diff --git "a/DPO_Zephyr_Unsloth_Example.ipynb" "b/DPO_Zephyr_Unsloth_Example.ipynb" new file mode 100644--- /dev/null +++ "b/DPO_Zephyr_Unsloth_Example.ipynb" @@ -0,0 +1,10442 @@ +{ + "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", + 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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", + " 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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
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\n", + " \n", + " \n", + " \n", + "
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