>
Date: Mon, 8 Jul 2024 17:06:13 +0800
Subject: [PATCH 21/32] Add files via upload

---
 .../mlvu/__pycache__/utils.cpython-310.pyc    | Bin 0 -> 3207 bytes
 lmms_eval/tasks/mlvu/mlvu.yaml                |  21 +++
 lmms_eval/tasks/mlvu/utils.py                 | 124 ++++++++++++++++++
 3 files changed, 145 insertions(+)
 create mode 100644 lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc
 create mode 100644 lmms_eval/tasks/mlvu/mlvu.yaml
 create mode 100644 lmms_eval/tasks/mlvu/utils.py

diff --git a/lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc b/lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc
new file mode 100644
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diff --git a/lmms_eval/tasks/mlvu/mlvu.yaml b/lmms_eval/tasks/mlvu/mlvu.yaml
new file mode 100644
index 00000000..375da595
--- /dev/null
+++ b/lmms_eval/tasks/mlvu/mlvu.yaml
@@ -0,0 +1,21 @@
+dataset_path: sy1998/temp
+dataset_kwargs:
+  token: True
+  cache_dir: mlvu
+  video: True
+task: mlvu
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.mlvu_doc_to_visual
+doc_to_text: !function utils.mlvu_doc_to_text
+doc_to_target: "answer"
+# The return value of process_results will be used by metrics
+process_results: !function utils.mlvu_process_results
+# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
+metric_list:
+  - metric: mlvu_percetion_score
+    aggregation: !function utils.mlvu_aggregate_results
+    higher_is_better: true
+
+
+
diff --git a/lmms_eval/tasks/mlvu/utils.py b/lmms_eval/tasks/mlvu/utils.py
new file mode 100644
index 00000000..476cf9dc
--- /dev/null
+++ b/lmms_eval/tasks/mlvu/utils.py
@@ -0,0 +1,124 @@
+from collections import defaultdict
+import os
+import datetime
+import json
+from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
+from pathlib import Path
+import yaml
+import sys
+from typing import List, Dict, Optional, Union
+import re
+import cv2
+import numpy as np
+from loguru import logger as eval_logger
+
+TASK_TYPES = [
+    "TR",
+    "AR",
+    "VS",
+    "NQA",
+    "ER",
+    "PQA",
+    "SSC",
+    "AO",
+    "AC"
+]
+
+
+
+hf_home = os.getenv("HF_HOME", "./~/.cache/huggingface")
+base_cache_dir = os.path.expanduser(hf_home)
+
+with open(Path(__file__).parent / "mlvu.yaml", "r") as f:
+    raw_data = f.readlines()
+    safe_data = []
+    for i, line in enumerate(raw_data):
+        # remove function definition since yaml load cannot handle it
+        if "!function" not in line:
+            safe_data.append(line)
+cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"]
+
+
+
+def mlvu_doc_to_visual(doc):
+
+    cache_dir = os.path.join(base_cache_dir, cache_name)
+    video_path = doc["video_name"]
+    video_path = os.path.join(cache_dir, video_path)
+    if os.path.exists(video_path):
+        video_path = video_path
+    else:
+        sys.exit(f"video path:{video_path} does not exist, please check")
+    return [video_path]
+
+
+def mlvu_doc_to_text(doc, model_specific_prompt_kwargs=None):
+    # option_prompt="Carefully watch this video and pay attention to every detail. Based on your observations, select the best option that accurately addresses the question."
+    option_prompt=""
+    question = doc["question"] + "\nOnly give the best option.\n" 
+    full_prompt=option_prompt+"\n"+question+"\n"+"Best option: ("
+    return full_prompt
+
+
+def extract_characters_regex(s):
+    s = s.strip()
+    if ")" in s:
+        index=s.index(")")
+        pred=s[index-1:index]
+        return pred
+    else:
+        return s
+
+def mlvu_process_results(doc, results):
+    """
+    Args:
+        doc: a instance of the eval dataset
+        results: [pred]
+    Returns:
+        a dictionary with key: metric name (in this case videomme score), value: metric value
+    """
+    pred = results[0]
+    # print("****************",pred)
+    pred_ans = extract_characters_regex(pred)
+
+    task_type = doc["task_type"]
+    data_dict = {"question_id": doc["question"], "task_type": task_type, "pred_answer": pred_ans, "answer": doc["answer"]}
+
+    return {f"mlvu_percetion_score": data_dict}
+
+
+def mlvu_aggregate_results(results):
+    """
+    Args:
+        results: a list of values returned by process_results
+    Returns:
+        A score
+    """
+    category2score = {}
+    for task_type in TASK_TYPES:
+        category2score[task_type] = {"correct": 0, "answered": 0}
+
+
+    for result in results:
+        task_type = result["task_type"]
+        category2score[task_type]["answered"] += 1
+        category2score[task_type]["correct"] += result["pred_answer"] == result["answer"]
+
+
+    for task_cate in TASK_TYPES:
+        total_correct = 0
+        total_answered = 0
+        for k, v in category2score.items():
+            if task_cate in k:
+                total_correct += v["correct"]
+                total_answered += v["answered"]
+        eval_logger.info(f"Evaluation on Task Categories: {task_cate}: {100 * total_correct / total_answered if total_answered > 0 else 0 : .1f}%")
+
+    total_correct = 0
+    total_answered = 0
+    for k, v in category2score.items():
+        total_correct += v["correct"]
+        total_answered += v["answered"]
+    eval_logger.info(f"Overall Performance: {100 * total_correct / total_answered if total_answered > 0 else 0 : .1f}%")
+
+    return 100 * total_correct / total_answered if total_answered > 0 else 0

From 3018af5ea5569960e05ef1f4897edb3d87679004 Mon Sep 17 00:00:00 2001
From: Yan Shu <570533048@qq.com>
Date: Mon, 8 Jul 2024 17:17:20 +0800
Subject: [PATCH 22/32] Delete lmms_eval/tasks/mlvu directory

---
 .../mlvu/__pycache__/utils.cpython-310.pyc    | Bin 3207 -> 0 bytes
 lmms_eval/tasks/mlvu/mlvu.yaml                |  21 ---
 lmms_eval/tasks/mlvu/utils.py                 | 124 ------------------
 3 files changed, 145 deletions(-)
 delete mode 100644 lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc
 delete mode 100644 lmms_eval/tasks/mlvu/mlvu.yaml
 delete mode 100644 lmms_eval/tasks/mlvu/utils.py

diff --git a/lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc b/lmms_eval/tasks/mlvu/__pycache__/utils.cpython-310.pyc
deleted file mode 100644
index 1a9fda0f2b70ce7af4bbcc1868a64d7c8e67571d..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/lmms_eval/tasks/mlvu/mlvu.yaml b/lmms_eval/tasks/mlvu/mlvu.yaml
deleted file mode 100644
index 375da595..00000000
--- a/lmms_eval/tasks/mlvu/mlvu.yaml
+++ /dev/null
@@ -1,21 +0,0 @@
-dataset_path: sy1998/temp
-dataset_kwargs:
-  token: True
-  cache_dir: mlvu
-  video: True
-task: mlvu
-test_split: test
-output_type: generate_until
-doc_to_visual: !function utils.mlvu_doc_to_visual
-doc_to_text: !function utils.mlvu_doc_to_text
-doc_to_target: "answer"
-# The return value of process_results will be used by metrics
-process_results: !function utils.mlvu_process_results
-# Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
-metric_list:
-  - metric: mlvu_percetion_score
-    aggregation: !function utils.mlvu_aggregate_results
-    higher_is_better: true
-
-
-
diff --git a/lmms_eval/tasks/mlvu/utils.py b/lmms_eval/tasks/mlvu/utils.py
deleted file mode 100644
index 476cf9dc..00000000
--- a/lmms_eval/tasks/mlvu/utils.py
+++ /dev/null
@@ -1,124 +0,0 @@
-from collections import defaultdict
-import os
-import datetime
-import json
-from lmms_eval.tasks._task_utils.file_utils import generate_submission_file
-from pathlib import Path
-import yaml
-import sys
-from typing import List, Dict, Optional, Union
-import re
-import cv2
-import numpy as np
-from loguru import logger as eval_logger
-
-TASK_TYPES = [
-    "TR",
-    "AR",
-    "VS",
-    "NQA",
-    "ER",
-    "PQA",
-    "SSC",
-    "AO",
-    "AC"
-]
-
-
-
-hf_home = os.getenv("HF_HOME", "./~/.cache/huggingface")
-base_cache_dir = os.path.expanduser(hf_home)
-
-with open(Path(__file__).parent / "mlvu.yaml", "r") as f:
-    raw_data = f.readlines()
-    safe_data = []
-    for i, line in enumerate(raw_data):
-        # remove function definition since yaml load cannot handle it
-        if "!function" not in line:
-            safe_data.append(line)
-cache_name = yaml.safe_load("".join(safe_data))["dataset_kwargs"]["cache_dir"]
-
-
-
-def mlvu_doc_to_visual(doc):
-
-    cache_dir = os.path.join(base_cache_dir, cache_name)
-    video_path = doc["video_name"]
-    video_path = os.path.join(cache_dir, video_path)
-    if os.path.exists(video_path):
-        video_path = video_path
-    else:
-        sys.exit(f"video path:{video_path} does not exist, please check")
-    return [video_path]
-
-
-def mlvu_doc_to_text(doc, model_specific_prompt_kwargs=None):
-    # option_prompt="Carefully watch this video and pay attention to every detail. Based on your observations, select the best option that accurately addresses the question."
-    option_prompt=""
-    question = doc["question"] + "\nOnly give the best option.\n" 
-    full_prompt=option_prompt+"\n"+question+"\n"+"Best option: ("
-    return full_prompt
-
-
-def extract_characters_regex(s):
-    s = s.strip()
-    if ")" in s:
-        index=s.index(")")
-        pred=s[index-1:index]
-        return pred
-    else:
-        return s
-
-def mlvu_process_results(doc, results):
-    """
-    Args:
-        doc: a instance of the eval dataset
-        results: [pred]
-    Returns:
-        a dictionary with key: metric name (in this case videomme score), value: metric value
-    """
-    pred = results[0]
-    # print("****************",pred)
-    pred_ans = extract_characters_regex(pred)
-
-    task_type = doc["task_type"]
-    data_dict = {"question_id": doc["question"], "task_type": task_type, "pred_answer": pred_ans, "answer": doc["answer"]}
-
-    return {f"mlvu_percetion_score": data_dict}
-
-
-def mlvu_aggregate_results(results):
-    """
-    Args:
-        results: a list of values returned by process_results
-    Returns:
-        A score
-    """
-    category2score = {}
-    for task_type in TASK_TYPES:
-        category2score[task_type] = {"correct": 0, "answered": 0}
-
-
-    for result in results:
-        task_type = result["task_type"]
-        category2score[task_type]["answered"] += 1
-        category2score[task_type]["correct"] += result["pred_answer"] == result["answer"]
-
-
-    for task_cate in TASK_TYPES:
-        total_correct = 0
-        total_answered = 0
-        for k, v in category2score.items():
-            if task_cate in k:
-                total_correct += v["correct"]
-                total_answered += v["answered"]
-        eval_logger.info(f"Evaluation on Task Categories: {task_cate}: {100 * total_correct / total_answered if total_answered > 0 else 0 : .1f}%")
-
-    total_correct = 0
-    total_answered = 0
-    for k, v in category2score.items():
-        total_correct += v["correct"]
-        total_answered += v["answered"]
-    eval_logger.info(f"Overall Performance: {100 * total_correct / total_answered if total_answered > 0 else 0 : .1f}%")
-
-    return 100 * total_correct / total_answered if total_answered > 0 else 0

From b9c391adcc263d8201ef12e7dfc407305e069205 Mon Sep 17 00:00:00 2001
From: kcz358 <kaichenzhang358@outlook.com>
Date: Mon, 8 Jul 2024 10:03:01 +0000
Subject: [PATCH 23/32] Restrick max tokens to 4096 for gpt4v and claude

---
 lmms_eval/models/claude.py | 2 ++
 lmms_eval/models/gpt4v.py  | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/lmms_eval/models/claude.py b/lmms_eval/models/claude.py
index e843871c..5829fbed 100644
--- a/lmms_eval/models/claude.py
+++ b/lmms_eval/models/claude.py
@@ -223,6 +223,8 @@ def generate_until(self, requests) -> List[str]:
 
             if "max_new_tokens" not in gen_kwargs:
                 gen_kwargs["max_new_tokens"] = 1024
+            if gen_kwargs["max_new_tokens"] > 4096:
+                gen_kwargs["max_new_tokens"] = 4096
             if "temperature" not in gen_kwargs:
                 gen_kwargs["temperature"] = 0
             if "top_p" not in gen_kwargs or gen_kwargs["top_p"] is None:
diff --git a/lmms_eval/models/gpt4v.py b/lmms_eval/models/gpt4v.py
index 5f89cc5d..7d9c5850 100755
--- a/lmms_eval/models/gpt4v.py
+++ b/lmms_eval/models/gpt4v.py
@@ -181,6 +181,8 @@ def generate_until(self, requests) -> List[str]:
 
             if "max_new_tokens" not in gen_kwargs:
                 gen_kwargs["max_new_tokens"] = 1024
+            if gen_kwargs["max_new_tokens"] > 4096:
+                gen_kwargs["max_new_tokens"] = 4096
             if "temperature" not in gen_kwargs:
                 gen_kwargs["temperature"] = 0
             if "top_p" not in gen_kwargs:

From 843d27330c1a150903a24d6384d116e6c643d9e3 Mon Sep 17 00:00:00 2001
From: kcz358 <kaichenzhang358@outlook.com>
Date: Mon, 8 Jul 2024 10:03:35 +0000
Subject: [PATCH 24/32] Bring back xcomposer 4kHD

---
 lmms_eval/models/xcomposer2_4KHD.py | 294 ++++++++++++++++++++++++++++
 1 file changed, 294 insertions(+)
 create mode 100644 lmms_eval/models/xcomposer2_4KHD.py

diff --git a/lmms_eval/models/xcomposer2_4KHD.py b/lmms_eval/models/xcomposer2_4KHD.py
new file mode 100644
index 00000000..e741f637
--- /dev/null
+++ b/lmms_eval/models/xcomposer2_4KHD.py
@@ -0,0 +1,294 @@
+from multiprocessing import context
+import torch
+from transformers import AutoModel, AutoTokenizer
+from PIL import Image
+import numpy as np
+import torchvision.transforms as transforms
+from datetime import timedelta
+
+from lmms_eval import utils
+from lmms_eval.api.instance import Instance
+from lmms_eval.api.model import lmms
+from lmms_eval.api.registry import register_model
+from lmms_eval.utils import stop_sequences_criteria
+
+from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
+from accelerate.state import AcceleratorState
+
+from typing import Optional, Sequence, List, Tuple, Union
+import re
+from tqdm import tqdm
+
+pattern = re.compile(r"[A-Z]")
+
+from loguru import logger as eval_logger
+
+meta_instruction = """You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔).
+- InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed\
+ by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
+- InternLM-XComposer (浦语·灵笔) can understand and communicate fluently in the language chosen by\
+ the user such as English and 中文.
+- InternLM-XComposer (浦语·灵笔) is capable of comprehending and articulating responses\
+ effectively based on the provided image."""
+
+
+@register_model("xcomposer2_4khd")
+class XComposer2_4KHD(lmms):
+    def __init__(
+        self,
+        pretrained: str = "internlm/internlm-xcomposer2-4khd-7b",
+        device: Optional[str] = "cuda:0",
+        batch_size: Optional[Union[int, str]] = 1,
+        device_map="cuda:0",
+        need_bos: bool = True,
+        padding: bool = False,
+        half: bool = False,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+
+        accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52))
+        accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs])
+        if accelerator.num_processes > 1:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            self._device = torch.device(device)
+            self.device_map = device_map
+        else:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+
+        self.pretrained = pretrained
+        self.need_bos = need_bos
+        self.padding = padding
+        self._model = AutoModel.from_pretrained(self.pretrained, device_map=self.device_map, trust_remote_code=True)
+        self._tokenizer = AutoTokenizer.from_pretrained(self.pretrained, trust_remote_code=True)
+        self.model.tokenizer = self.tokenizer
+        self.batch_size_per_gpu = batch_size
+
+        if accelerator.num_processes > 1:
+            assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
+            # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
+            # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works
+            # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work.
+            if accelerator.distributed_type == DistributedType.DEEPSPEED:
+                kwargs = {
+                    "train_micro_batch_size_per_gpu": self.batch_size_per_gpu,
+                    "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes,
+                }
+                AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs)
+                eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0")
+            if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED:
+                self._model = accelerator.prepare(self.model)
+            else:
+                self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
+            self.accelerator = accelerator
+            if self.accelerator.is_local_main_process:
+                eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
+            self._rank = self.accelerator.local_process_index
+            self._world_size = self.accelerator.num_processes
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism")
+            self._rank = 0
+            self._word_size = 1
+        else:
+            eval_logger.info(f"Using single device: {self._device}")
+            self.model.to(self._device)
+            self._rank = 0
+            self._world_size = 1
+
+    @property
+    def config(self):
+        # return the associated transformers.AutoConfig for the given pretrained model.
+        return self._config
+
+    @property
+    def tokenizer(self):
+        return self._tokenizer
+
+    @property
+    def model(self):
+        # returns the model, unwrapping it if using Accelerate
+        if hasattr(self, "accelerator"):
+            return self.accelerator.unwrap_model(self._model)
+        else:
+            return self._model
+
+    @property
+    def batch_size(self):
+        return self.batch_size_per_gpu
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def rank(self):
+        return self._rank
+
+    @property
+    def world_size(self):
+        return self._world_size
+
+    def flatten(self, input):
+        new_list = []
+        for i in input:
+            for j in i:
+                new_list.append(j)
+        return new_list
+
+    def generate_until(self, requests) -> List[str]:
+        res = []
+        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
+
+        for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
+            # encode, pad, and truncate contexts for this batch
+            if "[UNUSED_TOKEN_146]" not in contexts:
+                contexts = f"[UNUSED_TOKEN_146]user\n{contexts}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"
+            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
+            visuals = self.flatten(visuals)
+
+            if "hd_num" not in gen_kwargs:
+                if listinstr(["docvqa_test", "infovqa_test"], task.lower()):
+                    self.model.hd_num = 65
+                elif listinstr(["docvqa_val", "infovqa_val", "OCRBench"], task.lower()):
+                    self.model.hd_num = 55
+                elif listinstr(["mmmu", "mmbench", "mmvet"], task.lower()):
+                    self.model.hd_num = 16
+                else:
+                    self.model.hd_num = 25
+            else:
+                self.model.hd_num = gen_kwargs.pop("hd_num")
+
+            pt1 = 0
+            embeds = []
+            im_mask = []
+            images_loc = [0]
+            need_bos = self.need_bos
+            padding = self.padding
+            for i, pts in enumerate(images_loc + [len(contexts)]):
+                subtext = contexts[pt1:pts]
+                if need_bos or len(subtext) > 0:
+                    text_embeds = self.model.encode_text(subtext, add_special_tokens=need_bos).to(self.device)
+                    embeds.append(text_embeds)
+                    im_mask.append(torch.zeros(text_embeds.shape[:2]).to(self.device))
+                    need_bos = False
+                if i < len(visuals):
+                    image = visuals[i]
+
+                    image = HD_transform(image, im_num=self.model.hd_num)
+                    image = self.model.vis_processor(image).unsqueeze(0).to(self.device)
+                    image_embeds = self.model.encode_img(image)
+                    embeds.append(image_embeds)
+                    im_mask.append(torch.ones(image_embeds.shape[:2]).to(self.device))
+                pt1 = pts
+            embeds = torch.cat(embeds, dim=1)
+            im_mask = torch.cat(im_mask, dim=1)
+            im_mask = im_mask.bool()
+
+            if "max_new_tokens" not in gen_kwargs:
+                gen_kwargs["max_new_tokens"] = 1024
+            if "temperature" not in gen_kwargs:
+                gen_kwargs["temperature"] = 0
+            if "top_p" not in gen_kwargs:
+                gen_kwargs["top_p"] = None
+            if "num_beams" not in gen_kwargs:
+                gen_kwargs["num_beams"] = 1
+            if "do_sample" not in gen_kwargs:
+                gen_kwargs["do_sample"] = False
+            if "repetition_penalty" not in gen_kwargs:
+                gen_kwargs["repetition_penalty"] = 1.0
+
+            outputs = self.model.generate(
+                inputs_embeds=embeds,
+                im_mask=im_mask,
+                temperature=gen_kwargs["temperature"],
+                max_new_tokens=gen_kwargs["max_new_tokens"],
+                num_beams=gen_kwargs["num_beams"],
+                do_sample=gen_kwargs["do_sample"],
+                repetition_penalty=gen_kwargs["repetition_penalty"],
+            )
+            output_token = outputs[0]
+            if output_token[0] == 0 or output_token[0] == 1:
+                output_token = output_token[1:]
+            output_text = self.model.tokenizer.decode(output_token, add_special_tokens=False)
+            output_text = output_text.split("[UNUSED_TOKEN_145]")[0].strip()
+            output_text = output_text.split("<|im_end|>")[0].strip()
+            # if DATASET_TYPE(task) == "multi-choice":
+            #     output_text = pattern.findall(output_text)
+            #     if len(output_text) == 0:
+            #         print("Error:", output_text)
+            #         output_text = "Z"
+            #     if type(output_text) == list:
+            #         output_text = output_text[0]
+            res.append(output_text)
+            pbar.update(1)
+        pbar.close()
+        return res
+
+    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        return super().loglikelihood(requests)
+
+
+def padding_336(b):
+    width, height = b.size
+    tar = int(np.ceil(height / 336) * 336)
+    top_padding = int((tar - height) / 2)
+    bottom_padding = tar - height - top_padding
+    left_padding = 0
+    right_padding = 0
+    b = transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255, 255, 255])
+
+    return b
+
+
+def HD_transform(img, im_num=16):
+    width, height = img.size
+    trans = False
+    if width < height:
+        img = img.transpose(Image.TRANSPOSE)
+        trans = True
+        width, height = img.size
+    ratio = width / height
+    scale = 1
+    while scale * np.ceil(scale / ratio) <= im_num:
+        scale += 1
+    scale -= 1
+    new_w = int(scale * 336)
+    new_h = int(new_w / ratio)
+
+    img = transforms.functional.resize(
+        img,
+        [new_h, new_w],
+    )
+    img = padding_336(img)
+    width, height = img.size
+    assert width * height <= im_num * 336 * 336
+    if trans:
+        img = img.transpose(Image.TRANSPOSE)
+
+    return img
+
+
+def listinstr(lst, s):
+    assert isinstance(lst, list)
+    for item in lst:
+        if item in s:
+            return True
+    return False
+
+
+def DATASET_TYPE(dataset):
+    # Dealing with Custom Dataset
+    dataset = dataset.lower()
+    if listinstr(["mmbench", "seedbench", "ccbench", "mmmu", "scienceqa", "ai2d", "mmstar"], dataset):
+        return "multi-choice"
+    elif listinstr(["mme", "hallusion"], dataset):
+        return "Y/N"
+    elif "coco" in dataset:
+        return "Caption"
+    elif listinstr(["ocrvqa", "textvqa", "chartqa", "mathvista", "docvqa", "infovqa", "llavabench", "mmvet", "ocrbench"], dataset):
+        return "VQA"
+    else:
+        return "QA"

From 4ec1c585c0df534d8180fa902a47885b882b61de Mon Sep 17 00:00:00 2001
From: Yan Shu <570533048@qq.com>
Date: Mon, 8 Jul 2024 20:14:00 +0800
Subject: [PATCH 25/32] Add files via upload (#137)


From 77992c31929caaea9fbb3382becf905f8de3121d Mon Sep 17 00:00:00 2001
From: Bo Li <drluodian@gmail.com>
Date: Mon, 8 Jul 2024 15:41:06 +0000
Subject: [PATCH 26/32] Fix gen kwargs image aspect ratio in internvl2

---
 lmms_eval/models/internvl2.py                              | 3 ++-
 lmms_eval/tasks/llava_wilder/_default_template_wilder_yaml | 5 +----
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/lmms_eval/models/internvl2.py b/lmms_eval/models/internvl2.py
index 1f5a0185..5f4365d0 100644
--- a/lmms_eval/models/internvl2.py
+++ b/lmms_eval/models/internvl2.py
@@ -204,7 +204,7 @@ def generate_until(self, requests) -> List[str]:
         for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
             if "until" in gen_kwargs:
                 gen_kwargs.pop("until")
-            image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio", None)
+
             for k, v in DEFAULT_GEN_KWARGS.items():
                 if k not in gen_kwargs:
                     gen_kwargs[k] = v
@@ -220,6 +220,7 @@ def generate_until(self, requests) -> List[str]:
                     image_tokens = " ".join(image_tokens)
                     contexts = image_tokens + "\n" + contexts
                 response, history = self.model.chat(self.tokenizer, pixel_values, contexts, gen_kwargs, num_patches_list=num_patches_list, history=None, return_history=True)
+
             elif self.modality == "video":
                 assert len(visuals) == 1, f"Only one video is supported, but got {len(visuals)} videos."
                 video_path = visuals[0]
diff --git a/lmms_eval/tasks/llava_wilder/_default_template_wilder_yaml b/lmms_eval/tasks/llava_wilder/_default_template_wilder_yaml
index 37b744f5..356df525 100644
--- a/lmms_eval/tasks/llava_wilder/_default_template_wilder_yaml
+++ b/lmms_eval/tasks/llava_wilder/_default_template_wilder_yaml
@@ -3,11 +3,8 @@ doc_to_visual: !function utils.llava_doc_to_visual
 doc_to_text: !function utils.llava_doc_to_text
 doc_to_target: "gpt4v_answer"
 generation_kwargs:
-  until:
-    - "ASSISTANT:"
-  image_aspect_ratio: original
   max_new_tokens: 4096
-  temperature: 0
+  temperature: 0.7
   top_p: 1.0
   num_beams: 1
   do_sample: false

From e6844db1605cf093c6d7424723d37be6bbbb770b Mon Sep 17 00:00:00 2001
From: ZhangYuanhan-AI <yuanhan002@ntu.edu.sg>
Date: Tue, 9 Jul 2024 00:13:32 +0000
Subject: [PATCH 27/32] add vila

(cherry picked from commit 701d12e570c08f45a91b2700fd10dd349b5f683a)
---
 lmms_eval/models/__init__.py      |   1 +
 lmms_eval/models/llava_vid.py     |  16 +-
 lmms_eval/models/vila.py          | 376 ++++++++++++++++++++++++++++++
 lmms_eval/tasks/videomme/utils.py |   1 +
 pyproject.toml                    |  34 ++-
 5 files changed, 417 insertions(+), 11 deletions(-)
 create mode 100755 lmms_eval/models/vila.py

diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py
index 46fd92e8..b77adb48 100755
--- a/lmms_eval/models/__init__.py
+++ b/lmms_eval/models/__init__.py
@@ -37,6 +37,7 @@
     "llava_onevision": "Llava_OneVision",
     "llava_hf": "LlavaHf",
     "longva": "LongVA",
+    "vila": "VILA",
 }
 
 for model_name, model_class in AVAILABLE_MODELS.items():
diff --git a/lmms_eval/models/llava_vid.py b/lmms_eval/models/llava_vid.py
index 675c396e..14cd7e61 100755
--- a/lmms_eval/models/llava_vid.py
+++ b/lmms_eval/models/llava_vid.py
@@ -59,6 +59,8 @@ def __init__(
         mm_spatial_pool_mode: str = "average",
         overwrite: bool = True,
         video_decode_backend: str = "pyav",
+        delay_load: bool = False,
+        tie_weights: bool = True,
         **kwargs,
     ) -> None:
         super().__init__()
@@ -86,16 +88,19 @@ def __init__(
         self.mm_spatial_pool_out_channels = int(mm_spatial_pool_out_channels)
         self.mm_spatial_pool_mode = mm_spatial_pool_mode
         self.max_frames_num = int(max_frames_num)
-        print(self.max_frames_num)
+        self.mm_resampler_location = mm_resampler_location
+        self.delay_load = delay_load
         if self.overwrite == True:
             overwrite_config = {}
             overwrite_config["mm_resampler_type"] = self.mm_resampler_type
             overwrite_config["mm_spatial_pool_stride"] = self.mm_spatial_pool_stride
             overwrite_config["mm_spatial_pool_out_channels"] = self.mm_spatial_pool_out_channels
             overwrite_config["mm_spatial_pool_mode"] = self.mm_spatial_pool_mode
-            overwrite_config["mm_resampler_location"] = "before"
-            overwrite_config["patchify_video_feature"] = False
-            overwrite_config["attn_implementation"] = attn_implementation
+            overwrite_config["mm_pooling_position"] = self.mm_resampler_location
+            overwrite_config["mm_newline_position"] = mm_newline_position
+            overwrite_config["add_faster_video"] = False
+            overwrite_config["delay_load"] = self.delay_load
+            # overwrite_config["attn_implementation"] = attn_implementation
 
             cfg_pretrained = AutoConfig.from_pretrained(self.pretrained)
 
@@ -146,7 +151,8 @@ def __init__(
 
         self._config = self._model.config
         self.model.eval()
-        self.model.tie_weights()
+        if tie_weights:
+            self.model.tie_weights()
         self.truncation = truncation
         self.batch_size_per_gpu = int(batch_size)
         self.conv_template = conv_template
diff --git a/lmms_eval/models/vila.py b/lmms_eval/models/vila.py
new file mode 100755
index 00000000..295bf123
--- /dev/null
+++ b/lmms_eval/models/vila.py
@@ -0,0 +1,376 @@
+import argparse
+import torch
+import os
+import json
+from tqdm import tqdm
+import logging
+from typing import List, Optional, Union, Tuple
+from PIL import Image
+import math
+import numpy as np
+from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
+from accelerate.state import AcceleratorState
+from datetime import timedelta
+from decord import VideoReader, cpu
+
+
+from torchvision.transforms import Resize
+
+import signal
+
+from lmms_eval.api.instance import Instance
+from lmms_eval.api.model import lmms
+from lmms_eval.api.registry import register_model
+
+eval_logger = logging.getLogger("lmms-eval")
+# import sys;sys.path.append("llava-video")
+try:
+    from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+    from llava.conversation import conv_templates, SeparatorStyle
+    from llava.model.builder import load_pretrained_model
+    from llava.data.dataset import LazySupervisedDataset
+    from llava.utils import disable_torch_init
+    from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
+    from llava.mm_utils import process_images
+except ImportError as e:
+    print(e)
+    # import pdb;pdb.set_trace()
+    eval_logger.debug("VILA is not installed. Please install VILA to use this model.")
+
+
+@register_model("vila")
+class VILA(lmms):
+    """
+    VILA Model
+    """
+
+    def __init__(
+        self,
+        pretrained: str = "Efficient-Large-Model/VILA1.5-40b",
+        max_frames_num: Optional[int] = 100,
+        truncation: Optional[bool] = True,
+        device: Optional[str] = "cuda:0",
+        batch_size: Optional[Union[int, str]] = 1,
+        attn_implementation=(
+            "sdpa" if torch.__version__ >= "2.1.2" else "eager"
+        ),  # inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5
+        device_map="cuda:0",
+        conv_template="hermes-2",
+        use_cache=True,
+        truncate_context=False,  # whether to truncate the context in generation, set it False for LLaVA-1.6
+        video_decode_backend="decord",
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        assert kwargs == {}, f"Unexpected kwargs: {kwargs}"
+
+        accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52))
+        accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs])
+        if accelerator.num_processes > 1:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            self._device = torch.device(device)
+            self.device_map = device_map
+        else:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+
+        self.pretrained = pretrained
+        self.model_name = get_model_name_from_path(pretrained)
+        self.max_frames_num = max_frames_num
+        # self._config = AutoConfig.from_pretrained(self.pretrained)
+
+        # import pdb; pdb.set_trace()
+        self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, self.model_name, device_map=self.device_map, attn_implementation=attn_implementation)
+
+        self.model.image_processor = self._image_processor
+
+        self._config = self._model.config
+
+        if self._tokenizer.pad_token_id is None:
+            if "qwen" in self._tokenizer.name_or_path.lower():
+                print("Setting pad token to bos token for qwen model.")
+                self._tokenizer.pad_token_id = 151643
+
+        self.video_decode_backend = video_decode_backend
+        self.model.eval()
+        # self.model.tie_weights()
+        self.truncation = truncation
+        self.batch_size_per_gpu = int(batch_size)
+        self.conv_template = conv_template
+        self.use_cache = use_cache
+        self.truncate_context = truncate_context
+        # assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue."
+        if accelerator.num_processes > 1:
+            assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
+            # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
+            # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works
+            # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work.
+            if accelerator.distributed_type == DistributedType.DEEPSPEED:
+                kwargs = {
+                    "train_micro_batch_size_per_gpu": self.batch_size_per_gpu,
+                    "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes,
+                }
+                AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs)
+                eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0")
+            if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED:
+                self._model = accelerator.prepare(self.model)
+            else:
+                self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
+            self.accelerator = accelerator
+            if self.accelerator.is_local_main_process:
+                eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
+            self._rank = self.accelerator.local_process_index
+            self._world_size = self.accelerator.num_processes
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism")
+            self._rank = 0
+            self._word_size = 1
+        else:
+            eval_logger.info(f"Using single device: {self._device}")
+            self.model.to(self._device)
+            self._rank = 0
+            self._world_size = 1
+
+    @property
+    def config(self):
+        # return the associated transformers.AutoConfig for the given pretrained model.
+        return self._config
+
+    @property
+    def tokenizer(self):
+        return self._tokenizer
+
+    @property
+    def model(self):
+        # returns the model, unwrapping it if using Accelerate
+        if hasattr(self, "accelerator"):
+            return self.accelerator.unwrap_model(self._model)
+        else:
+            return self._model
+
+    @property
+    def eot_token_id(self):
+        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+        return self.tokenizer.eos_token_id
+
+    @property
+    def max_length(self):
+        return self._max_length
+
+    def pad_sequence(self, input_ids, batch_first, padding_value):
+        if self.tokenizer.padding_side == "left":
+            input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids]
+        input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value)
+        if self.tokenizer.padding_side == "left":
+            input_ids = torch.flip(input_ids, [1])
+        return input_ids
+
+    @property
+    def batch_size(self):
+        return self.batch_size_per_gpu
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def rank(self):
+        return self._rank
+
+    @property
+    def world_size(self):
+        return self._world_size
+
+    def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]:
+        """ """
+        add_special_tokens = False if add_special_tokens is None else add_special_tokens
+        encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
+        # left-truncate the encoded context to be at most `left_truncate_len` tokens long
+        if left_truncate_len:
+            encoding = encoding[-left_truncate_len:]
+        return encoding
+
+    def load_video(self, video_path, max_frames_num):
+        try:
+            vr = VideoReader(video_path, ctx=cpu(0))
+            total_frame_num = len(vr)
+            fps = round(vr.get_avg_fps())
+            frame_idx = np.linspace(0, total_frame_num - 2, max_frames_num, dtype=int)
+            spare_frames = vr.get_batch(frame_idx).asnumpy()
+            return [Image.fromarray(img) for img in spare_frames]
+        except Exception as e:
+            eval_logger.error(f"Failed to load video {video_path} with error: {e}")
+            # import pdb;pdb.set_trace()
+            return [Image.new("RGB", (448, 448), (0, 0, 0))] * max_frames_num
+
+    def tok_decode(self, tokens):
+        return self.tokenizer.decode(tokens)
+
+    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        res = []
+        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
+
+        for contexts, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
+            # encode, pad, and truncate contexts for this batch
+            if type(doc_to_target) == str:
+                continuation = doc_to_target
+            else:
+                continuation = doc_to_target(self.task_dict[task][split][doc_id])
+            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
+            visuals = self.flatten(visuals)
+            videos = []
+            for visual in visuals:
+                video = self.load_video(visual, self.max_frames_num)
+                video = self._image_processor.preprocess(video, return_tensors="pt")["pixel_values"].half().cuda()
+                videos.append(video)
+
+            qs = contexts
+            if self.model.config.mm_use_im_start_end:
+                qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + qs
+            else:
+                qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
+
+            conv = conv_templates[self.conv_template].copy()
+            conv.append_message(conv.roles[0], qs)
+            conv.append_message(conv.roles[1], None)
+            prompt = conv.get_prompt()
+
+            contxt_id = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device)
+
+            conv = conv_templates[self.conv_template].copy()
+            conv.append_message(conv.roles[0], qs)
+            conv.append_message(conv.roles[1], continuation)
+            prompt = conv.get_prompt()
+
+            input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).cuda()
+            attention_masks = input_ids.ne(self.tokenizer.pad_token_id).long().cuda()
+
+            labels = input_ids.clone()
+            # Context part no need to calculate for loss
+            labels[0, : contxt_id.shape[1]] = -100
+
+            with torch.inference_mode():
+                outputs = self.model(input_ids=input_ids, labels=labels, images=videos, modalities="video")
+
+            loss = outputs["loss"]
+            # loss = torch.exp(loss)
+            logits = outputs["logits"]
+            greedy_tokens = logits.argmax(dim=-1)
+            cont_toks = input_ids[:, contxt_id.shape[1] :]  # [1, seq]
+            greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : input_ids.shape[1]]  # [1, seq]
+            max_equal = (greedy_tokens == cont_toks).all()
+            res.append((float(loss.item()), bool(max_equal)))
+            pbar.update(1)
+        pbar.close()
+        return res
+
+    def flatten(self, input):
+        new_list = []
+        for i in input:
+            for j in i:
+                new_list.append(j)
+        return new_list
+
+    def generate_until(self, requests) -> List[str]:
+        res = []
+        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
+
+        for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
+            # if self.task_dict[task][split][doc_id]["duration"] != "short":
+            #     # import pdb;pdb.set_trace()
+            #     res.append("A")
+            #     pbar.update(1)
+            #     continue
+            # encode, pad, and truncate contexts for this batch
+            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
+            visuals = self.flatten(visuals)
+
+            num_video_frames = self.model.config.num_video_frames
+            videos = []
+            # import pdb;pdb.set_trace()
+            if self.max_frames_num == 0:
+                images = [Image.new("RGB", (448, 448), (0, 0, 0))] * num_video_frames
+                video = process_images(images, self.model.image_processor, self.model.config).half().cuda()
+                videos.append(video)
+            else:
+                for visual in visuals:
+                    # images, video_loading_succeed = LazySupervisedDataset._load_video(visual, num_video_frames, self.model)
+                    # import pdb;pdb.set_trace()
+                    if self.video_decode_backend == "decord":
+                        images = self.load_video(visual, num_video_frames)
+                    elif self.video_decode_backend == "pyav":
+                        images = read_video_pyav(visual, num_frm=num_video_frames)
+                    # import pdb;pdb.set_trace()
+                    video = process_images(images, self.model.image_processor, self.model.config).half().cuda()
+                    videos.append(video)
+
+            qs = f"<video>\n {contexts}"
+            if self.model.config.mm_use_im_start_end:
+                qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + qs
+            else:
+                qs = (DEFAULT_IMAGE_TOKEN + "\n") * len(images) + qs
+
+            # This is much safer for llama3, as we now have some object type in it
+            # if "llama_3" in self.conv_template:
+            #     conv = copy.deepcopy(conv_templates[self.conv_template])
+            # else:
+            #     conv = conv_templates[self.conv_template].copy()
+            conv = conv_templates[self.conv_template].copy()
+
+            conv.append_message(conv.roles[0], qs)
+            conv.append_message(conv.roles[1], None)
+            prompt = conv.get_prompt()
+
+            input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).cuda()
+            pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
+            # if "llama_3" in self.conv_template:
+            #     pad_token_ids = 0  # lmms-lab/llama3-llava-8b is trained on this pad token id. You may need to customize this for other models.
+            attention_masks = input_ids.ne(pad_token_ids).long().cuda()
+
+            # input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input]
+            # pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
+            # input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device)
+            # attention_masks = input_ids.ne(pad_token_ids).to(self.device)
+
+            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+            keywords = [stop_str]
+
+            stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids)
+
+            cur_prompt = contexts
+
+            if "max_new_tokens" not in gen_kwargs:
+                gen_kwargs["max_new_tokens"] = 1024
+            if "temperature" not in gen_kwargs:
+                gen_kwargs["temperature"] = 0.2
+            if "top_p" not in gen_kwargs:
+                gen_kwargs["top_p"] = None
+            if "num_beams" not in gen_kwargs:
+                gen_kwargs["num_beams"] = 1
+
+            # import pdb;pdb.set_trace()
+            with torch.inference_mode():
+                output_ids = self.model.generate(
+                    input_ids=input_ids,
+                    images=videos,
+                    attention_mask=attention_masks,
+                    use_cache=self.use_cache,
+                    stopping_criteria=[stopping_criteria],
+                    do_sample=True if gen_kwargs["temperature"] > 0 else False,
+                    temperature=gen_kwargs["temperature"],
+                    top_p=gen_kwargs["top_p"],
+                    num_beams=gen_kwargs["num_beams"],
+                    max_new_tokens=gen_kwargs["max_new_tokens"],
+                )
+                # output_ids_2 = self.model.generate(inputs=input_ids, images=videos, attention_mask=attention_masks, modalities="video", do_sample=False, max_new_tokens=50,stopping_criteria=[stopping_criteria])
+                # output_ids = self.model.generate(inputs=input_ids, images=videos, attention_mask=attention_masks, modalities="video", do_sample=True, temperature=0.2, max_new_tokens=50,use_cache=True)
+
+            outputs = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
+            print("Question: ", cur_prompt)
+            print("Answer: ", outputs)
+            # import pdb;pdb.set_trace()
+            res.append(outputs)
+            pbar.update(1)
+        return res
diff --git a/lmms_eval/tasks/videomme/utils.py b/lmms_eval/tasks/videomme/utils.py
index b70812a0..401b02a0 100644
--- a/lmms_eval/tasks/videomme/utils.py
+++ b/lmms_eval/tasks/videomme/utils.py
@@ -152,6 +152,7 @@ def videomme_doc_to_text(doc, model_specific_prompt_kwargs=None):
     option_prompt = "Select the best answer to the following multiple-choice question based on the video and the subtitles. Respond with only the letter (A, B, C, or D) of the correct option."
     question = doc["question"]
     option = str(doc["options"])
+    # option = "\n".join([f"{opt}" for i, opt in enumerate(doc["options"])])
     question = question + "\n" + option
     full_prompt = option_prompt + "\n" + question + "\n" + "The best answer is:"
     return full_prompt
diff --git a/pyproject.toml b/pyproject.toml
index 6e6ebe00..726ad164 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,17 +32,25 @@ dependencies = [
     "peft>=0.2.0",
     "pybind11>=2.6.2",
     "pytablewriter",
-    "rouge-score>=0.0.4",
     "sacrebleu>=1.5.0",
     "scikit-learn>=0.24.1",
     "sqlitedict",
     "torch>=2.1.0", # to enable sdpa mode for running 34B model on one 80GB GPU
-    "openai>=1.0.0",
+    "torchvision>=0.16.0",
+    "timm",
+    "einops",
+    "ftfy",
+    "openai",
+    "opencv-python-headless",
+    "av",
+    "hf_transfer",
+    "pywsd",
+    "nltk",
+    "sentencepiece==0.1.99",
     "yt-dlp",
-    "google-generativeai",
     "pycocoevalcap",
     "tqdm-multiprocess",
-    "transformers>=4.37.2",
+    "transformers==4.39.2",
     "transformers-stream-generator",
     "zstandard",
     "pillow",
@@ -63,9 +71,23 @@ dependencies = [
     "decord",
     "zss",
     "pywsd",
+    "spacy",
+    "anls",
     "rouge",
 ]
 
+[project.optional-dependencies]
+vila = [
+    "s2wrapper@git+https://github.com/bfshi/scaling_on_scales"
+]
+gemini = [
+    "google-generativeai",
+]
+all = [
+    "vila",
+    "gemini",
+]
+
 [tool.setuptools.packages.find]
 include = ["lmms_eval*"]
 exclude = [
@@ -107,5 +129,5 @@ exclude = [
 lmms-eval = "lmms_eval.__main__:cli_evaluate"
 
 [project.urls]
-Homepage = "https://lmms-lab.github.io/lmms-eval-blog/"
-Repository = "https://github.com/EvolvingLMMs-Lab/lmms-eval"
+Homepage = "https://lmms-lab.github.io"
+Repository = "https://github.com/EvolvingLMMs-Lab/lmms-eval"
\ No newline at end of file

From 672d7e5bb49dcb34e1b2fdeb09f3f4588dc583a6 Mon Sep 17 00:00:00 2001
From: Bo Li <drluodian@gmail.com>
Date: Tue, 9 Jul 2024 11:43:41 +1000
Subject: [PATCH 28/32] feat: Add tie_weights parameter to Llava model
 initialization

---
 lmms_eval/models/llava.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/lmms_eval/models/llava.py b/lmms_eval/models/llava.py
index 7528e356..7d6420ba 100755
--- a/lmms_eval/models/llava.py
+++ b/lmms_eval/models/llava.py
@@ -58,6 +58,7 @@ def __init__(
         device_map="cuda:0",
         conv_template="vicuna_v1",
         use_cache=True,
+        tie_weights: bool = True,
         truncate_context=False,  # whether to truncate the context in generation, set it False for LLaVA-1.6
         customized_config=None,  # ends in json
         **kwargs,
@@ -97,6 +98,8 @@ def __init__(
             self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args)
         self._config = self._model.config
         self.model.eval()
+        if tie_weights:
+            self.model.tie_weights()
 
         self.truncation = truncation
         self.batch_size_per_gpu = int(batch_size)

From cd1858523fcd8630082cbefba8710e0de3ee8805 Mon Sep 17 00:00:00 2001
From: Bo Li <drluodian@gmail.com>
Date: Tue, 9 Jul 2024 11:52:23 +1000
Subject: [PATCH 29/32] Upgrade lmms-eval to support more models and evaluation
 tasks

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 2bf7a26e..b9fb9474 100755
--- a/README.md
+++ b/README.md
@@ -12,6 +12,8 @@
 
 ## Annoucement
 
+- [2024-07] 👨‍💻👨‍💻 The `lmms-eval/v0.2.1` has been upgraded to support more models, including [LongVA](https://github.com/EvolvingLMMs-Lab/LongVA), [InterVL-2](https://github.com/OpenGVLab/InternVL), [VILA](https://github.com/NVlabs/VILA), and many more evaluation tasks, e.g. [Details Captions](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/136), [MLVU](https://arxiv.org/abs/2406.04264), [WildVision-Bench](https://huggingface.co/datasets/WildVision/wildvision-arena-data), [VITATECS](https://github.com/lscpku/VITATECS) and [LLaVA-Interleave-Bench](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/).
+
 - [2024-06] 🎬🎬 The `lmms-eval/v0.2` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details
 
 - [2024-03] 📝📝 We have released the first version of `lmms-eval`, please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.1/) for more details

From 6da76f36ecf5f9aa73057e767a4fcb60c99ff896 Mon Sep 17 00:00:00 2001
From: Bo Li <drluodian@gmail.com>
Date: Tue, 9 Jul 2024 11:55:56 +1000
Subject: [PATCH 30/32] Upgrade lmms-eval to version 0.2.1

---
 README.md      | 2 +-
 pyproject.toml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index b9fb9474..7be1c6c7 100755
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 
 - [2024-07] 👨‍💻👨‍💻 The `lmms-eval/v0.2.1` has been upgraded to support more models, including [LongVA](https://github.com/EvolvingLMMs-Lab/LongVA), [InterVL-2](https://github.com/OpenGVLab/InternVL), [VILA](https://github.com/NVlabs/VILA), and many more evaluation tasks, e.g. [Details Captions](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/136), [MLVU](https://arxiv.org/abs/2406.04264), [WildVision-Bench](https://huggingface.co/datasets/WildVision/wildvision-arena-data), [VITATECS](https://github.com/lscpku/VITATECS) and [LLaVA-Interleave-Bench](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/).
 
-- [2024-06] 🎬🎬 The `lmms-eval/v0.2` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details
+- [2024-06] 🎬🎬 The `lmms-eval/v0.2.0` has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for more details
 
 - [2024-03] 📝📝 We have released the first version of `lmms-eval`, please refer to the [blog](https://lmms-lab.github.io/posts/lmms-eval-0.1/) for more details
 
diff --git a/pyproject.toml b/pyproject.toml
index aa0afd04..da2f2120 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "lmms_eval"
-version = "0.2.0.post1"
+version = "0.2.1"
 authors = [
     { name = "LMMMs-Lab Evaluation Team", email = "lmms_eval@outlook.com" },
 ]

From 1d8c980d1089f9d7702c3b92d5c85039f2809c6d Mon Sep 17 00:00:00 2001
From: kcz358 <kaichenzhang358@outlook.com>
Date: Tue, 9 Jul 2024 02:08:52 +0000
Subject: [PATCH 31/32] Rename xcomposer 4KHD

---
 lmms_eval/models/{xcomposer2_4khd.py => xcomposer2_4KHD.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename lmms_eval/models/{xcomposer2_4khd.py => xcomposer2_4KHD.py} (100%)

diff --git a/lmms_eval/models/xcomposer2_4khd.py b/lmms_eval/models/xcomposer2_4KHD.py
similarity index 100%
rename from lmms_eval/models/xcomposer2_4khd.py
rename to lmms_eval/models/xcomposer2_4KHD.py

From e31cd7883d4555c7530795c7f102b8d78cbd372f Mon Sep 17 00:00:00 2001
From: Bo Li <drluodian@gmail.com>
Date: Wed, 10 Jul 2024 12:08:08 +1000
Subject: [PATCH 32/32] chore: Update lmms_eval/models/vila.py and
 lmms_eval/tasks/__init__.py

---
 lmms_eval/models/vila.py    | 18 +++++++++---------
 lmms_eval/tasks/__init__.py |  2 +-
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/lmms_eval/models/vila.py b/lmms_eval/models/vila.py
index 295bf123..2adff620 100755
--- a/lmms_eval/models/vila.py
+++ b/lmms_eval/models/vila.py
@@ -34,8 +34,8 @@
     from llava.mm_utils import process_images
 except ImportError as e:
     print(e)
-    # import pdb;pdb.set_trace()
-    eval_logger.debug("VILA is not installed. Please install VILA to use this model.")
+    
+    eval_logger.debug("VILA is not installed. Please install VILA to use this model. Error: {e}")
 
 
 @register_model("vila")
@@ -202,7 +202,7 @@ def load_video(self, video_path, max_frames_num):
             return [Image.fromarray(img) for img in spare_frames]
         except Exception as e:
             eval_logger.error(f"Failed to load video {video_path} with error: {e}")
-            # import pdb;pdb.set_trace()
+            
             return [Image.new("RGB", (448, 448), (0, 0, 0))] * max_frames_num
 
     def tok_decode(self, tokens):
@@ -279,7 +279,7 @@ def generate_until(self, requests) -> List[str]:
 
         for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
             # if self.task_dict[task][split][doc_id]["duration"] != "short":
-            #     # import pdb;pdb.set_trace()
+            #     
             #     res.append("A")
             #     pbar.update(1)
             #     continue
@@ -289,7 +289,7 @@ def generate_until(self, requests) -> List[str]:
 
             num_video_frames = self.model.config.num_video_frames
             videos = []
-            # import pdb;pdb.set_trace()
+            
             if self.max_frames_num == 0:
                 images = [Image.new("RGB", (448, 448), (0, 0, 0))] * num_video_frames
                 video = process_images(images, self.model.image_processor, self.model.config).half().cuda()
@@ -297,12 +297,12 @@ def generate_until(self, requests) -> List[str]:
             else:
                 for visual in visuals:
                     # images, video_loading_succeed = LazySupervisedDataset._load_video(visual, num_video_frames, self.model)
-                    # import pdb;pdb.set_trace()
+                    
                     if self.video_decode_backend == "decord":
                         images = self.load_video(visual, num_video_frames)
                     elif self.video_decode_backend == "pyav":
                         images = read_video_pyav(visual, num_frm=num_video_frames)
-                    # import pdb;pdb.set_trace()
+                    
                     video = process_images(images, self.model.image_processor, self.model.config).half().cuda()
                     videos.append(video)
 
@@ -350,7 +350,7 @@ def generate_until(self, requests) -> List[str]:
             if "num_beams" not in gen_kwargs:
                 gen_kwargs["num_beams"] = 1
 
-            # import pdb;pdb.set_trace()
+            
             with torch.inference_mode():
                 output_ids = self.model.generate(
                     input_ids=input_ids,
@@ -370,7 +370,7 @@ def generate_until(self, requests) -> List[str]:
             outputs = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
             print("Question: ", cur_prompt)
             print("Answer: ", outputs)
-            # import pdb;pdb.set_trace()
+            
             res.append(outputs)
             pbar.update(1)
         return res
diff --git a/lmms_eval/tasks/__init__.py b/lmms_eval/tasks/__init__.py
index 22912ec9..4bb2e01d 100755
--- a/lmms_eval/tasks/__init__.py
+++ b/lmms_eval/tasks/__init__.py
@@ -72,7 +72,7 @@ def include_task_folder(task_dir: str, register_task: bool = True) -> None:
         # if (subdirs == [] or subdirs == ["__pycache__"]) and (len(file_list) > 0):
         for f in file_list:
             # if "detail" in f:
-            #     import pdb;pdb.set_trace()
+            #     
             # if "vatex" in f:
             #     print("a")
             if f.endswith(".yaml"):