{ "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", "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n", "!pip install unsloth\n", "# Get latest Unsloth\n", "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"" ] }, { "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()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QmUBVEnvCDJv", "colab": { "base_uri": "https://localhost:8080/", "height": 491, "referenced_widgets": [ "4c7a9e6327fc4b839f1e2eee705e1574", "904fef1ac7404cc8a33e65ffe8938c8b", "f7562580501845e9a67ab9a8b4a96ba0", "96f9847627504ae7abe7461e4c0b3cff", "5165aa69bef148af9717d90cc5a870b1", "e6dfcf0456824556ad09fe7945674a14", "80568bca0c604eaaab2933fc76da2bf0", "079284acd5c6454eb03d8876a62e6f12", "1d917d774780421fbdca680009a706ac", "342cf2a9d6674b4d94fdbaf3b63b9b79", "16ace0251b954bc08a33eaf3b2a47216", "2cb8afe9c6e2441fa4e232146ecd08b4", "bc745fa3da114007af35ea15dbd7ab7d", "127ed424448840cea8b14fc9997b9cde", "803f5904c00d4cde8729226139e3c258", "4a273a69979b466dbf1c987552101fbf", "68893d4db1f74557932d8b24e0f72820", "3ccdc55cf6384695967eaf79325b1aef", "859bc87dfda746819298e213e4e0b067", "32478ec41d044773b70eba24e2ac0b43", "5aeb33ba799149c1ad6e580a2a1c0a51", "a2e31ba35dcf41c79c72dfd431da781f", "f7c089469828416aa89ade0cfa5a04cc", "3a52a2ec426b4fc2aaad23c7d8ce6d2b", "c298081665e844539b8d366325dbdab5", "65b1731e23364d4ea0bb292550d4a403", "5f6fd4c19b34458aa8f2019a25bfd605", "33dda465ac2f42fb8846171a974193ac", "bf6bc9099d1a475884be6f15115f01ad", "cdc411110d1b405ea12342b57fd8de30", "84d445570b31490fb658b08f819dd6a7", "ceb3830da80146da8fbe6423d25fd8a3", "d58599afce36484c8508c16beba13e36", "d43eea508a4d496e9460f01931659b52", "b146c0bbd15644678198977d63edebb7", "aa0323eef053447b9836931535984912", "effec7acec3d4b5384bcf481e3263b6c", "03ec8d28c0f34116b42e4e0dc65e3dd3", "9fd5df9115cf4deb8a36daf4a654bfc7", "cd6e07c3984a4e0e920d87c6ebd6ad1c", "b0831169530b402cac31b1e60484fbb2", "ccabf636e15e471cb82bb431b5ab4b5c", "e4120afe895742848cf756fd58cb42f8", "fe6eabd38a614113b36bafd1f8ce6920", "9b12628a46c546aebe2c749085ba2e61", "63d48b48f4ce449e85a55477df0b6967", "068c416eaaed476eacffb8f2f343e08f", "9fd3fe23f14b4403a18ec7d73b64dc6a", "cf639313cba7442893fdf880d368616d", "4bd0cf4d31604194a7761ea0345cc0a4", "783ad9a7097e4fcdb768c8ecb1f21227", "2557c3090a8d4760ae844fb518e1b555", "b508872df72d45beb26a625c7499cc8b", "cbc6fe3cc12a4d79ba08b67f23e5ce01", "d0d340fa13a54bcf9f1130cc2fc09cc8", "5d7fce052f8e4937adaaa670be8c1ae4", "77939b191e82425da0a5908fdbca2a2d", "30dc6079591b4908a57f5e285ebeda68", "960bd84fc3fe4d8eb7e79ecba0f3edf6", "bb7c7c1866af4641bc07d1ff0a5cd069", "0dc6aefaa24043768d656f5a61d79545", "d27056ab29e04c448fc62f9f18cbb82d", "926319902fe84c5095e0b5bd4b3e8187", "9f660e1b7b9c4ae7922d51353783acf9", "82d00942a39e465c8a6f869ef97cc250", "b9e2b829291247619a7b37c9f544d395", "3f6ccae685e34d05be9fde5538dc48cf", "3df8638084104cb7a0530a96ec6201af", "06d1fd85a45541609d11ec7f0bd3b444", "a9d05a8c3aaf4fe0a2f061c3eeb48b44", "8095a44a62804d6d86c24d62b9e1a1c1", "f61b85b63d5147db9f1040f30f4a3034", "e152d696872242ab8e0bc00ce9b50142", "623e912cfe404e69a64992fbb1c1bc47", "69f9e4bf820047658699d55d84d682c0", "a8306ce933304694aba188495743c2f4", "1cd708f679de4d319af26797aecca94d" ] }, "outputId": "f04a3ea4-0d0f-424d-8265-0fb99fd17ef3" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/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": [ "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": [ "5e875e8a442a41e8afef4ad494ef6897", "7adc769f384346b3a5835c73542a2d62", "fccd2f68b05d40c9b359133fbf383b1c", "2a197f7c6c4746e1a7fded1e0539bf29", "37a2eb8b91ed42a1be411ecb9079a082", "833097685a694d1e9cd5aa016b6b7d73", "4e48c220077d4546a51c02df9397a084", "f6200f86a4084c5f978f62481100b7a3", "530cd47150094e7fb925eb24ec071b5e", "9f6197721b90446091bf8b97de656d4b", "35edd118e5e54d19b87aa7e966e3c9fe", "3b258a09c7e0462e9d9fefa6746ebb9f", "ade70be3dc49443581a295e89faa65c6", "ad03a2ff7f974fa3a0f1be2dee56c9a0", "727d7d710957497c8bb91d52a04a00f3", "347ae8c1baf54004953d0eb9325f39f8", "6b419485aabe49c6bed15b5bf1316e43", "9eabdceae6484ba7b543ed1fa4f65141", "6cce73a8bdda4852968c807354a6d212", "7953ace8fec34f358506307a3efb208d", "ba6f8296f866419384de64f6e2cc727e", "d3fcca8f0d8c41a3b78bd2470542e950", "243999bf48174e72ae981effb01117ac", "77e6abd454c94559825201f34d5de044", "7522d496e9dd41828a4daddb9dd37883", "13b208926d4a4bae854e057bc9913100", "78ab252184ac46eba3e82c076f6b914a", "80308f7c18b244a698614fa86bc4c564", "9473389066664ed4aa2089c78adbb20d", "4a6807afa14049c781c7549467b85e1c", "4cc5ba58138d47129b776b04bc2d6646", "90b49ecfaf4a44409b0b405271c9797c", "9032335e102f465ea2e1adcc47462a8c", "6730cd77f7fa416b97f20fc5f5660488", "5a40fa44b76f44f88a24835c1bf00e56", "57f9a1fd43024e8bbe7c0df0867c8e12", "8f6c6f07bd254516ae4af2f80d2492f2", "5c28f5682c37476384f6a985d42e8360", "900f8248c6074ea5a93ef9372da4e958", "39a4f06a16f149789507232e4e28acb6", "c558b2bb11274910888b08138d9ee195", "3635323c2caf4f7ab2034306cdac61cb", "d855ad7e63ae45089832de406a3b00e6", "bf25365768a0441e8e4121ea1966971d", "757089cbb90f4d538e4a98a9ecee7dc6", "d28fb551bdac4910a04bb39b500b9228", "82cc0b43ea6d4ff4bddce1a1536fef62", "379878ecc7e44b448425c4155e106477", "5f8ada6bd56b4d79861a8f2bc36b7785", 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"21687939a40a491395c37bec223c37fa", "440101772f084da5b3e5724fb28c52d1", "32f7c34cd24c4d83976c96ee0cdc05cf", "57e3acfbf42c41d2b2a3774247e046b6", "bd508aa803f94073af7a91332e738668", "ddd131d700884af596f2b77c921f48c5", "8c785cb20818402a829e54ff3467e03f", "3d17ac67bb8a4fc9b4709f89de589fcd" ] }, "id": "r6bUnxe6N3pf", "outputId": "ff62990d-3e46-4a6d-826f-d4533b596a98" }, "outputs": [ { "output_type": "display_data", "data": { "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": "5e4e2858-9ac2-4023-f554-13bfeec367a8" }, "outputs": [ { "output_type": "stream", "name": "stdout", "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": "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", " use_gradient_checkpointing = True,\n", " random_state = 3407,\n", " use_rslora = False, # We support rank stabilized LoRA\n", " loftq_config = None, # And LoftQ\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "-kyd_iyz7DUM" }, "source": [ "\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" ], "text/html": [ "\n", "
\n", " \n", " \n", " [114/114 1:06:24, Epoch 2/3]\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
30.6922000.001937-0.0000080.6250000.001945-172.792877-202.709259-2.464616-2.728198
40.6927000.000855-0.0001300.7500000.000985-117.745728-170.711212-2.591983-2.805891
50.6925000.001272-0.0000440.5000000.001315-197.045288-338.722626-2.541809-2.483452
60.693700-0.0001550.0010340.500000-0.001189-279.681396-188.683563-2.870259-2.633069
70.692600-0.001525-0.0026550.6250000.001130-157.678253-236.215118-2.510528-2.838598
80.6925000.0032460.0019370.5000000.001309-451.776550-493.112579-2.677159-2.677977
90.693600-0.003702-0.0027730.500000-0.000929-198.079987-405.686523-2.366659-2.640016
100.6859000.009754-0.0048010.7500000.014555-192.067322-238.161560-2.735095-2.766379
110.6901000.006233-0.0001020.6250000.006335-279.058716-242.637787-2.741516-2.707231
120.6768000.029851-0.0037070.8750000.033558-293.294495-297.151794-2.636089-2.797787
130.6849000.0359300.0185580.6250000.017372-422.263824-439.903442-2.782973-2.722686
140.691100-0.007306-0.0126850.5000000.005379-229.290237-517.691650-2.761815-2.876947
150.6757000.0522420.0163290.8750000.035913-229.797638-368.169067-2.686517-2.849208
160.6710000.0496200.0028340.7500000.046786-153.669510-214.005783-2.551779-2.668840
170.6592000.031351-0.0393430.7500000.070693-145.433411-178.029861-2.550349-2.744968
180.6709000.042720-0.0036640.6250000.046384-193.517059-306.975281-2.544961-2.794021
190.6824000.0496490.0265230.6250000.023126-248.459091-226.184174-2.847072-2.758221
200.6679000.0970720.0450650.8750000.052007-265.443054-420.395142-2.906176-2.950474
210.7124000.0095290.0392240.500000-0.029695-156.176773-202.126480-2.445233-2.536827
220.6647000.027259-0.0323890.7500000.059647-241.187759-258.569122-2.660511-2.759560
230.6124000.111760-0.0642760.7500000.176036-172.977020-305.922211-2.420000-2.796694
240.6752000.0642460.0218860.3750000.042360-180.612473-224.649567-2.242796-2.368723
250.6383000.059822-0.0552700.8750000.115092-109.729645-135.127365-2.535609-2.693033
260.6821000.0448420.0196940.6250000.025148-217.967117-216.428604-2.346493-2.305699
270.6017000.134719-0.0653190.8750000.200038-254.221039-275.279114-2.407781-2.619088
280.6906000.026908-0.0051210.5000000.032030-241.214081-239.618500-2.711054-2.787158
290.6406000.031898-0.0916800.6250000.123578-176.982712-201.977280-2.830093-2.735838
300.5594000.228695-0.0727010.8750000.301397-289.870209-314.927216-2.553982-2.775172
310.6797000.034291-0.0202820.8750000.054574-251.074112-229.884064-2.657125-2.649465
320.6099000.073758-0.1133770.7500000.187135-183.661713-286.482422-2.423700-2.839998
330.6464000.127758-0.0202950.6250000.148053-394.591248-512.621033-2.927793-2.909138
340.6374000.028342-0.0901540.8750000.118495-214.089691-186.278961-2.780502-2.638843
350.6820000.006539-0.0167020.5000000.023241-210.243164-83.531448-2.730125-2.316058
360.5332000.213769-0.1952081.0000000.408977-279.895813-429.939270-2.432103-2.655017
370.6563000.063138-0.0163030.7500000.079441-141.416199-138.177078-2.473918-2.547494
380.6510000.094509-0.0193900.8750000.113899-200.108643-165.778290-2.236758-2.336314
390.5521000.149231-0.2008120.8750000.350043-232.878784-223.073883-2.657633-2.716261
400.4006000.367951-0.6397711.0000001.007722-267.012238-173.167068-2.681762-2.732720
410.4370000.394438-0.2883461.0000000.682784-186.329910-195.446442-2.616049-2.464377
420.3732000.502430-0.3947051.0000000.897135-184.657394-346.536224-2.492926-2.774618
430.5272000.093942-0.3000911.0000000.394033-130.909210-115.344902-2.750227-2.718995
440.3940000.518654-0.3854591.0000000.904113-229.264954-317.110352-2.540475-2.699816
450.2812000.686930-0.6968121.0000001.383742-244.271576-283.048157-2.240731-2.588020
460.2422000.650970-0.7057721.0000001.356742-203.522079-258.910522-2.546949-2.711936
470.3945000.561647-0.3382731.0000000.899920-142.371277-239.892838-2.526323-2.609220
480.3748000.471081-0.4278501.0000000.898931-160.959320-212.728607-2.422826-2.570896
490.3270000.703227-0.6393341.0000001.342561-219.936340-206.114929-2.642008-2.712688
500.2860000.396052-0.9694041.0000001.365456-357.370056-308.878326-2.753402-2.674081
510.1918000.713322-1.0663141.0000001.779635-405.730530-336.061005-2.561202-2.704443
520.3787000.434130-0.5077351.0000000.941865-231.648224-199.180984-2.683644-2.706589
530.4468000.479577-0.3294271.0000000.809003-130.374908-295.123444-2.684445-2.744673
540.2730000.722516-0.6103641.0000001.332880-239.890060-334.665283-2.653185-2.843446
550.4524000.024534-0.6295510.8750000.654085-188.501862-130.375015-2.748917-2.645331
560.2661000.659037-0.7109141.0000001.369951-213.674606-326.524048-2.625597-2.745638
570.2104001.066818-0.8941501.0000001.960968-356.119354-584.736694-2.604546-2.943971
580.2166000.581929-0.9848441.0000001.566772-251.694885-202.865784-2.672240-2.820692
590.2520000.775185-0.8553211.0000001.630505-229.775177-243.479630-2.541421-2.667074
600.2357000.678536-0.8906561.0000001.569192-245.954163-237.501450-2.326149-2.406452
610.3708000.287420-0.7023320.8750000.989753-188.207214-228.028946-2.577148-2.665427
620.2629000.559217-0.9179121.0000001.477129-235.731598-242.911835-2.600375-2.666599
630.3200000.677785-0.5267131.0000001.204498-244.343613-247.966156-2.786634-2.922563
640.2945000.573417-0.7644631.0000001.337880-262.969116-332.956848-2.830733-2.896999
650.2443000.549664-1.0878091.0000001.637473-247.386093-324.000397-2.571335-2.723726
660.4765000.178537-0.5667510.8750000.745289-118.356186-180.834473-2.530770-2.648711
670.2236000.633353-1.1624511.0000001.795804-307.716217-219.696091-2.212558-2.263221
680.2695000.654256-1.0308381.0000001.685093-197.056473-239.991241-2.563220-2.735702
690.2584000.750621-0.8535531.0000001.604174-317.039642-243.264709-2.802447-2.661048
700.2948000.779875-0.5899200.8750001.369795-231.162201-303.384857-2.517439-2.746666
710.3017000.602643-0.6357651.0000001.238408-216.019257-264.793274-2.675880-2.789920
720.2642000.664522-0.7868961.0000001.451418-268.222992-250.234924-2.888840-2.899784
730.1945001.065459-0.7016781.0000001.767137-376.451355-372.076660-2.590312-2.763450
740.2704000.859751-0.8199570.8750001.679707-149.462494-414.388550-2.319313-2.621300
750.2436000.477052-1.3593351.0000001.836387-351.890869-252.686096-2.666570-2.624575
760.5218000.133218-0.2856690.7500000.418887-156.737289-191.685196-2.572031-2.732099
770.2518000.547161-0.9815021.0000001.528663-247.284332-368.113342-2.663839-2.841396
780.4836000.198344-0.4287331.0000000.627078-94.173538-73.744232-2.580647-2.671262
790.2352000.804393-0.9384741.0000001.742867-206.442154-208.880112-2.574049-2.685945
800.2149000.959676-1.0440841.0000002.003760-316.608826-234.795319-2.721649-2.706306
810.1470000.606548-1.5988091.0000002.205357-220.293610-220.745911-2.564118-2.648017
820.1586000.695428-1.4124611.0000002.107888-268.839172-226.468628-2.439530-2.542942
830.2492000.614686-0.9543721.0000001.569058-152.696320-262.169922-2.440640-2.618743
840.1750000.862260-1.4437031.0000002.305963-283.026184-323.999420-2.673517-2.778322
850.1523001.219675-1.1419701.0000002.361645-261.000122-287.041412-2.748819-2.854210
860.2615000.703294-0.7668851.0000001.470178-109.584824-180.936401-2.330353-2.552672
870.0570001.309202-1.8126321.0000003.121835-432.551086-569.634766-2.979058-2.924992
880.1134001.130503-1.8258131.0000002.956316-415.485809-326.073120-2.788409-2.560507
890.1812000.784262-1.2307241.0000002.014986-262.161804-377.537537-2.736547-2.671477
900.1021001.122743-1.4968601.0000002.619603-396.485840-384.253632-2.682523-2.880081
910.1454001.151220-1.0967531.0000002.247973-216.382874-324.985718-2.543500-2.719545
920.1356000.915327-1.4242271.0000002.339553-353.060242-352.221008-2.704442-2.904936
930.2656000.771701-0.6724461.0000001.444147-121.571732-253.565125-2.585968-2.709661
940.2103000.828890-1.1822841.0000002.011175-220.569305-219.065125-2.253163-2.439387
950.1673000.742948-1.2230571.0000001.966005-239.375641-261.817993-2.562728-2.784313
960.2891000.318967-1.0594281.0000001.378395-198.750443-181.136566-2.669433-2.856450
970.1931000.473150-1.6728791.0000002.146029-219.311478-153.676514-2.851680-2.796827
980.2078000.637917-1.2000111.0000001.837928-158.673050-235.140503-2.663034-2.721454
990.2604000.444965-0.9460551.0000001.391021-161.993805-174.353287-2.403941-2.721178
1000.1450000.601810-1.6485751.0000002.250385-209.413452-150.945694-2.433372-2.527679
1010.2024000.970933-0.9494731.0000001.920407-248.121094-240.405640-2.550437-2.803463
1020.1444001.012043-1.2787881.0000002.290831-260.144043-226.668945-2.700693-2.540809
1030.2343000.627674-0.9607621.0000001.588436-154.225189-159.628006-2.725933-2.771981
1040.2413000.620007-1.0557921.0000001.675798-208.798172-225.909439-2.412787-2.513871
1050.1301001.341354-1.1761101.0000002.517464-260.019531-385.742279-2.458776-2.786789
1060.1846000.906436-0.9388551.0000001.845291-239.872620-370.590424-2.573937-2.770343
1070.1930000.909325-1.1228831.0000002.032207-195.062927-323.660950-2.605916-2.813245
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" ] }, "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", "

\n", " \n", " \n", " Support our work if you can! Thanks!\n", "
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