Support Inference of MiniCPM-V-2.6 and MiniCPM-o-2.6

setup.py

@@ -68,6 +68,7 @@ def get_console_scripts() -> List[str]:
         "msgpack",
         "referencing",
         "jsonschema_specifications",
+        "librosa",
     ],
     "modelscope": ["modelscope"],
     "openmind": ["openmind"],

src/llamafactory/chat/hf_engine.py

@@ -168,6 +168,8 @@ def _process_args(
         for key, value in mm_inputs.items():
             if isinstance(value, list) and all(isinstance(v, torch.Tensor) for v in value):  # for pixtral inputs
                 value = torch.stack(value)  # assume they have same sizes
+            elif isinstance(value, list) and all(isinstance(v, list) for v in value):  # for minicpmv inputs
+                value = torch.stack([torch.stack(per_value) for per_value in value])
             elif not isinstance(value, torch.Tensor):
                 value = torch.tensor(value)
 
@@ -176,6 +178,11 @@ def _process_args(
 
             gen_kwargs[key] = value.to(model.device)
 
+        if getattr(model.config, "model_type", None) in ["minicpmv", "minicpmo"]:
+            gen_kwargs["input_ids"] = inputs
+            del gen_kwargs["image_sizes"]
+            gen_kwargs["tokenizer"] = tokenizer
+
         return gen_kwargs, prompt_length
 
     @staticmethod
@@ -207,6 +214,9 @@ def _chat(
             input_kwargs,
         )
         generate_output = model.generate(**gen_kwargs)
+        if isinstance(generate_output, tuple):
+            generate_output = generate_output[1][0]  # for minicpm_o
+
         response_ids = generate_output[:, prompt_length:]
         response = tokenizer.batch_decode(
             response_ids, skip_special_tokens=generating_args["skip_special_tokens"], clean_up_tokenization_spaces=True

src/llamafactory/model/model_utils/visual.py

@@ -252,6 +252,13 @@ def patch_target_modules(
 
 _register_composite_model(
     model_type="minicpmv",
+    vision_model_keys=["vpm"],
+    language_model_keys=["llm"],
+)
+
+
+_register_composite_model(
+    model_type="minicpmo",
     vision_model_keys=["vpm", "apm", "resampler", "tts"],
     language_model_keys=["llm"],
 )

src/llamafactory/model/patcher.py

@@ -109,6 +109,10 @@ def patch_config(
 
     if getattr(config, "model_type", None) == "qwen2" and is_trainable and model_args.flash_attn == "fa2":
         setattr(config, "use_cache", False)  # qwen2 does not support use_cache when using flash attn
+
+    if getattr(config, "model_type", None) == "minicpmo":
+        setattr(config, "init_audio", False)
+        setattr(config, "init_tts", False)
 
     if "LlavaLlamaForCausalLM" in getattr(config, "architectures", []):
         raise ValueError("Please download llava models with hf-compatible format: https://huggingface.co/llava-hf")
@@ -145,7 +149,9 @@ def patch_model(
     ):
         gen_config.do_sample = True
 
-    if "GenerationMixin" not in str(model.generate.__func__):
+    if getattr(model.config, "model_type") not in ["minicpmv", "minicpmo"] and "GenerationMixin" not in str(
+        model.generate.__func__
+    ):
         model.generate = MethodType(PreTrainedModel.generate, model)
 
     if add_valuehead: