{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "o6RxqIirisTj" }, "source": [ "To run this, press \"Runtime\" and press \"Run all\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", " \n", " \n", " Join our Discord if you need help!\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n", "\n", "You will learn how to do [DPO data prep](#Data), and how to [train via `DPOTrainer`](#Train).\n", "To learn more about DPO, read TRL's [blog post](https://huggingface.co/blog/dpo-trl). We follow [Huggingface's Alignment Handbook](https://github.com/huggingface/alignment-handbook) to replicate [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "import torch\n", "major_version, minor_version = torch.cuda.get_device_capability()\n", "if major_version >= 8:\n", " # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", " !pip install \"unsloth[colab_ampere] @ git+https://github.com/unslothai/unsloth.git\"\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n", "pass\n", "\n", "!pip install \"git+https://github.com/huggingface/transformers.git\" # Native 4bit loading works!" ] }, { "cell_type": "markdown", "metadata": { "id": "QYds3fcii6gC" }, "source": [ "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n", "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n", "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n", "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n", "* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n", "* DPO requires a model already trained by SFT on a similar dataset that is used for DPO. We use `HuggingFaceH4/mistral-7b-sft-beta` as the SFT model. Use this [notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) first to train a SFT model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "E8-BWi7MzkRz", "outputId": "62b1cc0c-a494-4816-aff8-d2cf340a7691" }, "outputs": [ { "name": "stderr", "output_type": "stream", "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()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 385, "referenced_widgets": [ "e874eb7fb86d43a5b77a5dfacfa9d8a0", "da4e510666be477c841212f5017b47e0", "25a23224fd504c48bc4a20c6b47acb10", "8cf44fb3cbb745bea86d61189c5665a0", "08eeb449819d47409de21b90da03c70d", "6283d5ae17b544e8b49de72af49c171f", "78c999dc45a44436975ae35a6ba602fc", "c41bb81611974c11aa5bc31d6e649c65", "e2c7f0b1f20b4bda8269dcd30edcf4ac", "768a9a1adf9843c8afadc861016e9dd7", "9dfc6867aba4448fa534b8f74d2da3a1", "9e6b808ad9d243c69b099fffef673dec", "5e26903f37f44ccea54b031989bdee07", "78c354a390c54b0a990f052db95a70e3", "38e738f0e66c46aba21dba8f7eeed727", "ac130598ebcc4ae2a426b62a011775e8", "6b4d1d950cb14c1484ebede77c610e75", "b29322681da7490291118b138ebf28ba", "5dcc7b2a47e6495297d36855724940ed", "2c8c2043ab4c48e997c498fcb3169676", "158c8e46a9b34a7a89154ef484187eb2", "5aba62774bae4e15821623ac71700b70", "5ca45f817d784a8aa6c89969136ee53d", "f3b6a130e10e416ea8b95b80c264028d", "00273f529fc4482b83d90892421b2f38", "23888a5e3a9441f09f6e96943587c92f", "9176ce2d816c4faeb792aa2e95d149ff", "0b48053354ed49788897a6fff135cef6", "ebb8f01d7e834380ada8008f296207c3", "95abfe070a1042ceb459ddd9a719b4ed", "41a7293a45174bf69178d0e88aeb9f89", "42628252c650442d9824b684d8fac22f", "17ef306e2a27469da9086652b3acea14", "ecec31f77eab469aa61ead705557cdcb", "ee4c1dcd246c4818bf60bc6164bfaf3f", "db41489806bc426aa2259070d036e87f", "abf877d764b148d1a539be05a5ffa61a", "79fbf74966144b89b58c53329cf9bc55", "a2f1f703a0994bbe949948de631e8f9e", "aebf71c536714962827e11f17c6a6e16", "bda940f600d34640bb781db24841f944", "4a5a796382234e00a701ac3dcb5009ef", "e6cd6cca3a3e4378b8c28cb2c201ce65", "058a920f8b23400896363b9f799e5428", "abf769643beb4576be0ec06b40c3a39c", "d49c4bbd79764be6a367a529430bb21c", "66fda39ee94640f9bfbf95b277029f5f", "a91b988eba704f3d83b2427008360214", "c6984f94ef2946f8bf97184bb3f6569c", "e8402dfc1a8b41429e8015d0569fd3d2", "3d6b10bc206a4735b36e64d25ef8045e", "33585dca549944f3bae76a720aa2d0e2", "749f2a828c024012bc404f62b3110317", "2a5c7b2997744dafad3cfdd16fe931cf", "938ae07a3b014287a6b0f9d903c61b6b", "e29361925b634f968acb57d488fc849d", "86b2a0cba0f1497897d0cc8a4279cbfb", "cac455d66c424233bfcd8aef411d1180", "fb6a3fcef0e84843bc33bc9b929afbf1", "66859785d81d455e91032048226a01d4", "6e99c507e18d4d23adca6e9a23cf0004", "cdf03b41c0f94373a99fe372a5f13dc3", "eb797a1a19db4e898634d18f19b2056d", "fab1a0ae36644433b756857d36dd52f2", "928e0ff3f174436b817262603cc78b77", "7234655b163a4c3c80c28388a1f072fa", "788d1b6eb4904b94bcfd9e0929720d1f", "5b8655d89c63464c968da114b0077829", "7595ab61957945e192a988916b0fcec9", "5ad12f2b58204d3f8f77d12a4a2c04ec", "ed1bcf79e7f84a499f41b36cbe4f3a69", "319a5cd1e1f144c7b090eb78bc72a969", "71a02ae95f6745bcb68e25291a59cb4d", "9f25fb5cf7de47de8b6dd8c9213ec9e3", "ec2ab7b05c014f3bac6b15c12bde34d8", "ba337b31b13347b9be6e1a43fd0cd8aa", "2995caed8cd7422b9c3fbeacd11839d8" ] }, "id": "QmUBVEnvCDJv", "outputId": "0680b8c6-0dcb-447a-b5ce-557cb1b6a85b" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e874eb7fb86d43a5b77a5dfacfa9d8a0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "config.json: 0%| | 0.00/1.04k [00:00\\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": [ "\n", "### Data Prep\n", "We follow Huggingface's [Alignment Handbook](https://github.com/huggingface/alignment-handbook) for [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) and use the [Ultra Feedback dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), and sample 0.5% of it to speed things up. You can sample the full dataset for a full run." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 553, "referenced_widgets": [ "2b6fc1098a5944b0a24b217ae0afbde4", "6001cd0f400f41139dea8da6143e923c", "1df925fce5c84167b621ff0de7d3f5d9", "c168e892023e4bfa9817bb3e1d0ebd26", "922c78ac68b04f41a1994033d56af782", "0c4cb2aff30144229987978b34c2aca0", "9f30c0b8c55940f28157c09c86834504", "4b2e98b797c648089e72abd871e6b579", "33fa33cdcde342ebb5aab39d1831bf92", "21ae70b774094988b25768eb7a7db9c2", "c2d60fb0bb2c4a39a91b259c8caf88d2", "edca49569f0b45a3bda8327171d93d5d", "51f18d9993214dbea5c99b42cbd816bd", "fb834046447043518bbe944173f104de", "a536ba8737f7444d8ddef16ee2f56698", "ff90b7c5478d40c6a36d6021ab0c5708", "714365febd4b4c778cf3a300fd3ee8bc", "960e12d25c4a4e5c8d2f31ccc4d3d041", "e2f2ff4f89664ac4b52609e7ea90f3fd", "2232df7258c940efa25a0f789dc1721e", "6d9a36489c7a453abf1d90822649dacc", "2dfb2e2e7eb142058d6b0ef81b3dab64", "61d8de524f7a4070a5ae811b7c8cc92d", "d9527ff9d69f4268a2195524060a9dfa", "5f40887b20cd444b8fc646da40898c41", "26ac6197edea4476abc948042a9cb058", "8811335900fb4238b06abd62dffe70d7", "e627ac8f64b04b2493fec7a13c7b6eb6", "77560ba252fa4bf5961e6c8dc99bf88f", "3b6cdbd423554b99b27a59d55ae75e8d", "c6ccd287b7ef4aaabb25fe43d445fa85", "85af4c279f4e4efaa54ecc97bbe72e8e", "ca30d3124745449191e711dadce629f3", "68d33530886d4996ab8840db7329969e", "aaafd0ed672241df9406abe0a478ff1f", "192b08ba99484b38b301426837b0dea9", "888f40d51e044d429d2f363c72e34e1b", "366ec8fa7e0840daa0d6c24ac3f7a745", "3d2ad491df294f1581aedaf1aaf665f9", "7c1e77648fbe4807b7249067a0645658", "4e17bbdc4bdd4b34abc83c141607998e", "7831284a678340ca9ce6794ec0fdc1bc", "0010cc35659944efa5cccc638e0aaa0d", "0a578ffc2d8543c7a49fafbf23aff60c", "ca5724ddc9ad4a529aab73c11ee9c878", "1ee9c355b65e4c059ce870557f675e6c", "420dcbfb1c2846f48ace8a2b06db6aa6", "04ba536ded2e407e9d698df696d819d8", "8c0207d0b50740b1b6d8e14e882ff013", 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"8803985e095f45a9b0b66b4ab94883bb", "f3d39586bdb04cf9b4332bae94322f45", "db82b1debf674087b8675cc44c40f83a", "7692d345715b4c0282c04044ef5ec9a1", "f4a731b502714d5e87f5a9e8a4c4a260", "7230a076728248fc92bc9ef99ee7f9b4", "9a8b0c94fc6b44bfa90486816410bda7", "8546e894584949129f5c20c13d7d115f" ] }, "id": "r6bUnxe6N3pf", "outputId": "978ac892-91b0-4131-cab9-8a5d99af7edb" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2b6fc1098a5944b0a24b217ae0afbde4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading readme: 0%| | 0.00/5.98k [00:00 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": "928a60d3-1b8f-4b14-a32c-2675d46984e1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('<|system|>\\n'\n", " '\\n'\n", " '<|user|>\\n'\n", " 'List two natural resources which was made in the factory.\\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.\\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?\\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": "45c7035e-f016-46ab-9ad4-b0ac389fcc40" }, "outputs": [ { "name": "stderr", "output_type": "stream", "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", " use_gradient_checkpointing = True,\n", " random_state = 3407,\n", " max_seq_length = max_seq_length,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "-kyd_iyz7DUM" }, "source": [ "\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": [ "f29440fa274c43809ce9b008f8a61ed9", "b5ed6177daa74fb5ab8e28a050d5fcfd", "2c44b6297394458f8c56ee66e97cbd56", "cfbbbeb2d66d4e80adb96bf3f8be2ff7", "64d0811807b74a9c99d09390612287de", "7af647b06e3c4ea69a3b239098fd64cf", "4ce289d0f23449dab2dc5455cac6182e", "9bdb645f28cf472cb00e5ba139f7347a", "baed51e023c24d40b4d463894cc6a067", "a049050103b24a3bb0ff201c87f42fc4", "59ee90a791574570bd1c1327d54e30f1" ] }, "id": "QtoqUw80QDV0", "outputId": "4afaedc7-7063-4160-fca2-ecb71bad2e66" }, "outputs": [ { "name": "stderr", "output_type": "stream", "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" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f29440fa274c43809ce9b008f8a61ed9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map: 0%| | 0/309 [00:00\n", " \n", " \n", " [114/114 1:10:45, Epoch 2/3]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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StepTraining Lossrewards / chosenrewards / rejectedrewards / accuraciesrewards / marginslogps / rejectedlogps / chosenlogits / rejectedlogits / chosen
10.6931000.0000000.0000000.0000000.000000-297.338806-218.968842-2.758142-2.924523
20.6931000.0000000.0000000.0000000.000000-237.602417-217.613892-2.731790-2.913610
30.6916000.0031990.0000520.8750000.003147-172.792282-202.696640-2.464586-2.728166
40.695100-0.0015570.0024100.125000-0.003966-117.720329-170.735352-2.592084-2.805901
50.693600-0.001848-0.0009510.500000-0.000897-197.054352-338.753784-2.541833-2.483452
60.6935000.0017330.0023730.375000-0.000640-279.668030-188.664688-2.870125-2.633015
70.694400-0.0014850.0009830.375000-0.002468-157.641891-236.214722-2.510456-2.838329
80.695900-0.0012330.0041720.250000-0.005405-451.754211-493.157379-2.676884-2.677660
90.6919000.0027810.0002740.5000000.002507-198.049500-405.621704-2.366738-2.639533
100.6919000.001583-0.0008670.7500000.002450-192.027969-238.243286-2.734776-2.765939
110.6929000.000210-0.0002820.5000000.000492-279.060547-242.698013-2.741585-2.707133
120.6907000.002133-0.0028650.8750000.004998-293.286072-297.428986-2.636093-2.797532
130.6933000.0007930.0011400.375000-0.000347-422.437988-440.254791-2.782841-2.722314
140.693800-0.001575-0.0003110.375000-0.001263-229.166489-517.634277-2.761792-2.876637
150.6932000.0011830.0012570.250000-0.000073-229.948364-368.679657-2.687079-2.848794
160.6926000.0025480.0013840.6250000.001164-153.684021-214.476501-2.553330-2.669835
170.6913000.002753-0.0009080.8750000.003661-145.049057-178.315857-2.551767-2.745412
180.693300-0.001510-0.0012900.375000-0.000220-193.493317-307.417603-2.546383-2.794016
190.6917000.0051410.0022400.7500000.002900-248.701904-226.629242-2.847236-2.759187
200.6925000.0050210.0037000.6250000.001321-265.856720-421.315674-2.905803-2.947715
210.694900-0.0004420.0029560.375000-0.003398-156.539444-202.226196-2.447355-2.538493
220.6898000.002207-0.0044260.6250000.006633-240.908142-258.819641-2.661672-2.760096
230.6873000.007077-0.0047230.8750000.011800-172.381485-306.969055-2.421829-2.795570
240.6936000.0035910.0044610.500000-0.000870-180.786728-225.256119-2.247471-2.371035
250.6887000.002078-0.0067810.8750000.008858-109.244751-135.704803-2.537647-2.694038
260.6923000.0040200.0023250.6250000.001695-218.140793-216.836838-2.350788-2.309595
270.6885000.007711-0.0017270.8750000.009437-253.585098-276.549164-2.410436-2.620286
280.6934000.0016740.0021270.375000-0.000453-241.141602-239.870850-2.711540-2.787073
290.6884000.001827-0.0077940.7500000.009621-176.143845-202.277985-2.833032-2.738228
300.6823000.017523-0.0044491.0000000.021972-289.187653-317.038971-2.557965-2.777354
310.6920000.0029310.0004760.8750000.002455-250.866531-230.197632-2.658638-2.652516
320.6847000.006979-0.0101720.7500000.017150-182.629639-287.150208-2.427922-2.839752
330.6885000.0100150.0002860.7500000.009729-394.385437-513.798523-2.927996-2.907134
340.6876000.000746-0.0104270.8750000.011174-213.292419-186.554916-2.781289-2.641831
350.694400-0.0001880.0023970.500000-0.002585-210.052170-83.598724-2.734563-2.323755
360.6767000.020572-0.0129790.7500000.033551-278.073486-431.871216-2.437477-2.656088
370.6907000.004792-0.0001680.7500000.004959-141.254837-138.760544-2.480301-2.554194
380.6871000.010404-0.0018650.8750000.012269-199.933395-166.619339-2.244387-2.343439
390.6855000.004061-0.0114040.8750000.015465-230.984711-224.525558-2.661340-2.720686
400.6730000.014156-0.0269981.0000000.041154-260.884521-176.705002-2.687092-2.736533
410.6812000.013829-0.0101900.8750000.024019-183.548355-199.252533-2.620351-2.470730
420.6692000.031616-0.0173440.8750000.048961-180.883789-351.244385-2.498347-2.774542
430.6854000.003653-0.0119421.0000000.015595-128.027740-116.247787-2.753370-2.723312
440.6739000.028433-0.0113280.8750000.039760-225.523666-322.012604-2.543076-2.699420
450.6622000.034732-0.0285471.0000000.063279-237.588928-289.570129-2.249139-2.591806
460.6590000.036781-0.0327901.0000000.069571-196.792236-265.052399-2.553383-2.715359
470.6725000.029729-0.0122671.0000000.041996-139.111206-245.212021-2.533509-2.613470
480.6711000.025400-0.0194371.0000000.044837-156.875183-217.185425-2.432833-2.575721
490.6611000.035756-0.0304021.0000000.066158-213.846985-212.789627-2.647654-2.714958
500.6632000.021139-0.0401361.0000000.061275-348.077393-312.627441-2.757344-2.675975
510.6533000.038236-0.0433191.0000000.081555-395.500549-342.811859-2.567836-2.708135
520.6743000.021404-0.0169691.0000000.038373-226.740570-203.308228-2.689174-2.713107
530.6757000.022191-0.0133471.0000000.035538-127.214119-299.697296-2.689854-2.747266
540.6580000.041239-0.0308041.0000000.072043-234.094452-341.478027-2.658128-2.845522
550.678700-0.002169-0.0313711.0000000.029202-182.520050-130.642044-2.751769-2.653854
560.6591000.035994-0.0338551.0000000.069849-206.904007-332.754486-2.633317-2.746063
570.6408000.072543-0.0359490.8750000.108491-347.537323-594.679443-2.610380-2.938568
580.6568000.031076-0.0434051.0000000.074480-242.280487-208.374329-2.675403-2.823531
590.6465000.053982-0.0435621.0000000.097543-221.657608-250.691666-2.548671-2.671937
600.6517000.044328-0.0409831.0000000.085311-237.457428-243.843521-2.340865-2.417122
610.6687000.016157-0.0337560.8750000.049913-181.521423-230.741577-2.583697-2.671347
620.6505000.042586-0.0457361.0000000.088322-227.009842-248.078140-2.601808-2.666926
630.6663000.039459-0.0154920.8750000.054951-239.231415-254.349426-2.788095-2.922115
640.6601000.036744-0.0314290.8750000.068173-255.638794-338.323608-2.834287-2.895520
650.6558000.030527-0.0464431.0000000.076970-236.972427-329.191803-2.574853-2.719908
660.6697000.016352-0.0320690.8750000.048421-113.009361-182.456314-2.535565-2.650409
670.6565000.035327-0.0399271.0000000.075254-296.490997-225.676331-2.226444-2.276212
680.6501000.041216-0.0485751.0000000.089791-187.233826-246.121643-2.574435-2.739054
690.6549000.051283-0.0281920.8750000.079475-308.786011-250.258072-2.803828-2.665385
700.6561000.053900-0.0227470.8750000.076647-225.490479-310.644623-2.522727-2.745466
710.6628000.037783-0.0242221.0000000.062006-209.903839-270.441864-2.677671-2.789300
720.6602000.043013-0.0242641.0000000.067276-260.596680-256.450012-2.888245-2.899030
730.6571000.063199-0.0110270.8750000.074227-369.544830-382.099243-2.595025-2.764428
740.6375000.069101-0.0474020.8750000.116503-141.736923-422.295044-2.328428-2.619377
750.6468000.034380-0.0614201.0000000.095800-338.911743-257.112823-2.674836-2.631948
760.6801000.016118-0.0102620.7500000.026380-153.983215-192.856201-2.583967-2.739872
770.6535000.037388-0.0447840.8750000.082172-237.917175-373.211090-2.670840-2.840366
780.6822000.005111-0.0170311.0000000.022142-90.056511-75.676575-2.589334-2.678804
790.6492000.048614-0.0424611.0000000.091075-197.482025-216.437912-2.580309-2.688187
800.6381000.067506-0.0473591.0000000.114865-306.641571-243.717010-2.727369-2.713167
810.6304000.046541-0.0859171.0000000.132458-205.164703-226.345978-2.571737-2.649376
820.6399000.046804-0.0636671.0000000.110471-255.351227-232.954865-2.444653-2.546993
830.6479000.043133-0.0505451.0000000.093678-143.658051-267.885437-2.450764-2.622156
840.6234000.066124-0.0815651.0000000.147689-269.404785-331.960785-2.679103-2.777452
850.6253000.093795-0.0486011.0000000.142396-250.066422-298.300201-2.747368-2.852850
860.6522000.047347-0.0374431.0000000.084790-102.290405-187.495880-2.346936-2.561124
870.5971000.108838-0.0949471.0000000.203784-415.374237-581.638367-2.975214-2.911619
880.6096000.089707-0.0884181.0000000.178125-398.111847-336.481079-2.793868-2.568107
890.6359000.066105-0.0535451.0000000.119650-250.390030-384.719116-2.739915-2.677882
900.6110000.094179-0.0797891.0000000.173968-382.315125-394.539276-2.684922-2.875659
910.6254000.085292-0.0568671.0000000.142159-205.984009-335.645020-2.549550-2.717720
920.6218000.068006-0.0816111.0000000.149617-339.634094-360.694153-2.709456-2.903818
930.6557000.047537-0.0295531.0000000.077090-115.142799-260.806763-2.594357-2.715160
940.6396000.055383-0.0578901.0000000.113273-209.325363-226.800201-2.263216-2.443685
950.6446000.045497-0.0555991.0000000.101096-227.701065-268.792511-2.571428-2.790503
960.6627000.024437-0.0378910.8750000.062327-188.535080-184.081863-2.675784-2.853757
970.6340000.036011-0.0890011.0000000.125011-203.472717-158.047913-2.858229-2.802696
980.6452000.041265-0.0586300.8750000.099896-147.259262-241.107040-2.673885-2.728743
990.6574000.027796-0.0459030.8750000.073699-152.992294-178.524979-2.415729-2.726150
1000.6315000.043453-0.0851621.0000000.128614-193.779343-156.529266-2.446479-2.537483
1010.6447000.059261-0.0422161.0000000.101477-239.048523-249.522369-2.556858-2.802905
1020.6356000.072766-0.0485410.8750000.121307-247.841553-236.061722-2.710063-2.557176
1030.6565000.035755-0.0394881.0000000.075243-145.012451-165.547180-2.728357-2.772579
1040.6529000.039176-0.0439770.8750000.083153-198.680023-231.717743-2.430846-2.528418
1050.6166000.104496-0.0572141.0000000.161710-248.830582-398.110840-2.472265-2.786351
1060.6408000.069526-0.0393451.0000000.108871-230.877518-378.959503-2.584360-2.771723
1070.6279000.077549-0.0602031.0000000.137752-184.436127-331.978729-2.613068-2.810034
1080.6411000.062340-0.0454821.0000000.107821-193.510544-236.701370-2.472780-2.615651
1090.6396000.063451-0.0501690.8750000.113619-174.547577-269.642944-2.326431-2.456072
1100.6508000.047151-0.0412711.0000000.088421-173.169586-174.600586-2.659404-2.637673
1110.6289000.089443-0.0453570.8750000.134800-292.942871-368.299500-2.448925-2.652367
1120.6724000.028686-0.0134180.8750000.042104-92.405304-134.066833-2.416434-2.578195
1130.6731000.017251-0.0235171.0000000.040768-104.922539-79.426987-2.740997-2.566977
1140.6187000.071029-0.0870800.8750000.158109-322.882080-338.982452-2.519094-2.479210

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "TrainOutput(global_step=114, training_loss=0.6645530284496776, metrics={'train_runtime': 4282.1008, 'train_samples_per_second': 0.216, 'train_steps_per_second': 0.027, 'total_flos': 0.0, 'train_loss': 0.6645530284496776, 'epoch': 2.94})" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "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 channel! If you find any bugs or issues, feel free to make an issue on either Unsloth, TRL or PEFT! Thanks!\n", "

\n", " \n", " \n", " \n", "
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