diff --git a/.gitignore b/.gitignore
index 68bc17f9..c4f65693 100644
--- a/.gitignore
+++ b/.gitignore
@@ -158,3 +158,5 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+**/runpod
\ No newline at end of file
diff --git a/examples/Alpaca_Mixtral.ipynb b/examples/Alpaca_Mixtral.ipynb
new file mode 100644
index 00000000..de5c7655
--- /dev/null
+++ b/examples/Alpaca_Mixtral.ipynb
@@ -0,0 +1,219 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4b2a910-40ce-48f9-91b6-11d5eec547f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('/workspace/unsloth')\n",
+    "from unsloth.models.mixtral import FastMixtralModel\n",
+    "from unsloth import FastLanguageModel\n",
+    "import torch\n",
+    "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
+    "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
+    "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
+    "\n",
+    "model, tokenizer = FastMixtralModel.from_pretrained(\n",
+    "    model_name = \"mistralai/Mixtral-8x7B-v0.1\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\n",
+    "    max_seq_length = max_seq_length,\n",
+    "    dtype = dtype,\n",
+    "    load_in_4bit = load_in_4bit,\n",
+    "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77182aa0-762f-4e80-bdf8-8af785fc6f97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = FastMixtralModel.get_peft_model(\n",
+    "    model,\n",
+    "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
+    "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"],\n",
+    "                      # \"gate\", \"w1\", \"w2\", \"w3\"],\n",
+    "    lora_alpha = 16,\n",
+    "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
+    "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
+    "    use_gradient_checkpointing = True,\n",
+    "    random_state = 3407,\n",
+    "    use_rslora = False,  # We support rank stabilized LoRA\n",
+    "    loftq_config = None, # And LoftQ\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4847ef2c-cdf5-4905-899d-9fc331fde245",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
+    "\n",
+    "### Instruction:\n",
+    "{}\n",
+    "\n",
+    "### Input:\n",
+    "{}\n",
+    "\n",
+    "### Response:\n",
+    "{}\"\"\"\n",
+    "\n",
+    "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
+    "def formatting_prompts_func(examples):\n",
+    "    instructions = examples[\"instruction\"]\n",
+    "    inputs       = examples[\"input\"]\n",
+    "    outputs      = examples[\"output\"]\n",
+    "    texts = []\n",
+    "    for instruction, input, output in zip(instructions, inputs, outputs):\n",
+    "        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
+    "        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n",
+    "        texts.append(text)\n",
+    "    return { \"text\" : texts, }\n",
+    "pass\n",
+    "\n",
+    "from datasets import load_dataset\n",
+    "dataset = load_dataset(\"yahma/alpaca-cleaned\", split = \"train\")\n",
+    "dataset = dataset.map(formatting_prompts_func, batched = True,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "508de7d8-64be-407d-8899-f8c737ed3650",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from trl import SFTTrainer\n",
+    "from transformers import TrainingArguments\n",
+    "\n",
+    "trainer = SFTTrainer(\n",
+    "    model = model,\n",
+    "    tokenizer = tokenizer,\n",
+    "    train_dataset = dataset,\n",
+    "    dataset_text_field = \"text\",\n",
+    "    max_seq_length = max_seq_length,\n",
+    "    dataset_num_proc = 2,\n",
+    "    packing = False, # Can make training 5x faster for short sequences.\n",
+    "    args = TrainingArguments(\n",
+    "        per_device_train_batch_size = 2,\n",
+    "        gradient_accumulation_steps = 4,\n",
+    "        warmup_steps = 5,\n",
+    "        max_steps = 60,\n",
+    "        learning_rate = 2e-4,\n",
+    "        fp16 = not torch.cuda.is_bf16_supported(),\n",
+    "        bf16 = torch.cuda.is_bf16_supported(),\n",
+    "        logging_steps = 1,\n",
+    "        optim = \"adamw_8bit\",\n",
+    "        weight_decay = 0.01,\n",
+    "        lr_scheduler_type = \"linear\",\n",
+    "        seed = 3407,\n",
+    "        output_dir = \"outputs\",\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e33a6e5-a8b9-402c-8419-10c3e969a561",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#@title Show current memory stats\n",
+    "gpu_stats = torch.cuda.get_device_properties(0)\n",
+    "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
+    "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
+    "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
+    "print(f\"{start_gpu_memory} GB of memory reserved.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9406a6c0-ce05-4c1b-bbb1-60ed2b3a7418",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer_stats = trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47f6cd90-4b6a-4165-a934-ee8d630d1f9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#@title Show final memory and time stats\n",
+    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
+    "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
+    "used_percentage = round(used_memory         /max_memory*100, 3)\n",
+    "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
+    "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
+    "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
+    "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
+    "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
+    "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
+    "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e13e692-e4f7-46d1-9df0-870695fd7e9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# alpaca_prompt = Copied from above\n",
+    "FastMixtralModel.for_inference(model) # Enable native 2x faster inference\n",
+    "inputs = tokenizer(\n",
+    "[\n",
+    "    alpaca_prompt.format(\n",
+    "        \"Continue the fibonnaci sequence.\", # instruction\n",
+    "        \"1, 1, 2, 3, 5, 8\", # input\n",
+    "        \"\", # output - leave this blank for generation!\n",
+    "    )\n",
+    "], return_tensors = \"pt\").to(\"cuda\")\n",
+    "\n",
+    "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
+    "tokenizer.batch_decode(outputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09978b57-d549-4888-aaba-459abb683545",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/DPO_Mixtral_Unsloth_Example.ipynb b/examples/DPO_Mixtral_Unsloth_Example.ipynb
deleted file mode 100644
index ee7c61f2..00000000
--- a/examples/DPO_Mixtral_Unsloth_Example.ipynb
+++ /dev/null
@@ -1,8661 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "o6RxqIirisTj"
-      },
-      "source": [
-        "To run this, press \"Runtime\" and press \"Run all\" on a **free** Tesla T4 Google Colab instance!\n",
-        "<div class=\"align-center\">\n",
-        "  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
-        "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
-        "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Join our Discord if you need help!\n",
-        "</div>\n",
-        "\n",
-        "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n",
-        "\n",
-        "You will learn how to do [DPO data prep](#Data), and how to [train via `DPOTrainer`](#Train).\n",
-        "To learn more about DPO, read TRL's [blog post](https://huggingface.co/blog/dpo-trl). We follow [Huggingface's Alignment Handbook](https://github.com/huggingface/alignment-handbook) to replicate [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2eSvM9zX_2d3"
-      },
-      "outputs": [],
-      "source": [
-        "%%capture\n",
-        "import torch\n",
-        "major_version, minor_version = torch.cuda.get_device_capability()\n",
-        "# if major_version >= 8:\n",
-        "#     # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n",
-        "#     %pip install \"unsloth[colab_ampere] @ git+https://github.com/unslothai/unsloth.git\"\n",
-        "# else:\n",
-        "#     # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n",
-        "#     %pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n",
-        "# pass"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QYds3fcii6gC"
-      },
-      "source": [
-        "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n",
-        "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n",
-        "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n",
-        "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n",
-        "* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n",
-        "* DPO requires a model already trained by SFT on a similar dataset that is used for DPO. We use `HuggingFaceH4/mistral-7b-sft-beta` as the SFT model. Use this [notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) first to train a SFT model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "E8-BWi7MzkRz",
-        "outputId": "0626186f-6e70-47b7-db59-b8e7591d0de4"
-      },
-      "outputs": [],
-      "source": [
-        "# One must patch the DPO Trainer first!\n",
-        "import sys\n",
-        "sys.path.append('/workspace/unsloth')\n",
-        "from unsloth import PatchDPOTrainer\n",
-        "from unsloth.models.mixtral import FastMixtralModel\n",
-        "PatchDPOTrainer()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 491,
-          "referenced_widgets": [
-            "4c7a9e6327fc4b839f1e2eee705e1574",
-            "904fef1ac7404cc8a33e65ffe8938c8b",
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-            "96f9847627504ae7abe7461e4c0b3cff",
-            "5165aa69bef148af9717d90cc5a870b1",
-            "e6dfcf0456824556ad09fe7945674a14",
-            "80568bca0c604eaaab2933fc76da2bf0",
-            "079284acd5c6454eb03d8876a62e6f12",
-            "1d917d774780421fbdca680009a706ac",
-            "342cf2a9d6674b4d94fdbaf3b63b9b79",
-            "16ace0251b954bc08a33eaf3b2a47216",
-            "2cb8afe9c6e2441fa4e232146ecd08b4",
-            "bc745fa3da114007af35ea15dbd7ab7d",
-            "127ed424448840cea8b14fc9997b9cde",
-            "803f5904c00d4cde8729226139e3c258",
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-            "960bd84fc3fe4d8eb7e79ecba0f3edf6",
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-            "0dc6aefaa24043768d656f5a61d79545",
-            "d27056ab29e04c448fc62f9f18cbb82d",
-            "926319902fe84c5095e0b5bd4b3e8187",
-            "9f660e1b7b9c4ae7922d51353783acf9",
-            "82d00942a39e465c8a6f869ef97cc250",
-            "b9e2b829291247619a7b37c9f544d395",
-            "3f6ccae685e34d05be9fde5538dc48cf",
-            "3df8638084104cb7a0530a96ec6201af",
-            "06d1fd85a45541609d11ec7f0bd3b444",
-            "a9d05a8c3aaf4fe0a2f061c3eeb48b44",
-            "8095a44a62804d6d86c24d62b9e1a1c1",
-            "f61b85b63d5147db9f1040f30f4a3034",
-            "e152d696872242ab8e0bc00ce9b50142",
-            "623e912cfe404e69a64992fbb1c1bc47",
-            "69f9e4bf820047658699d55d84d682c0",
-            "a8306ce933304694aba188495743c2f4",
-            "1cd708f679de4d319af26797aecca94d"
-          ]
-        },
-        "id": "QmUBVEnvCDJv",
-        "outputId": "f04a3ea4-0d0f-424d-8265-0fb99fd17ef3"
-      },
-      "outputs": [],
-      "source": [
-        "from unsloth import FastLanguageModel\n",
-        "import torch\n",
-        "max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally!\n",
-        "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
-        "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
-        "\n",
-        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
-        "    model_name = \"ybelkada/Mixtral-8x7B-Instruct-v0.1-bnb-4bit\", # Choose ANY! eg mistralai/Mistral-7B-Instruct-v0.2\n",
-        "    max_seq_length = max_seq_length,\n",
-        "    dtype = dtype,\n",
-        "    load_in_4bit = load_in_4bit,\n",
-        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "for name, module in model.named_modules():\n",
-        "    print(\"------------------------------------------------\")\n",
-        "    print(name, module)\n",
-        "    print(\"------------------------------------------------\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "cellView": "form",
-        "id": "AqkY_wHdKyOl"
-      },
-      "outputs": [],
-      "source": [
-        "#@title Alignment Handbook utils\n",
-        "import os\n",
-        "import re\n",
-        "from typing import List, Literal, Optional\n",
-        "\n",
-        "from datasets import DatasetDict, concatenate_datasets, load_dataset, load_from_disk\n",
-        "from datasets.builder import DatasetGenerationError\n",
-        "\n",
-        "\n",
-        "DEFAULT_CHAT_TEMPLATE = \"{% for message in messages %}\\n{% if message['role'] == 'user' %}\\n{{ '<|user|>\\n' + message['content'] + eos_token }}\\n{% elif message['role'] == 'system' %}\\n{{ '<|system|>\\n' + message['content'] + eos_token }}\\n{% elif message['role'] == 'assistant' %}\\n{{ '<|assistant|>\\n'  + message['content'] + eos_token }}\\n{% endif %}\\n{% if loop.last and add_generation_prompt %}\\n{{ '<|assistant|>' }}\\n{% endif %}\\n{% endfor %}\"\n",
-        "\n",
-        "\n",
-        "def apply_chat_template(\n",
-        "    example, tokenizer, task: Literal[\"sft\", \"generation\", \"rm\", \"dpo\"] = \"sft\", assistant_prefix=\"<|assistant|>\\n\"\n",
-        "):\n",
-        "    def _strip_prefix(s, pattern):\n",
-        "        # Use re.escape to escape any special characters in the pattern\n",
-        "        return re.sub(f\"^{re.escape(pattern)}\", \"\", s)\n",
-        "\n",
-        "    if task in [\"sft\", \"generation\"]:\n",
-        "        messages = example[\"messages\"]\n",
-        "        # We add an empty system message if there is none\n",
-        "        if messages[0][\"role\"] != \"system\":\n",
-        "            messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n",
-        "        example[\"text\"] = tokenizer.apply_chat_template(\n",
-        "            messages, tokenize=False, add_generation_prompt=True if task == \"generation\" else False\n",
-        "        )\n",
-        "    elif task == \"rm\":\n",
-        "        if all(k in example.keys() for k in (\"chosen\", \"rejected\")):\n",
-        "            chosen_messages = example[\"chosen\"]\n",
-        "            rejected_messages = example[\"rejected\"]\n",
-        "            # We add an empty system message if there is none\n",
-        "            if chosen_messages[0][\"role\"] != \"system\":\n",
-        "                chosen_messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n",
-        "            if rejected_messages[0][\"role\"] != \"system\":\n",
-        "                rejected_messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n",
-        "            example[\"text_chosen\"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False)\n",
-        "            example[\"text_rejected\"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False)\n",
-        "        else:\n",
-        "            raise ValueError(\n",
-        "                f\"Could not format example as dialogue for `rm` task! Require `[chosen, rejected]` keys but found {list(example.keys())}\"\n",
-        "            )\n",
-        "    elif task == \"dpo\":\n",
-        "        if all(k in example.keys() for k in (\"chosen\", \"rejected\")):\n",
-        "            # Compared to reward modeling, we filter out the prompt, so the text is everything after the last assistant token\n",
-        "            prompt_messages = [[msg for msg in example[\"chosen\"] if msg[\"role\"] == \"user\"][0]]\n",
-        "            # Insert system message\n",
-        "            if example[\"chosen\"][0][\"role\"] != \"system\":\n",
-        "                prompt_messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n",
-        "            else:\n",
-        "                prompt_messages.insert(0, example[\"chosen\"][0])\n",
-        "            # TODO: handle case where chosen/rejected also have system messages\n",
-        "            chosen_messages = example[\"chosen\"][1:]\n",
-        "            rejected_messages = example[\"rejected\"][1:]\n",
-        "            example[\"text_chosen\"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False)\n",
-        "            example[\"text_rejected\"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False)\n",
-        "            example[\"text_prompt\"] = tokenizer.apply_chat_template(\n",
-        "                prompt_messages, tokenize=False, add_generation_prompt=True\n",
-        "            )\n",
-        "            example[\"text_chosen\"] = _strip_prefix(example[\"text_chosen\"], assistant_prefix)\n",
-        "            example[\"text_rejected\"] = _strip_prefix(example[\"text_rejected\"], assistant_prefix)\n",
-        "        else:\n",
-        "            raise ValueError(\n",
-        "                f\"Could not format example as dialogue for `dpo` task! Require `[chosen, rejected]` keys but found {list(example.keys())}\"\n",
-        "            )\n",
-        "    else:\n",
-        "        raise ValueError(\n",
-        "            f\"Task {task} not supported, please ensure that the provided task is one of {['sft', 'generation', 'rm', 'dpo']}\"\n",
-        "        )\n",
-        "    return example\n",
-        "\n",
-        "\n",
-        "def get_datasets(\n",
-        "    data_config: dict,\n",
-        "    splits: List[str] = [\"train\", \"test\"],\n",
-        "    shuffle: bool = True,\n",
-        ") -> DatasetDict:\n",
-        "    \"\"\"\n",
-        "    Loads one or more datasets with varying training set proportions.\n",
-        "\n",
-        "    Args:\n",
-        "        data_config (`DataArguments` or `dict`):\n",
-        "            Dataset configuration and split proportions.\n",
-        "        splits (`List[str]`, *optional*, defaults to `['train', 'test']`):\n",
-        "            Dataset splits to load and mix. Assumes the splits exist in all datasets and have a `train_` or `test_` prefix.\n",
-        "        shuffle (`bool`, *optional*, defaults to `True`):\n",
-        "            Whether to shuffle the training and testing/validation data.\n",
-        "\n",
-        "    Returns\n",
-        "        [`DatasetDict`]: The dataset dictionary containing the loaded datasets.\n",
-        "    \"\"\"\n",
-        "\n",
-        "    if type(data_config) is dict:\n",
-        "        # Structure of the input is:\n",
-        "        #     dataset_mixer = {\n",
-        "        #             \"dataset1\": 0.5,\n",
-        "        #             \"dataset1\": 0.3,\n",
-        "        #             \"dataset1\": 0.2,\n",
-        "        #         }\n",
-        "        dataset_mixer = data_config\n",
-        "    else:\n",
-        "        raise ValueError(f\"Data config {data_config} not recognized.\")\n",
-        "\n",
-        "    raw_datasets = mix_datasets(dataset_mixer, splits=splits, shuffle=shuffle)\n",
-        "    return raw_datasets\n",
-        "\n",
-        "\n",
-        "def mix_datasets(dataset_mixer: dict, splits: Optional[List[str]] = None, shuffle=True) -> DatasetDict:\n",
-        "    \"\"\"\n",
-        "    Loads and mixes datasets according to proportions specified in `dataset_mixer`.\n",
-        "\n",
-        "    Args:\n",
-        "        dataset_mixer (`dict`):\n",
-        "            Dictionary containing the dataset names and their training proportions. By default, all test proportions are 1.\n",
-        "        splits (Optional[List[str]], *optional*, defaults to `None`):\n",
-        "            Dataset splits to load and mix. Assumes the splits exist in all datasets and have a `train_` or `test_` prefix.\n",
-        "        shuffle (`bool`, *optional*, defaults to `True`):\n",
-        "            Whether to shuffle the training and testing/validation data.\n",
-        "    \"\"\"\n",
-        "    raw_datasets = DatasetDict()\n",
-        "    raw_train_datasets = []\n",
-        "    raw_val_datasets = []\n",
-        "    fracs = []\n",
-        "    for ds, frac in dataset_mixer.items():\n",
-        "        fracs.append(frac)\n",
-        "        for split in splits:\n",
-        "            try:\n",
-        "                # Try first if dataset on a Hub repo\n",
-        "                dataset = load_dataset(ds, split=split)\n",
-        "            except DatasetGenerationError:\n",
-        "                # If not, check local dataset\n",
-        "                dataset = load_from_disk(os.path.join(ds, split))\n",
-        "\n",
-        "            if \"train\" in split:\n",
-        "                raw_train_datasets.append(dataset)\n",
-        "            elif \"test\" in split:\n",
-        "                raw_val_datasets.append(dataset)\n",
-        "            else:\n",
-        "                raise ValueError(f\"Split type {split} not recognized as one of test or train.\")\n",
-        "\n",
-        "    if any(frac < 0 for frac in fracs):\n",
-        "        raise ValueError(\"Dataset fractions cannot be negative.\")\n",
-        "\n",
-        "    if len(raw_train_datasets) > 0:\n",
-        "        train_subsets = []\n",
-        "        for dataset, frac in zip(raw_train_datasets, fracs):\n",
-        "            train_subset = dataset.select(range(int(frac * len(dataset))))\n",
-        "            train_subsets.append(train_subset)\n",
-        "        if shuffle:\n",
-        "            raw_datasets[\"train\"] = concatenate_datasets(train_subsets).shuffle(seed=42)\n",
-        "        else:\n",
-        "            raw_datasets[\"train\"] = concatenate_datasets(train_subsets)\n",
-        "    # No subsampling for test datasets to enable fair comparison across models\n",
-        "    if len(raw_val_datasets) > 0:\n",
-        "        if shuffle:\n",
-        "            raw_datasets[\"test\"] = concatenate_datasets(raw_val_datasets).shuffle(seed=42)\n",
-        "        else:\n",
-        "            raw_datasets[\"test\"] = concatenate_datasets(raw_val_datasets)\n",
-        "\n",
-        "    if len(raw_datasets) == 0:\n",
-        "        raise ValueError(\n",
-        "            f\"Dataset {dataset_mixer} not recognized with split {split}. Check the dataset has been correctly formatted.\"\n",
-        "        )\n",
-        "\n",
-        "    return raw_datasets"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EQ-Cp2V6kDcr"
-      },
-      "source": [
-        "<a name=\"Data\"></a>\n",
-        "### Data Prep\n",
-        "We follow Huggingface's [Alignment Handbook](https://github.com/huggingface/alignment-handbook) for [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) and use the [Ultra Feedback dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), and sample 0.5% of it to speed things up. You can sample the full dataset for a full run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
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-          ]
-        },
-        "id": "r6bUnxe6N3pf",
-        "outputId": "ff62990d-3e46-4a6d-826f-d4533b596a98"
-      },
-      "outputs": [],
-      "source": [
-        "raw_datasets = get_datasets(\n",
-        "    {\"HuggingFaceH4/ultrafeedback_binarized\" : 0.005}, # 0.5% sampled\n",
-        "    splits = [\"train_prefs\", \"test_prefs\"],\n",
-        ")\n",
-        "column_names = list(raw_datasets[\"train\"].features)\n",
-        "\n",
-        "raw_datasets = raw_datasets.map(\n",
-        "    apply_chat_template,\n",
-        "    fn_kwargs = {\"tokenizer\": tokenizer, \"task\": \"dpo\"},\n",
-        "    num_proc = 12,\n",
-        "    remove_columns = column_names,\n",
-        "    desc = \"Formatting comparisons with prompt template\",\n",
-        ")\n",
-        "\n",
-        "# Replace column names with what TRL needs, text_chosen -> chosen and text_rejected -> rejected\n",
-        "for split in [\"train\", \"test\"]:\n",
-        "    raw_datasets[split] = raw_datasets[split].rename_columns(\n",
-        "        {\"text_prompt\": \"prompt\", \"text_chosen\": \"chosen\", \"text_rejected\": \"rejected\"}\n",
-        "    )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7AxUmeAGkjDd"
-      },
-      "source": [
-        "We shall print a random item from the dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "oF63zQqNlNJC",
-        "outputId": "5e4e2858-9ac2-4023-f554-13bfeec367a8"
-      },
-      "outputs": [],
-      "source": [
-        "import pprint\n",
-        "row = raw_datasets[\"train\"][8]\n",
-        "pprint.pprint(row[\"prompt\"])\n",
-        "pprint.pprint(row[\"chosen\"])\n",
-        "pprint.pprint(row[\"rejected\"])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "86wyNoeMj-Ph"
-      },
-      "source": [
-        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "6bZsfBuZDeCL",
-        "outputId": "677aef23-d549-44d3-aace-9efda302d1e8"
-      },
-      "outputs": [],
-      "source": [
-        "model = FastMixtralModel.get_peft_model(\n",
-        "    model,\n",
-        "    r = 64, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
-        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
-        "                      \"gate\", \"w1\", \"w2\", \"w3\"],\n",
-        "    lora_alpha = 64,\n",
-        "    lora_dropout = 0, # Currently only supports dropout = 0\n",
-        "    bias = \"none\",    # Currently only supports bias = \"none\"\n",
-        "    use_gradient_checkpointing = True,\n",
-        "    random_state = 3407,\n",
-        "    use_rslora = False,  # We support rank stabilized LoRA\n",
-        "    loftq_config = None, # And LoftQ\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "-kyd_iyz7DUM"
-      },
-      "source": [
-        "<a name=\"Train\"></a>\n",
-        "### Train the DPO model\n",
-        "Now let's use Huggingface TRL's `DPOTrainer`! More docs here: [TRL DPO docs](https://huggingface.co/docs/trl/dpo_trainer). We do 3 epochs on 0.5% of the dataset to speed things up."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "v-2BFpDWzo1K"
-      },
-      "outputs": [],
-      "source": [
-        "# One must patch the DPO Trainer first!\n",
-        "from unsloth import PatchDPOTrainer\n",
-        "PatchDPOTrainer()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
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-        "outputId": "40764ccc-6502-4be1-fb4f-e386f5460147"
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-      "outputs": [],
-      "source": [
-        "from transformers import TrainingArguments\n",
-        "from trl import DPOTrainer\n",
-        "\n",
-        "dpo_trainer = DPOTrainer(\n",
-        "    model = model,\n",
-        "    ref_model = None,\n",
-        "    args = TrainingArguments(\n",
-        "        per_device_train_batch_size = 2,\n",
-        "        gradient_accumulation_steps = 4,\n",
-        "        warmup_ratio = 0.1,\n",
-        "        num_train_epochs = 3,\n",
-        "        learning_rate = 5e-6,\n",
-        "        fp16 = not torch.cuda.is_bf16_supported(),\n",
-        "        bf16 = torch.cuda.is_bf16_supported(),\n",
-        "        logging_steps = 1,\n",
-        "        optim = \"adamw_8bit\",\n",
-        "        weight_decay = 0.0,\n",
-        "        lr_scheduler_type = \"linear\",\n",
-        "        seed = 42,\n",
-        "        output_dir = \"outputs\",\n",
-        "    ),\n",
-        "    beta = 0.1,\n",
-        "    train_dataset = raw_datasets[\"train\"],\n",
-        "    # eval_dataset = raw_datasets[\"test\"],\n",
-        "    tokenizer = tokenizer,\n",
-        "    max_length = 1024,\n",
-        "    max_prompt_length = 512,\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "EWGFqAo5Q2me",
-        "outputId": "1bcba65a-8253-4a49-a180-fde8e26ad4b5"
-      },
-      "outputs": [],
-      "source": [
-        "dpo_trainer.train()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Zt9CHJqO6p30"
-      },
-      "source": [
-        "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
-        "\n",
-        "Some other links:\n",
-        "1. Mistral 7b 2x faster [free Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)\n",
-        "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n",
-        "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n",
-        "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n",
-        "5. Llama 7b [free Kaggle](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp)\n",
-        "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n",
-        "\n",
-        "<div class=\"align-center\">\n",
-        "  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
-        "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
-        "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Support our work if you can! Thanks!\n",
-        "</div>"
-      ]
-    }
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-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
-            "_view_name": "HTMLView",
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-            "layout": "IPY_MODEL_1aa72a1d1d0d4ce4b36b10c8ecf021a8",
-            "placeholder": "​",
-            "style": "IPY_MODEL_a004e9208a5f441e9e4e60cc1f6d7aa9",
-            "value": "Generating test_sft split: 100%"
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diff --git a/unsloth/models/mixtral.py b/unsloth/models/mixtral.py
index c635a7a7..578f5017 100644
--- a/unsloth/models/mixtral.py
+++ b/unsloth/models/mixtral.py
@@ -303,12 +303,11 @@ def MixtralForCausalLM_fast_forward(
         shift_logits = logits[..., :-1, :].contiguous()
         shift_labels = labels[..., 1:].contiguous()
         # Flatten the tokens
-        loss_fct = fast_cross_entropy_loss()
-        shift_logits = shift_logits.view(-1, self.config.vocab_size)
-        shift_labels = shift_labels.view(-1)
+        # shift_logits = shift_logits.view(-1, self.config.vocab_size)
+        # shift_labels = shift_labels.view(-1)
         # Enable model parallelism
-        shift_labels = shift_labels.to(shift_logits.device)
-        loss = loss_fct(shift_logits, shift_labels)
+        # shift_labels = shift_labels.to(shift_logits.device)
+        loss = fast_cross_entropy_loss(logits = shift_logits, labels = shift_labels)
 
     aux_loss = None
     if output_router_logits:
@@ -338,6 +337,34 @@ def MixtralForCausalLM_fast_forward(
     )
 pass
 
+def MixtralPeftModelForCausalLM_fast_forward(
+    self,
+    input_ids=None,
+    causal_mask=None,
+    attention_mask=None,
+    inputs_embeds=None,
+    labels=None,
+    output_attentions=None,
+    output_hidden_states=None,
+    output_router_logits=None,
+    return_dict=None,
+    task_ids=None,
+    **kwargs,
+):
+    return self.base_model(
+        input_ids=input_ids,
+        causal_mask=causal_mask,
+        attention_mask=attention_mask,
+        inputs_embeds=inputs_embeds,
+        labels=labels,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        output_router_logits=output_router_logits,
+        return_dict=return_dict,
+        **kwargs,
+    )
+pass
+
 class FastMixtralModel:
     @staticmethod
     def pre_patch():
@@ -347,7 +374,7 @@ def pre_patch():
         MixtralDecoderLayer   .forward = MixtralDecoderLayer_fast_forward
         MixtralModel          .forward = MixtralModel_fast_forward
         MixtralForCausalLM    .forward = MixtralForCausalLM_fast_forward
-        PeftModelForCausalLM  .forward = PeftModelForCausalLM_fast_forward
+        PeftModelForCausalLM  .forward = MixtralPeftModelForCausalLM_fast_forward
         return
     pass