DPO_Zephyr_Unsloth_Example.ipynb

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      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ninjaelastic/models/blob/master/DPO_Zephyr_Unsloth_Example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "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",
        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "!pip install --no-deps \"xformers<0.0.26\" trl \"peft<0.11.0\" accelerate bitsandbytes"
      ]
    },
    {
      "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",
        "* 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.\n",
        "* [**NEW**] We make Gemma 6 trillion tokens **2.5x faster**! See our [Gemma notebook](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8-BWi7MzkRz",
        "outputId": "0626186f-6e70-47b7-db59-b8e7591d0de4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:67: UserWarning: CUDA is not linked properly.\n",
            "We shall run `ldconfig /usr/lib64-nvidia` to try to fix it.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# One must patch the DPO Trainer first!\n",
        "from unsloth import PatchDPOTrainer\n",
        "PatchDPOTrainer()"
      ]
    },
    {
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            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:72: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "==((====))==  Unsloth: Fast Mistral patching release 2024.1\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB\n",
            "O^O/ \\_/ \\    CUDA capability = 7.5. Xformers = 0.0.22.post7. FA = False.\n",
            "\\        /    Pytorch version: 2.1.0+cu121. CUDA Toolkit = 12.1\n",
            " \"-____-\"     bfloat16 = FALSE. Platform = Linux\n",
            "\n",
            "You passed `quantization_config` to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` attribute will be overwritten with the one you passed to `from_pretrained`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
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          "data": {
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          "metadata": {}
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      ],
      "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 = \"unsloth/zephyr-sft-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",
        ")"
      ]
    },
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      "cell_type": "code",
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      "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",
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        "id": "r6bUnxe6N3pf",
        "outputId": "ff62990d-3e46-4a6d-826f-d4533b596a98"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/5.98k [00:00<?, ?B/s]"
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        {
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          "data": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97a28a558b2a4ef997fb35547ef2f51d"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/7.12M [00:00<?, ?B/s]"
            ],
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              "version_minor": 0,
              "model_id": "e10b53d12a134bc6a674bc568e1ab7bf"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train_sft split:   0%|          | 0/61966 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65ea6829bd484590a9e99f07bbadf52a"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test_sft split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03b41c21d7634543a1b29eaadbf1cd40"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train_gen split:   0%|          | 0/61966 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c7cd9bca9994ee4abc817035db4f115"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test_gen split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d8bd9a5ec064157a2099a23621ccdad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train_prefs split:   0%|          | 0/61966 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "baa1b968a1de4d029730f6a5a2301101"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test_prefs split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6c12557e5814d4db2ca39372d44a167"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Formatting comparisons with prompt template (num_proc=12):   0%|          | 0/309 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6ed1837c16b47d3913f123a7c09d833"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Formatting comparisons with prompt template (num_proc=12):   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e63c3a8c021b4d30b0c93b99a7dba155"
            }
          },
          "metadata": {}
        }
      ],
      "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": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('<|system|>\\n'\n",
            " '</s>\\n'\n",
            " '<|user|>\\n'\n",
            " 'List two natural resources which was made in the factory.</s>\\n'\n",
            " '<|assistant|>\\n')\n",
            "('Natural resources are not made in factories. Natural resources are materials '\n",
            " 'and substances that occur naturally on Earth, such as water, minerals, '\n",
            " 'forests, and fossil fuels. Factories typically produce man-made materials or '\n",
            " 'process natural resources into finished products.</s>\\n')\n",
            "(\"I'm sorry, but it seems there might be some confusion in your question as \"\n",
            " 'natural resources are typically sourced from the earth or sea, and not made '\n",
            " 'in a factory. However, factories often use natural resources to create '\n",
            " 'various products. Two examples of natural resources that factories may use '\n",
            " 'are crude oil and iron ore. Crude oil is refined to produce various '\n",
            " 'petroleum products, such as gasoline and plastics, while iron ore is refined '\n",
            " 'to create steel, which is used in the construction industry, vehicle '\n",
            " 'manufacturing, and more. Does this help clarify things?</s>\\n')\n"
          ]
        }
      ],
      "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": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2024.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.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_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 64,\n",
        "    lora_dropout = 0, # Currently only supports dropout = 0\n",
        "    bias = \"none\",    # Currently only supports bias = \"none\"\n",
        "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\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/",
          "height": 104,
          "referenced_widgets": [
            "1410214ec6fd4e19a200b5e03464ceee",
            "0ba04e31d310422f9efaef634afb1296",
            "6ac7582965d341c383c806ae3a8b513a",
            "971371cfb1bd447e94f9f2e38f9f50c8",
            "3d4dbe5814d24b7dae1a7462a33e7f40",
            "6b4743fee43b40638d8f2df36fa9fed9",
            "77f706dc09eb43d7aa4522f8a0b36142",
            "175c7bb9e8c94b8daa3287dd773b6837",
            "a10418a202e14a96893e24267bc194c9",
            "8138d9f175df4a28b2e58e9c852cabb8",
            "43b39a8abcb94847bc21ec16fa373ed9"
          ]
        },
        "id": "QtoqUw80QDV0",
        "outputId": "40764ccc-6502-4be1-fb4f-e386f5460147"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/dpo_trainer.py:294: UserWarning: When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments we have set it for you, but you should do it yourself in the future.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/309 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1410214ec6fd4e19a200b5e03464ceee"
            }
          },
          "metadata": {}
        }
      ],
      "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": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth: `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`\n",
            "Could not estimate the number of tokens of the input, floating-point operations will not be computed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='114' max='114' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [114/114 1:06:24, Epoch 2/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>rewards / chosen</th>\n",
              "      <th>rewards / rejected</th>\n",
              "      <th>rewards / accuracies</th>\n",
              "      <th>rewards / margins</th>\n",
              "      <th>logps / rejected</th>\n",
              "      <th>logps / chosen</th>\n",
              "      <th>logits / rejected</th>\n",
              "      <th>logits / chosen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.693100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-297.338806</td>\n",
              "      <td>-218.968842</td>\n",
              "      <td>-2.758142</td>\n",
              "      <td>-2.924523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.693100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-237.602417</td>\n",
              "      <td>-217.613892</td>\n",
              "      <td>-2.731790</td>\n",
              "      <td>-2.913610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.692200</td>\n",
              "      <td>0.001937</td>\n",
              "      <td>-0.000008</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.001945</td>\n",
              "      <td>-172.792877</td>\n",
              "      <td>-202.709259</td>\n",
              "      <td>-2.464616</td>\n",
              "      <td>-2.728198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.692700</td>\n",
              "      <td>0.000855</td>\n",
              "      <td>-0.000130</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.000985</td>\n",
              "      <td>-117.745728</td>\n",
              "      <td>-170.711212</td>\n",
              "      <td>-2.591983</td>\n",
              "      <td>-2.805891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.692500</td>\n",
              "      <td>0.001272</td>\n",
              "      <td>-0.000044</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.001315</td>\n",
              "      <td>-197.045288</td>\n",
              "      <td>-338.722626</td>\n",
              "      <td>-2.541809</td>\n",
              "      <td>-2.483452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.693700</td>\n",
              "      <td>-0.000155</td>\n",
              "      <td>0.001034</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>-0.001189</td>\n",
              "      <td>-279.681396</td>\n",
              "      <td>-188.683563</td>\n",
              "      <td>-2.870259</td>\n",
              "      <td>-2.633069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.692600</td>\n",
              "      <td>-0.001525</td>\n",
              "      <td>-0.002655</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.001130</td>\n",
              "      <td>-157.678253</td>\n",
              "      <td>-236.215118</td>\n",
              "      <td>-2.510528</td>\n",
              "      <td>-2.838598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.692500</td>\n",
              "      <td>0.003246</td>\n",
              "      <td>0.001937</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.001309</td>\n",
              "      <td>-451.776550</td>\n",
              "      <td>-493.112579</td>\n",
              "      <td>-2.677159</td>\n",
              "      <td>-2.677977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.693600</td>\n",
              "      <td>-0.003702</td>\n",
              "      <td>-0.002773</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>-0.000929</td>\n",
              "      <td>-198.079987</td>\n",
              "      <td>-405.686523</td>\n",
              "      <td>-2.366659</td>\n",
              "      <td>-2.640016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.685900</td>\n",
              "      <td>0.009754</td>\n",
              "      <td>-0.004801</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.014555</td>\n",
              "      <td>-192.067322</td>\n",
              "      <td>-238.161560</td>\n",
              "      <td>-2.735095</td>\n",
              "      <td>-2.766379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.690100</td>\n",
              "      <td>0.006233</td>\n",
              "      <td>-0.000102</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.006335</td>\n",
              "      <td>-279.058716</td>\n",
              "      <td>-242.637787</td>\n",
              "      <td>-2.741516</td>\n",
              "      <td>-2.707231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.676800</td>\n",
              "      <td>0.029851</td>\n",
              "      <td>-0.003707</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.033558</td>\n",
              "      <td>-293.294495</td>\n",
              "      <td>-297.151794</td>\n",
              "      <td>-2.636089</td>\n",
              "      <td>-2.797787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.684900</td>\n",
              "      <td>0.035930</td>\n",
              "      <td>0.018558</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.017372</td>\n",
              "      <td>-422.263824</td>\n",
              "      <td>-439.903442</td>\n",
              "      <td>-2.782973</td>\n",
              "      <td>-2.722686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.691100</td>\n",
              "      <td>-0.007306</td>\n",
              "      <td>-0.012685</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.005379</td>\n",
              "      <td>-229.290237</td>\n",
              "      <td>-517.691650</td>\n",
              "      <td>-2.761815</td>\n",
              "      <td>-2.876947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.675700</td>\n",
              "      <td>0.052242</td>\n",
              "      <td>0.016329</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.035913</td>\n",
              "      <td>-229.797638</td>\n",
              "      <td>-368.169067</td>\n",
              "      <td>-2.686517</td>\n",
              "      <td>-2.849208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.671000</td>\n",
              "      <td>0.049620</td>\n",
              "      <td>0.002834</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.046786</td>\n",
              "      <td>-153.669510</td>\n",
              "      <td>-214.005783</td>\n",
              "      <td>-2.551779</td>\n",
              "      <td>-2.668840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.659200</td>\n",
              "      <td>0.031351</td>\n",
              "      <td>-0.039343</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.070693</td>\n",
              "      <td>-145.433411</td>\n",
              "      <td>-178.029861</td>\n",
              "      <td>-2.550349</td>\n",
              "      <td>-2.744968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.670900</td>\n",
              "      <td>0.042720</td>\n",
              "      <td>-0.003664</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.046384</td>\n",
              "      <td>-193.517059</td>\n",
              "      <td>-306.975281</td>\n",
              "      <td>-2.544961</td>\n",
              "      <td>-2.794021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.682400</td>\n",
              "      <td>0.049649</td>\n",
              "      <td>0.026523</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.023126</td>\n",
              "      <td>-248.459091</td>\n",
              "      <td>-226.184174</td>\n",
              "      <td>-2.847072</td>\n",
              "      <td>-2.758221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.667900</td>\n",
              "      <td>0.097072</td>\n",
              "      <td>0.045065</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.052007</td>\n",
              "      <td>-265.443054</td>\n",
              "      <td>-420.395142</td>\n",
              "      <td>-2.906176</td>\n",
              "      <td>-2.950474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.712400</td>\n",
              "      <td>0.009529</td>\n",
              "      <td>0.039224</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>-0.029695</td>\n",
              "      <td>-156.176773</td>\n",
              "      <td>-202.126480</td>\n",
              "      <td>-2.445233</td>\n",
              "      <td>-2.536827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.664700</td>\n",
              "      <td>0.027259</td>\n",
              "      <td>-0.032389</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.059647</td>\n",
              "      <td>-241.187759</td>\n",
              "      <td>-258.569122</td>\n",
              "      <td>-2.660511</td>\n",
              "      <td>-2.759560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.612400</td>\n",
              "      <td>0.111760</td>\n",
              "      <td>-0.064276</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.176036</td>\n",
              "      <td>-172.977020</td>\n",
              "      <td>-305.922211</td>\n",
              "      <td>-2.420000</td>\n",
              "      <td>-2.796694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.675200</td>\n",
              "      <td>0.064246</td>\n",
              "      <td>0.021886</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.042360</td>\n",
              "      <td>-180.612473</td>\n",
              "      <td>-224.649567</td>\n",
              "      <td>-2.242796</td>\n",
              "      <td>-2.368723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.638300</td>\n",
              "      <td>0.059822</td>\n",
              "      <td>-0.055270</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.115092</td>\n",
              "      <td>-109.729645</td>\n",
              "      <td>-135.127365</td>\n",
              "      <td>-2.535609</td>\n",
              "      <td>-2.693033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.682100</td>\n",
              "      <td>0.044842</td>\n",
              "      <td>0.019694</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.025148</td>\n",
              "      <td>-217.967117</td>\n",
              "      <td>-216.428604</td>\n",
              "      <td>-2.346493</td>\n",
              "      <td>-2.305699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.601700</td>\n",
              "      <td>0.134719</td>\n",
              "      <td>-0.065319</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.200038</td>\n",
              "      <td>-254.221039</td>\n",
              "      <td>-275.279114</td>\n",
              "      <td>-2.407781</td>\n",
              "      <td>-2.619088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.690600</td>\n",
              "      <td>0.026908</td>\n",
              "      <td>-0.005121</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.032030</td>\n",
              "      <td>-241.214081</td>\n",
              "      <td>-239.618500</td>\n",
              "      <td>-2.711054</td>\n",
              "      <td>-2.787158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.640600</td>\n",
              "      <td>0.031898</td>\n",
              "      <td>-0.091680</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.123578</td>\n",
              "      <td>-176.982712</td>\n",
              "      <td>-201.977280</td>\n",
              "      <td>-2.830093</td>\n",
              "      <td>-2.735838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.559400</td>\n",
              "      <td>0.228695</td>\n",
              "      <td>-0.072701</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.301397</td>\n",
              "      <td>-289.870209</td>\n",
              "      <td>-314.927216</td>\n",
              "      <td>-2.553982</td>\n",
              "      <td>-2.775172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.679700</td>\n",
              "      <td>0.034291</td>\n",
              "      <td>-0.020282</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.054574</td>\n",
              "      <td>-251.074112</td>\n",
              "      <td>-229.884064</td>\n",
              "      <td>-2.657125</td>\n",
              "      <td>-2.649465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.609900</td>\n",
              "      <td>0.073758</td>\n",
              "      <td>-0.113377</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.187135</td>\n",
              "      <td>-183.661713</td>\n",
              "      <td>-286.482422</td>\n",
              "      <td>-2.423700</td>\n",
              "      <td>-2.839998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.646400</td>\n",
              "      <td>0.127758</td>\n",
              "      <td>-0.020295</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.148053</td>\n",
              "      <td>-394.591248</td>\n",
              "      <td>-512.621033</td>\n",
              "      <td>-2.927793</td>\n",
              "      <td>-2.909138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.637400</td>\n",
              "      <td>0.028342</td>\n",
              "      <td>-0.090154</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.118495</td>\n",
              "      <td>-214.089691</td>\n",
              "      <td>-186.278961</td>\n",
              "      <td>-2.780502</td>\n",
              "      <td>-2.638843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.682000</td>\n",
              "      <td>0.006539</td>\n",
              "      <td>-0.016702</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.023241</td>\n",
              "      <td>-210.243164</td>\n",
              "      <td>-83.531448</td>\n",
              "      <td>-2.730125</td>\n",
              "      <td>-2.316058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.533200</td>\n",
              "      <td>0.213769</td>\n",
              "      <td>-0.195208</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.408977</td>\n",
              "      <td>-279.895813</td>\n",
              "      <td>-429.939270</td>\n",
              "      <td>-2.432103</td>\n",
              "      <td>-2.655017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.656300</td>\n",
              "      <td>0.063138</td>\n",
              "      <td>-0.016303</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.079441</td>\n",
              "      <td>-141.416199</td>\n",
              "      <td>-138.177078</td>\n",
              "      <td>-2.473918</td>\n",
              "      <td>-2.547494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.651000</td>\n",
              "      <td>0.094509</td>\n",
              "      <td>-0.019390</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.113899</td>\n",
              "      <td>-200.108643</td>\n",
              "      <td>-165.778290</td>\n",
              "      <td>-2.236758</td>\n",
              "      <td>-2.336314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.552100</td>\n",
              "      <td>0.149231</td>\n",
              "      <td>-0.200812</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.350043</td>\n",
              "      <td>-232.878784</td>\n",
              "      <td>-223.073883</td>\n",
              "      <td>-2.657633</td>\n",
              "      <td>-2.716261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.400600</td>\n",
              "      <td>0.367951</td>\n",
              "      <td>-0.639771</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.007722</td>\n",
              "      <td>-267.012238</td>\n",
              "      <td>-173.167068</td>\n",
              "      <td>-2.681762</td>\n",
              "      <td>-2.732720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.437000</td>\n",
              "      <td>0.394438</td>\n",
              "      <td>-0.288346</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.682784</td>\n",
              "      <td>-186.329910</td>\n",
              "      <td>-195.446442</td>\n",
              "      <td>-2.616049</td>\n",
              "      <td>-2.464377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.373200</td>\n",
              "      <td>0.502430</td>\n",
              "      <td>-0.394705</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.897135</td>\n",
              "      <td>-184.657394</td>\n",
              "      <td>-346.536224</td>\n",
              "      <td>-2.492926</td>\n",
              "      <td>-2.774618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.527200</td>\n",
              "      <td>0.093942</td>\n",
              "      <td>-0.300091</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.394033</td>\n",
              "      <td>-130.909210</td>\n",
              "      <td>-115.344902</td>\n",
              "      <td>-2.750227</td>\n",
              "      <td>-2.718995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.394000</td>\n",
              "      <td>0.518654</td>\n",
              "      <td>-0.385459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.904113</td>\n",
              "      <td>-229.264954</td>\n",
              "      <td>-317.110352</td>\n",
              "      <td>-2.540475</td>\n",
              "      <td>-2.699816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.281200</td>\n",
              "      <td>0.686930</td>\n",
              "      <td>-0.696812</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.383742</td>\n",
              "      <td>-244.271576</td>\n",
              "      <td>-283.048157</td>\n",
              "      <td>-2.240731</td>\n",
              "      <td>-2.588020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.242200</td>\n",
              "      <td>0.650970</td>\n",
              "      <td>-0.705772</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.356742</td>\n",
              "      <td>-203.522079</td>\n",
              "      <td>-258.910522</td>\n",
              "      <td>-2.546949</td>\n",
              "      <td>-2.711936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.394500</td>\n",
              "      <td>0.561647</td>\n",
              "      <td>-0.338273</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.899920</td>\n",
              "      <td>-142.371277</td>\n",
              "      <td>-239.892838</td>\n",
              "      <td>-2.526323</td>\n",
              "      <td>-2.609220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.374800</td>\n",
              "      <td>0.471081</td>\n",
              "      <td>-0.427850</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.898931</td>\n",
              "      <td>-160.959320</td>\n",
              "      <td>-212.728607</td>\n",
              "      <td>-2.422826</td>\n",
              "      <td>-2.570896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.327000</td>\n",
              "      <td>0.703227</td>\n",
              "      <td>-0.639334</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.342561</td>\n",
              "      <td>-219.936340</td>\n",
              "      <td>-206.114929</td>\n",
              "      <td>-2.642008</td>\n",
              "      <td>-2.712688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.286000</td>\n",
              "      <td>0.396052</td>\n",
              "      <td>-0.969404</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.365456</td>\n",
              "      <td>-357.370056</td>\n",
              "      <td>-308.878326</td>\n",
              "      <td>-2.753402</td>\n",
              "      <td>-2.674081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.191800</td>\n",
              "      <td>0.713322</td>\n",
              "      <td>-1.066314</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.779635</td>\n",
              "      <td>-405.730530</td>\n",
              "      <td>-336.061005</td>\n",
              "      <td>-2.561202</td>\n",
              "      <td>-2.704443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.378700</td>\n",
              "      <td>0.434130</td>\n",
              "      <td>-0.507735</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.941865</td>\n",
              "      <td>-231.648224</td>\n",
              "      <td>-199.180984</td>\n",
              "      <td>-2.683644</td>\n",
              "      <td>-2.706589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.446800</td>\n",
              "      <td>0.479577</td>\n",
              "      <td>-0.329427</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.809003</td>\n",
              "      <td>-130.374908</td>\n",
              "      <td>-295.123444</td>\n",
              "      <td>-2.684445</td>\n",
              "      <td>-2.744673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.273000</td>\n",
              "      <td>0.722516</td>\n",
              "      <td>-0.610364</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.332880</td>\n",
              "      <td>-239.890060</td>\n",
              "      <td>-334.665283</td>\n",
              "      <td>-2.653185</td>\n",
              "      <td>-2.843446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.452400</td>\n",
              "      <td>0.024534</td>\n",
              "      <td>-0.629551</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.654085</td>\n",
              "      <td>-188.501862</td>\n",
              "      <td>-130.375015</td>\n",
              "      <td>-2.748917</td>\n",
              "      <td>-2.645331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.266100</td>\n",
              "      <td>0.659037</td>\n",
              "      <td>-0.710914</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.369951</td>\n",
              "      <td>-213.674606</td>\n",
              "      <td>-326.524048</td>\n",
              "      <td>-2.625597</td>\n",
              "      <td>-2.745638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.210400</td>\n",
              "      <td>1.066818</td>\n",
              "      <td>-0.894150</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.960968</td>\n",
              "      <td>-356.119354</td>\n",
              "      <td>-584.736694</td>\n",
              "      <td>-2.604546</td>\n",
              "      <td>-2.943971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.216600</td>\n",
              "      <td>0.581929</td>\n",
              "      <td>-0.984844</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.566772</td>\n",
              "      <td>-251.694885</td>\n",
              "      <td>-202.865784</td>\n",
              "      <td>-2.672240</td>\n",
              "      <td>-2.820692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.252000</td>\n",
              "      <td>0.775185</td>\n",
              "      <td>-0.855321</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.630505</td>\n",
              "      <td>-229.775177</td>\n",
              "      <td>-243.479630</td>\n",
              "      <td>-2.541421</td>\n",
              "      <td>-2.667074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.235700</td>\n",
              "      <td>0.678536</td>\n",
              "      <td>-0.890656</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.569192</td>\n",
              "      <td>-245.954163</td>\n",
              "      <td>-237.501450</td>\n",
              "      <td>-2.326149</td>\n",
              "      <td>-2.406452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.370800</td>\n",
              "      <td>0.287420</td>\n",
              "      <td>-0.702332</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.989753</td>\n",
              "      <td>-188.207214</td>\n",
              "      <td>-228.028946</td>\n",
              "      <td>-2.577148</td>\n",
              "      <td>-2.665427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.262900</td>\n",
              "      <td>0.559217</td>\n",
              "      <td>-0.917912</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.477129</td>\n",
              "      <td>-235.731598</td>\n",
              "      <td>-242.911835</td>\n",
              "      <td>-2.600375</td>\n",
              "      <td>-2.666599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>0.677785</td>\n",
              "      <td>-0.526713</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.204498</td>\n",
              "      <td>-244.343613</td>\n",
              "      <td>-247.966156</td>\n",
              "      <td>-2.786634</td>\n",
              "      <td>-2.922563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.294500</td>\n",
              "      <td>0.573417</td>\n",
              "      <td>-0.764463</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.337880</td>\n",
              "      <td>-262.969116</td>\n",
              "      <td>-332.956848</td>\n",
              "      <td>-2.830733</td>\n",
              "      <td>-2.896999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.244300</td>\n",
              "      <td>0.549664</td>\n",
              "      <td>-1.087809</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.637473</td>\n",
              "      <td>-247.386093</td>\n",
              "      <td>-324.000397</td>\n",
              "      <td>-2.571335</td>\n",
              "      <td>-2.723726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>0.476500</td>\n",
              "      <td>0.178537</td>\n",
              "      <td>-0.566751</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.745289</td>\n",
              "      <td>-118.356186</td>\n",
              "      <td>-180.834473</td>\n",
              "      <td>-2.530770</td>\n",
              "      <td>-2.648711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>0.223600</td>\n",
              "      <td>0.633353</td>\n",
              "      <td>-1.162451</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.795804</td>\n",
              "      <td>-307.716217</td>\n",
              "      <td>-219.696091</td>\n",
              "      <td>-2.212558</td>\n",
              "      <td>-2.263221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.269500</td>\n",
              "      <td>0.654256</td>\n",
              "      <td>-1.030838</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.685093</td>\n",
              "      <td>-197.056473</td>\n",
              "      <td>-239.991241</td>\n",
              "      <td>-2.563220</td>\n",
              "      <td>-2.735702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>0.258400</td>\n",
              "      <td>0.750621</td>\n",
              "      <td>-0.853553</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.604174</td>\n",
              "      <td>-317.039642</td>\n",
              "      <td>-243.264709</td>\n",
              "      <td>-2.802447</td>\n",
              "      <td>-2.661048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.294800</td>\n",
              "      <td>0.779875</td>\n",
              "      <td>-0.589920</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>1.369795</td>\n",
              "      <td>-231.162201</td>\n",
              "      <td>-303.384857</td>\n",
              "      <td>-2.517439</td>\n",
              "      <td>-2.746666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>0.301700</td>\n",
              "      <td>0.602643</td>\n",
              "      <td>-0.635765</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.238408</td>\n",
              "      <td>-216.019257</td>\n",
              "      <td>-264.793274</td>\n",
              "      <td>-2.675880</td>\n",
              "      <td>-2.789920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>0.264200</td>\n",
              "      <td>0.664522</td>\n",
              "      <td>-0.786896</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.451418</td>\n",
              "      <td>-268.222992</td>\n",
              "      <td>-250.234924</td>\n",
              "      <td>-2.888840</td>\n",
              "      <td>-2.899784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.194500</td>\n",
              "      <td>1.065459</td>\n",
              "      <td>-0.701678</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.767137</td>\n",
              "      <td>-376.451355</td>\n",
              "      <td>-372.076660</td>\n",
              "      <td>-2.590312</td>\n",
              "      <td>-2.763450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>0.270400</td>\n",
              "      <td>0.859751</td>\n",
              "      <td>-0.819957</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>1.679707</td>\n",
              "      <td>-149.462494</td>\n",
              "      <td>-414.388550</td>\n",
              "      <td>-2.319313</td>\n",
              "      <td>-2.621300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.243600</td>\n",
              "      <td>0.477052</td>\n",
              "      <td>-1.359335</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.836387</td>\n",
              "      <td>-351.890869</td>\n",
              "      <td>-252.686096</td>\n",
              "      <td>-2.666570</td>\n",
              "      <td>-2.624575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.521800</td>\n",
              "      <td>0.133218</td>\n",
              "      <td>-0.285669</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.418887</td>\n",
              "      <td>-156.737289</td>\n",
              "      <td>-191.685196</td>\n",
              "      <td>-2.572031</td>\n",
              "      <td>-2.732099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>0.251800</td>\n",
              "      <td>0.547161</td>\n",
              "      <td>-0.981502</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.528663</td>\n",
              "      <td>-247.284332</td>\n",
              "      <td>-368.113342</td>\n",
              "      <td>-2.663839</td>\n",
              "      <td>-2.841396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.483600</td>\n",
              "      <td>0.198344</td>\n",
              "      <td>-0.428733</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.627078</td>\n",
              "      <td>-94.173538</td>\n",
              "      <td>-73.744232</td>\n",
              "      <td>-2.580647</td>\n",
              "      <td>-2.671262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.235200</td>\n",
              "      <td>0.804393</td>\n",
              "      <td>-0.938474</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.742867</td>\n",
              "      <td>-206.442154</td>\n",
              "      <td>-208.880112</td>\n",
              "      <td>-2.574049</td>\n",
              "      <td>-2.685945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.214900</td>\n",
              "      <td>0.959676</td>\n",
              "      <td>-1.044084</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.003760</td>\n",
              "      <td>-316.608826</td>\n",
              "      <td>-234.795319</td>\n",
              "      <td>-2.721649</td>\n",
              "      <td>-2.706306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.147000</td>\n",
              "      <td>0.606548</td>\n",
              "      <td>-1.598809</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.205357</td>\n",
              "      <td>-220.293610</td>\n",
              "      <td>-220.745911</td>\n",
              "      <td>-2.564118</td>\n",
              "      <td>-2.648017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.158600</td>\n",
              "      <td>0.695428</td>\n",
              "      <td>-1.412461</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.107888</td>\n",
              "      <td>-268.839172</td>\n",
              "      <td>-226.468628</td>\n",
              "      <td>-2.439530</td>\n",
              "      <td>-2.542942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>0.249200</td>\n",
              "      <td>0.614686</td>\n",
              "      <td>-0.954372</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.569058</td>\n",
              "      <td>-152.696320</td>\n",
              "      <td>-262.169922</td>\n",
              "      <td>-2.440640</td>\n",
              "      <td>-2.618743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.175000</td>\n",
              "      <td>0.862260</td>\n",
              "      <td>-1.443703</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.305963</td>\n",
              "      <td>-283.026184</td>\n",
              "      <td>-323.999420</td>\n",
              "      <td>-2.673517</td>\n",
              "      <td>-2.778322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.152300</td>\n",
              "      <td>1.219675</td>\n",
              "      <td>-1.141970</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.361645</td>\n",
              "      <td>-261.000122</td>\n",
              "      <td>-287.041412</td>\n",
              "      <td>-2.748819</td>\n",
              "      <td>-2.854210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.261500</td>\n",
              "      <td>0.703294</td>\n",
              "      <td>-0.766885</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.470178</td>\n",
              "      <td>-109.584824</td>\n",
              "      <td>-180.936401</td>\n",
              "      <td>-2.330353</td>\n",
              "      <td>-2.552672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>0.057000</td>\n",
              "      <td>1.309202</td>\n",
              "      <td>-1.812632</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.121835</td>\n",
              "      <td>-432.551086</td>\n",
              "      <td>-569.634766</td>\n",
              "      <td>-2.979058</td>\n",
              "      <td>-2.924992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.113400</td>\n",
              "      <td>1.130503</td>\n",
              "      <td>-1.825813</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.956316</td>\n",
              "      <td>-415.485809</td>\n",
              "      <td>-326.073120</td>\n",
              "      <td>-2.788409</td>\n",
              "      <td>-2.560507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.181200</td>\n",
              "      <td>0.784262</td>\n",
              "      <td>-1.230724</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.014986</td>\n",
              "      <td>-262.161804</td>\n",
              "      <td>-377.537537</td>\n",
              "      <td>-2.736547</td>\n",
              "      <td>-2.671477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.102100</td>\n",
              "      <td>1.122743</td>\n",
              "      <td>-1.496860</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.619603</td>\n",
              "      <td>-396.485840</td>\n",
              "      <td>-384.253632</td>\n",
              "      <td>-2.682523</td>\n",
              "      <td>-2.880081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.145400</td>\n",
              "      <td>1.151220</td>\n",
              "      <td>-1.096753</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.247973</td>\n",
              "      <td>-216.382874</td>\n",
              "      <td>-324.985718</td>\n",
              "      <td>-2.543500</td>\n",
              "      <td>-2.719545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>0.135600</td>\n",
              "      <td>0.915327</td>\n",
              "      <td>-1.424227</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.339553</td>\n",
              "      <td>-353.060242</td>\n",
              "      <td>-352.221008</td>\n",
              "      <td>-2.704442</td>\n",
              "      <td>-2.904936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>0.265600</td>\n",
              "      <td>0.771701</td>\n",
              "      <td>-0.672446</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.444147</td>\n",
              "      <td>-121.571732</td>\n",
              "      <td>-253.565125</td>\n",
              "      <td>-2.585968</td>\n",
              "      <td>-2.709661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>0.210300</td>\n",
              "      <td>0.828890</td>\n",
              "      <td>-1.182284</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.011175</td>\n",
              "      <td>-220.569305</td>\n",
              "      <td>-219.065125</td>\n",
              "      <td>-2.253163</td>\n",
              "      <td>-2.439387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.167300</td>\n",
              "      <td>0.742948</td>\n",
              "      <td>-1.223057</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.966005</td>\n",
              "      <td>-239.375641</td>\n",
              "      <td>-261.817993</td>\n",
              "      <td>-2.562728</td>\n",
              "      <td>-2.784313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.289100</td>\n",
              "      <td>0.318967</td>\n",
              "      <td>-1.059428</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.378395</td>\n",
              "      <td>-198.750443</td>\n",
              "      <td>-181.136566</td>\n",
              "      <td>-2.669433</td>\n",
              "      <td>-2.856450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.193100</td>\n",
              "      <td>0.473150</td>\n",
              "      <td>-1.672879</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.146029</td>\n",
              "      <td>-219.311478</td>\n",
              "      <td>-153.676514</td>\n",
              "      <td>-2.851680</td>\n",
              "      <td>-2.796827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>0.207800</td>\n",
              "      <td>0.637917</td>\n",
              "      <td>-1.200011</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.837928</td>\n",
              "      <td>-158.673050</td>\n",
              "      <td>-235.140503</td>\n",
              "      <td>-2.663034</td>\n",
              "      <td>-2.721454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.260400</td>\n",
              "      <td>0.444965</td>\n",
              "      <td>-0.946055</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.391021</td>\n",
              "      <td>-161.993805</td>\n",
              "      <td>-174.353287</td>\n",
              "      <td>-2.403941</td>\n",
              "      <td>-2.721178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.145000</td>\n",
              "      <td>0.601810</td>\n",
              "      <td>-1.648575</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.250385</td>\n",
              "      <td>-209.413452</td>\n",
              "      <td>-150.945694</td>\n",
              "      <td>-2.433372</td>\n",
              "      <td>-2.527679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>101</td>\n",
              "      <td>0.202400</td>\n",
              "      <td>0.970933</td>\n",
              "      <td>-0.949473</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.920407</td>\n",
              "      <td>-248.121094</td>\n",
              "      <td>-240.405640</td>\n",
              "      <td>-2.550437</td>\n",
              "      <td>-2.803463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>102</td>\n",
              "      <td>0.144400</td>\n",
              "      <td>1.012043</td>\n",
              "      <td>-1.278788</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.290831</td>\n",
              "      <td>-260.144043</td>\n",
              "      <td>-226.668945</td>\n",
              "      <td>-2.700693</td>\n",
              "      <td>-2.540809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>103</td>\n",
              "      <td>0.234300</td>\n",
              "      <td>0.627674</td>\n",
              "      <td>-0.960762</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.588436</td>\n",
              "      <td>-154.225189</td>\n",
              "      <td>-159.628006</td>\n",
              "      <td>-2.725933</td>\n",
              "      <td>-2.771981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>104</td>\n",
              "      <td>0.241300</td>\n",
              "      <td>0.620007</td>\n",
              "      <td>-1.055792</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.675798</td>\n",
              "      <td>-208.798172</td>\n",
              "      <td>-225.909439</td>\n",
              "      <td>-2.412787</td>\n",
              "      <td>-2.513871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>105</td>\n",
              "      <td>0.130100</td>\n",
              "      <td>1.341354</td>\n",
              "      <td>-1.176110</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.517464</td>\n",
              "      <td>-260.019531</td>\n",
              "      <td>-385.742279</td>\n",
              "      <td>-2.458776</td>\n",
              "      <td>-2.786789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>106</td>\n",
              "      <td>0.184600</td>\n",
              "      <td>0.906436</td>\n",
              "      <td>-0.938855</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.845291</td>\n",
              "      <td>-239.872620</td>\n",
              "      <td>-370.590424</td>\n",
              "      <td>-2.573937</td>\n",
              "      <td>-2.770343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>107</td>\n",
              "      <td>0.193000</td>\n",
              "      <td>0.909325</td>\n",
              "      <td>-1.122883</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.032207</td>\n",
              "      <td>-195.062927</td>\n",
              "      <td>-323.660950</td>\n",
              "      <td>-2.605916</td>\n",
              "      <td>-2.813245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>108</td>\n",
              "      <td>0.182900</td>\n",
              "      <td>0.899943</td>\n",
              "      <td>-1.053020</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.952963</td>\n",
              "      <td>-203.585938</td>\n",
              "      <td>-228.325348</td>\n",
              "      <td>-2.465954</td>\n",
              "      <td>-2.610229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>109</td>\n",
              "      <td>0.219900</td>\n",
              "      <td>0.795095</td>\n",
              "      <td>-1.129289</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.924384</td>\n",
              "      <td>-185.338791</td>\n",
              "      <td>-262.326508</td>\n",
              "      <td>-2.308619</td>\n",
              "      <td>-2.451966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>0.256900</td>\n",
              "      <td>0.728312</td>\n",
              "      <td>-0.873666</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.601979</td>\n",
              "      <td>-181.493530</td>\n",
              "      <td>-167.788956</td>\n",
              "      <td>-2.650259</td>\n",
              "      <td>-2.627371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>111</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>1.187222</td>\n",
              "      <td>-1.249772</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.436995</td>\n",
              "      <td>-304.987030</td>\n",
              "      <td>-357.321716</td>\n",
              "      <td>-2.437974</td>\n",
              "      <td>-2.653895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>112</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.525752</td>\n",
              "      <td>-0.475295</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>1.001047</td>\n",
              "      <td>-97.024063</td>\n",
              "      <td>-129.096161</td>\n",
              "      <td>-2.403481</td>\n",
              "      <td>-2.571845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>113</td>\n",
              "      <td>0.336500</td>\n",
              "      <td>0.365618</td>\n",
              "      <td>-0.640655</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.006273</td>\n",
              "      <td>-111.093918</td>\n",
              "      <td>-75.943321</td>\n",
              "      <td>-2.735875</td>\n",
              "      <td>-2.555395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>114</td>\n",
              "      <td>0.081500</td>\n",
              "      <td>1.106894</td>\n",
              "      <td>-1.968600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.075494</td>\n",
              "      <td>-341.697296</td>\n",
              "      <td>-328.623779</td>\n",
              "      <td>-2.506866</td>\n",
              "      <td>-2.463103</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=114, training_loss=0.397163258179238, metrics={'train_runtime': 4017.5003, 'train_samples_per_second': 0.231, 'train_steps_per_second': 0.028, 'total_flos': 0.0, 'train_loss': 0.397163258179238, 'epoch': 2.94})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "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. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\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",
        "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n",
        "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n",
        "9. Gemma 6 trillion tokens is 2.5x faster! [free Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)\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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