{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "o6RxqIirisTj" }, "source": [ "Thanks to Unsloth this notebook can run on free Colab T4 instances.\n", "\n", "This notebook is tested on Runpod.\n", "\n", "- Image: `runpod/pytorch\n", ":2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04`\n", "- GPU: `NVIDIA RTX3090` for $0.26/hr" ] }, { "cell_type": "markdown", "source": [ "Run the next 2 cells to install Unsloth and its dependencies for Runpod, or the one after for Colab." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting unsloth[cu118-ampere]@ git+https://github.com/unslothai/unsloth.git\n", " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-install-a96gi2s8/unsloth_bbc08fa32528419089350c216e1d50ee\n", " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git 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safetensors, regex, pyarrow-hotfix, pyarrow, multidict, mdurl, fsspec, frozenlist, einops, docstring-parser, dill, async-timeout, yarl, pandas, multiprocess, markdown-it-py, huggingface-hub, bitsandbytes, aiosignal, xformers, tokenizers, rich, flash-attn, aiohttp, accelerate, tyro, transformers, peft, datasets, trl\n", " Attempting uninstall: wheel\n", " Found existing installation: wheel 0.41.3\n", " Uninstalling wheel-0.41.3:\n", " Successfully uninstalled wheel-0.41.3\n", " Attempting uninstall: fsspec\n", " Found existing installation: fsspec 2023.4.0\n", " Uninstalling fsspec-2023.4.0:\n", " Successfully uninstalled fsspec-2023.4.0\n", "Successfully installed accelerate-0.27.2 aiohttp-3.9.3 aiosignal-1.3.1 async-timeout-4.0.3 bitsandbytes-0.42.0 datasets-2.17.1 dill-0.3.8 docstring-parser-0.15 einops-0.7.0 flash-attn-2.5.5 frozenlist-1.4.1 fsspec-2023.10.0 huggingface-hub-0.21.2 markdown-it-py-3.0.0 mdurl-0.1.2 multidict-6.0.5 multiprocess-0.70.16 ninja-1.11.1.1 pandas-2.2.1 peft-0.9.0 pyarrow-15.0.0 pyarrow-hotfix-0.6 pytz-2024.1 regex-2023.12.25 rich-13.7.1 safetensors-0.4.2 scipy-1.12.0 sentencepiece-0.2.0 shtab-1.7.0 tokenizers-0.15.2 tqdm-4.66.2 transformers-4.38.1 trl-0.7.11 tyro-0.7.3 tzdata-2024.1 unsloth-2024.2 wheel-0.42.0 xformers-0.0.22.post7+cu118 xxhash-3.4.1 yarl-1.9.4\n", "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", "\u001B[0m\n", "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.0\u001B[0m\n", "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython -m pip install --upgrade pip\u001B[0m\n" ] } ], "source": [ "!pip install \"unsloth[cu118-ampere] @ git+https://github.com/unslothai/unsloth.git\"" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting protobuf\n", " Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes)\n", "Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl (294 kB)\n", "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m294.6/294.6 kB\u001B[0m \u001B[31m3.2 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0ma \u001B[36m0:00:01\u001B[0m\n", "\u001B[?25hInstalling collected packages: protobuf\n", "Successfully installed protobuf-4.25.3\n", "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", "\u001B[0m\n", "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.0\u001B[0m\n", "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython -m pip install --upgrade pip\u001B[0m\n" ] } ], "source": [ "!pip install protobuf" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "## For Colab, use this cell instead of the above 2 cells\n", "%%capture\n", "import torch\n", "major_version, minor_version = torch.cuda.get_device_capability()\n", "if major_version >= 8:\n", " # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", " !pip install \"unsloth[colab-ampere] @ git+https://github.com/unslothai/unsloth.git\"\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n", "pass" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "E8-BWi7MzkRz", "outputId": "3c2ac545-fdf6-41ab-8f25-f7c0b1a6b7df" }, "outputs": [], "source": [ "# One must patch the DPO Trainer first!\n", "from unsloth import PatchDPOTrainer\n", "PatchDPOTrainer()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# This done to save model download time in Runpod\n", "import os\n", "os.environ['TRANSFORMERS_CACHE']=\"/workspace/cache\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 383, "referenced_widgets": [ "15a973259d4b44fb927ab6f90558a67b", "e50bab4535214a33876e82fc723aba36", "e440224790d84f9a970e24e09677b358", "126b051df9a94212a3eb795da94202b9", "44275ae9daf54ae7912cf624e4072988", "43ec90294d174b55ac9125570e67390b", "5558e381bcf14c85800a9f6a60db245f", "c5b88871eb594318a242bd2cacac3ebf", "7bd55b67cbc74517ae7c8963734dd4ec", "0cc00b9da0844590a03178c433f80546", "e0d3a2ca3b624df39f49e5b68c624ffc", "0f5f3245e7e84b78bfd694c8598bbc0d", "7cf09a9e07f84a0b8e14ababea33397f", "0cfe63f655c2462abaf5f63b71c10999", "8af545abdca14e5ea7e8ea1f0cb912c3", "99dcfb9374464eb9a0eb70b3309ac135", "28c22816c978415c923f362faf4b337a", "99946db965b94adfa9985deabf7c0df3", "0e72bd2777174f1c9e4e875446078b93", "dcf840ac14664da68560209fbd70ee89", "b47efe2277ac4e0da48e9b2b413462af", "a9b982a7b601457388726a733c4f6ff4", "3d8f3fc569d44c51a99b3038518ca8e2", "70c1d7567da549209ab9ae2edf1f9281", "518e4e76da204ee9b788f5b55fe45481", "97543f8e3fbc47209fdfcb6277b70d69", "6994b71a1a8f4b69a8c4067ed724a614", "f61f781a6f1b4a64869dd9ec8ca535c6", "e576602d281a4fafabcb75f2b1982cc8", "95b08ac9a6f34ae9a751baae30a0d157", "ad5e7439882446318106548f16ae9ecc", "b11f2f32d6a7491ca66cb55cea8415f5", "2569b25a50e04c0ca75a412d70fe427e", "bf98441686744c69ba87d8971df488dc", "3cc5ee93be08486b8c0d3cdf539339bc", "c8c2bbfeab4543b8b56d9b5ea3fc6ef7", "14ba3e2104464f3b95382a9e19e0b33e", "9b8f527a2a99480db59633bf914e53da", "68d2acc803c34054bc364c59ac6e711d", "677f568c77c84fe8946c56370e1b9604", "7e189a2138a64260a583cb038110619f", "3fa4f343f18a42f1b4fb97e6a39a118d", "718b82c54f7142739742a203e86597a1", "0d05591bfe244c60bc04a9ab7a2efe3c", "336128f9ff7948d3aa47f4b3d260427e", "6ef14c55233f4decafa572788420fc63", "3ca46749427c40f88606776d5209be2a", "850e1e3ef2584991a437ab26de692683", "6473291994474e3383440ff2f7363c83", "2babdc60af7542ae888558824d87c18a", "8e8daeb77627417a95cb4b1e85c11404", "f2acf2cfcc5245a7b2e963156a3118e8", "b81da86fd9b34ffabe63328b47ce0e7c", "1365c26a079c401982eb8f018bdb0f72", "b70da8ff49ff48adb78981672e1547c9", "0b7e7d3ad8c04a27afb8a00ab9ae5558", "9a07d893edc44b17bfc0dba84dc5fadd", "d01b8dd3dd4442fbbb66607dbe28eeb5", "993f4c1df40444e39e9b1637e7df8fc5", "3186909fbe404aa79d97f9536819d099", "c638a317c20c41718512200c49e08a16", "117f7f951659466c83f71074a85d9e9e", "bb756c5fdb884b7b926ffc43b01da118", "20515c5e2e994baeaebd3ca51fbba943", "cc15bb8ed82e4bf4ac24a8e6428de56c", "700681f8ce7f49c4aa03290c47cd7257", "69ea74f7b6ea499f8acd2cd1b2f77177", "d8e65af631d6429b90d46686276a3a5a", "a869800400d2458189fe24c3695f4c66", "b4b0a29ded2544ebbe6aff3f4a364e7a", "6e7aa23ab2e24b7fbfa12620d1c6c80b", "7fe1735402964808b2d68ee8e5c80041", "392cbbfaea12496e84f5fdcf76194048", "6b2b437dadd54e8bb074caedd109030a", "44b88386034f437aa10faaf75cefa0ef", "d7466ce6b14b4271b49aa423eaa7daa1", "e2d106264b864342b6180909e5c71747" ] }, "id": "QmUBVEnvCDJv", "outputId": "be54f502-375b-41ba-82a9-8b1b20466e56" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==((====))== Unsloth: Fast Mistral patching release 2024.2\n", " \\\\ /| GPU: NVIDIA GeForce RTX 3090. Max memory: 23.691 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.1.0+cu118. CUDA = 8.6. CUDA Toolkit = 11.8.\n", "\\ / Bfloat16 = TRUE. Xformers = 0.0.22.post7+cu118. FA = True.\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ba3e645227204343b86d9c0f4fd36bb9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [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)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241, "referenced_widgets": [ "d901e138176b41ddbad5fdb5316c0863", "10d759b4b1bc4c2ea68dea8ab8647dda", "2fc7943f9dc8406c93f8bc4d99c7fa02", "8aa90a0e55c8435ba34857f357ae43c0", "a6485ae6ca3340a29c598b70155357bc", "e99ca0fa87ff41319dfad7fbd21980e2", "329f6c3e39184f7e86c4b7258bcb1394", "76037e249ebd406a951cab72e13deb6e", "41fc0c318b4b458f82aa8e6d22749416", "b471d282aaa143c4833cdf8aa6d79605", "df00d987b1ef432f86ecec36b9da1d6f", "310f49804ac54410b4d5b6544750ca0f", "c49b57f385514a80852a49b2063d58fa", "1bdf8bcb30964a69b032af040c64277c", "eff8110e11a34285bfb430c4d77fbfac", "4b3cd96bd26842b180d784f73c4474ad", "798a6888cd85403bb9f4499cb94bdf5e", "6e415e65b6954bc48126b10755d53305", "ed3d498b49354e9abaedfb5a0a058af1", "1ce9c65b2dc6487c988daad38d435a37", "c6a776d9fa6b49e49d0628adf583c6b2", "81e18304f8a34c24918ac144f9aae009", "73ec85a1f8e9404d9370aed85d10f271", "b52aaf3f11804913bc670c31630de6ec", "f4aafe0b85034f59a3c6059526c1000b", "c7811eeb616e431eb5e084bb9910d8f4", "5ca844fcfc294150b142a03eb43198f9", "cf216e9f1b034bbdbc1cf3fcf0e92db9", "b44c4067ecf04c3ab2a71b88dbe08b62", "bb282552158740e2b80c257a5a32b3c9", "cfbd166f0fbb48449e607cb4a2062c46", "3cab34d140594b08a13c29a6b6ae0823", "9103a72fd8c44301b6f25f0d3b557db6", "6b4fe0d31a0f4241a5434c24b87982a1", "979d707b6b744dd5afa9403c7644243f", "83b05de4ee6b410ab868a6d00c1c54f2", "957596e7359b4568b1ec1ecd0f62ed16", "3d1195a0c5fc402ca0caa60af2ebe9fb", "875d19302b51491e93971ff962167dce", "ba3362b426644df1adec99ed7e0409c0", "ba16b71a0aeb4967af22835e16af714a", "3bbd5a9c16e74d66a33a5d8af31f3aa5", "c0e556a7a16047b3b3b59b7e5f3ed844", "192ab4a14d21407d87e6456f6d67a039", "f4308a80acc0467a8f59580d6fff3d3f", "97ea18a097294cc79a149ab6d1452a97", "fe8555e19277403699add8894564047c", "187bca0dd81b47bdbb9ff3bbe0c7b4ec", "401a6f7d0b83489c8a7e10f1a6183446", "eeea816e2b8b4b7cafe7c8128cbcfaf2", "a380a4890c9149c38155903067b619ed", "693d23f5576248ba935a290dd0370f74", "393f612767e3432291e02e697f39baad", "1eea4a30bc8c4eac807e54c0807ad8e0", "c7e918b86d9440e5b5b86251b705748a", "926934ae2aad48328021e3a1eb1fa73e", "e40ab7e886014768bc0addcf518befd4", "d89eb869ceec4e2aa9d8404981a10f86", "1b29060d0a374dd2a88c5dad6887b0b7", "69ae5076109447be9b7f44901cbce61d", "bc53fa375def4423899a4fe8a309acda", "aff94722fd074c538d64541762b64bb6", "ab7b17b7063d4bac9863fdb3fa8e93d3", "41df1c965c5d4e43ab744b1b32746f01", "088e28c1b7ff4fe487dcfdaf75816854", "76897587840441f6bdd47da9c6628c97", "88285ee707f246f5b2d7863970a3833b", "35c6646626d148edbacaf54bc5626186", "c73b2d4224d340f6bfd1b2a227cfe558", "96aad1f018474e5583e28a3b8f95c54c", "64e2cc232eaf46eb803365c3c3557efd", "21de2cd6324344989dbe49043fc84f09", "f3713ee3330042eb90f7765addb8f141", "a5fe587e51324907bfdec469a84c6b55", "ea112eccf16643c2a7cb01f6046d0051", "2729037657a7456f852c397b07401666", "bfccd42c89f64e61a0ca095047393ad6" ] }, "id": "r6bUnxe6N3pf", "outputId": "689b8e8d-bbe0-4205-8b77-3331a93926e9" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ee502fb8d5304ba38586d15fe28e2f42", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading readme: 0%| | 0.00/606 [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": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oF63zQqNlNJC", "outputId": "74bb4c47-85f3-4b8d-aa1a-912a9c6b2df2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('<|im_start|>system\\n'\n", " 'Segment:<|im_end|>\\n'\n", " '<|im_start|>user\\n'\n", " '```yaml\\n'\n", " '\"input\":\\n'\n", " ' \"sentence\":\\n'\n", " ' \"segment\":\\n'\n", " ' - \"word\": \"Shere,\"\\n'\n", " ' - \"word\": \"in\"\\n'\n", " ' - \"word\": \"your\"\\n'\n", " ' - \"word\": \"report\"\\n'\n", " ' - \"word\": \"on\"\\n'\n", " ' - \"word\": \"female\"\\n'\n", " ' - \"word\": \"sexuality,\"\\n'\n", " ' - \"word\": \"men\"\\n'\n", " ' - \"word\": \"were\"\\n'\n", " ' - \"word\": \"staggered\"\\n'\n", " ' - \"word\": \"to\"\\n'\n", " ' - \"word\": \"learn\"\\n'\n", " ' - \"word\": \"that\"\\n'\n", " ' - \"word\": \"clitoral\"\\n'\n", " ' - \"word\": \"stimulation\"\\n'\n", " ' - \"word\": \"was\"\\n'\n", " ' - \"word\": \"much\"\\n'\n", " ' - \"word\": \"more\"\\n'\n", " ' - \"word\": \"important\"\\n'\n", " ' - \"word\": \"than\"\\n'\n", " ' - \"word\": \"penetration.\"\\n'\n", " '\\n'\n", " '```<|im_end|>\\n'\n", " '<|im_start|>assistant\\n')\n", "('<|im_start|>user\\n'\n", " '```yaml\\n'\n", " '\"input\":\\n'\n", " ' \"sentence\":\\n'\n", " ' \"segment\":\\n'\n", " ' - \"word\": \"Shere,\"\\n'\n", " ' - \"word\": \"in\"\\n'\n", " ' - \"word\": \"your\"\\n'\n", " ' - \"word\": \"report\"\\n'\n", " ' - \"word\": \"on\"\\n'\n", " ' - \"word\": \"female\"\\n'\n", " ' - \"word\": \"sexuality,\"\\n'\n", " ' - \"word\": \"men\"\\n'\n", " ' - \"word\": \"were\"\\n'\n", " ' - \"word\": \"staggered\"\\n'\n", " ' - \"word\": \"to\"\\n'\n", " ' - \"word\": \"learn\"\\n'\n", " ' - \"word\": \"that\"\\n'\n", " ' - \"word\": \"clitoral\"\\n'\n", " ' - \"word\": \"stimulation\"\\n'\n", " ' - \"word\": \"was\"\\n'\n", " ' - \"word\": \"much\"\\n'\n", " ' - \"word\": \"more\"\\n'\n", " ' - \"word\": \"important\"\\n'\n", " ' - \"word\": \"than\"\\n'\n", " ' - \"word\": \"penetration.\"\\n'\n", " '\\n'\n", " '```<|im_end|>\\n'\n", " '<|im_start|>assistant\\n'\n", " '```yaml\\n'\n", " '\"output\":\\n'\n", " '- \"sentence\":\\n'\n", " ' - \"segment\":\\n'\n", " ' - \"word\": \"Shere,\"\\n'\n", " ' - \"word\": \"in\"\\n'\n", " ' - \"word\": \"your\"\\n'\n", " ' - \"word\": \"report\"\\n'\n", " ' - \"word\": \"\\'on\\'\"\\n'\n", " ' - \"word\": \"female\"\\n'\n", " ' - \"word\": \"sexuality,\"\\n'\n", " ' - \"segment\":\\n'\n", " ' - \"word\": \"men\"\\n'\n", " ' - \"word\": \"were\"\\n'\n", " ' - \"word\": \"staggered\"\\n'\n", " ' - \"word\": \"to\"\\n'\n", " ' - \"word\": \"learn\"\\n'\n", " ' - \"word\": \"that\"\\n'\n", " ' - \"word\": \"clitoral\"\\n'\n", " ' - \"word\": \"stimulation\"\\n'\n", " ' - \"segment\":\\n'\n", " ' - \"word\": \"was\"\\n'\n", " ' - \"word\": \"much\"\\n'\n", " ' - \"word\": \"more\"\\n'\n", " ' - \"word\": \"important\"\\n'\n", " ' - \"word\": \"than\"\\n'\n", " ' - \"word\": \"penetration.\"\\n'\n", " '\\n'\n", " '```<|im_end|>\\n')\n", "('<|im_start|>user\\n'\n", " '```yaml\\n'\n", " '\"input\":\\n'\n", " ' \"sentence\":\\n'\n", " ' \"segment\":\\n'\n", " ' - \"word\": \"Shere,\"\\n'\n", " ' - \"word\": \"in\"\\n'\n", " ' - \"word\": \"your\"\\n'\n", " ' - \"word\": \"report\"\\n'\n", " ' - \"word\": \"on\"\\n'\n", " ' - \"word\": \"female\"\\n'\n", " ' - \"word\": \"sexuality,\"\\n'\n", " ' - \"word\": \"men\"\\n'\n", " ' - \"word\": \"were\"\\n'\n", " ' - \"word\": \"staggered\"\\n'\n", " ' - \"word\": \"to\"\\n'\n", " ' - \"word\": \"learn\"\\n'\n", " ' - \"word\": \"that\"\\n'\n", " ' - \"word\": \"clitoral\"\\n'\n", " ' - \"word\": \"stimulation\"\\n'\n", " ' - \"word\": \"was\"\\n'\n", " ' - \"word\": \"much\"\\n'\n", " ' - \"word\": \"more\"\\n'\n", " ' - \"word\": \"important\"\\n'\n", " ' - \"word\": \"than\"\\n'\n", " ' - \"word\": \"penetration.\"\\n'\n", " '\\n'\n", " '```<|im_end|>\\n'\n", " '<|im_start|>assistant\\n'\n", " '```yaml\\n'\n", " '\"input\":\\n'\n", " ' \"sentence\":\\n'\n", " ' \"segment\":\\n'\n", " ' - \"word\": \"Shere,\"\\n'\n", " ' - \"word\": \"in\"\\n'\n", " ' - \"word\": \"your\"\\n'\n", " ' - \"word\": \"report\"\\n'\n", " ' - \"word\": \"on\"\\n'\n", " ' - \"word\": \"female\"\\n'\n", " ' - \"word\": \"sexuality,\"\\n'\n", " ' - \"word\": \"men\"\\n'\n", " ' - \"word\": \"were\"\\n'\n", " ' - \"word\": \"staggered\"\\n'\n", " ' - \"word\": \"to\"\\n'\n", " ' - \"word\": \"learn\"\\n'\n", " ' - \"word\": \"that\"\\n'\n", " ' - \"word\": \"clitoral\"\\n'\n", " ' - \"word\": \"stimulation\"\\n'\n", " ' - \"word\": \"was\"\\n'\n", " ' - \"word\": \"much\"\\n'\n", " ' - \"word\": \"more\"\\n'\n", " ' - \"word\": \"important\"\\n'\n", " ' - \"word\": \"than\"\\n'\n", " ' - \"word\": \"penetration.\"\\n'\n", " '\\n'\n", " '```<|im_end|>\\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": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6bZsfBuZDeCL", "outputId": "2941d5d9-6de3-48a4-a039-3d5c6cf809ef" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Unsloth 2024.2 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 = 16, # 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 = 16,\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", "\n", "Parameters are borrowed from [here](https://freedium.cfd/https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac?gi=9eb916bb6d8d) but we use a smaller learning rate and warmup steps.\n", "\n", "It takes ~12 hours on a single RTX 3090 to run this config. Max VRAM is ~15GB." ] }, { "cell_type": "code", "execution_count": 8, "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": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 104, "referenced_widgets": [ "c86b2607a677411cabb088a67af2b4ef", "5b2e5edfc16f4098bf657eadc3822c35", "806eed72cad64e93b0dd7536877c87c8", "77a19ee389ad47a8b0170eaa8562b76e", "7a5eeef2a863406cb2404c5f9b5e469e", "4f3dd8d3c99940eb9d6f5b5531a1f51b", "2c879d1ca0dc4051a8675786994c3e98", "9f88fc44294b4c35ae32b799cabcc81b", "e1eb177b1d424c91b1eb673013203e20", "0b36054150d9427193289aecc7354b9f", "f6a35143024d4d2897056bc6a2b2cd82" ] }, "id": "QtoqUw80QDV0", "outputId": "cf014d38-3960-4dc7-a322-f10dc8fe397b" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/trl/trainer/dpo_trainer.py:328: 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" ] } ], "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 = 4, # change me to 1 for T4 to avoid OOM. 4 goes to 15GB VRAM.\n", " gradient_accumulation_steps = 8,\n", " #warmup_ratio = 0.1,\n", " #num_train_epochs = 3,\n", " warmup_steps = 50,\n", " max_steps = 200,\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": 515 }, "id": "EWGFqAo5Q2me", "outputId": "4a3e644b-020f-4c2a-d5b8-247ecfd7f8e4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", " \\\\ /| Num examples = 8,250 | Num Epochs = 1\n", "O^O/ \\_/ \\ Batch size per device = 4 | Gradient Accumulation steps = 8\n", "\\ / Total batch size = 32 | Total steps = 200\n", " \"-____-\" Number of trainable parameters = 41,943,040\n" ] }, { "data": { "text/html": [ "\n", "
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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining Lossrewards / chosenrewards / rejectedrewards / accuraciesrewards / marginslogps / rejectedlogps / chosenlogits / rejectedlogits / chosen
10.6925000.0099310.0082670.4375000.001664-65.225388-101.532379-1.887149-1.941751
20.6943000.0014750.0029850.468750-0.001510-61.391518-103.492035-1.946624-2.016978
30.6884000.0228940.0129000.5312500.009994-63.438393-102.275764-1.874705-1.928362
40.694400-0.005765-0.0042490.468750-0.001515-73.846512-113.181061-1.914331-1.969258
50.6872000.0268460.0144030.6562500.012443-66.318810-101.976959-1.877555-1.921344
60.700900-0.0040230.0109630.312500-0.014986-64.160797-103.950340-1.880763-1.940524
70.6902000.0119230.0055270.5000000.006397-60.049065-99.933624-1.923996-1.987151
80.687000-0.012407-0.0256440.5937500.013237-62.400936-105.044968-1.857906-1.922159
90.6835000.0262660.0063600.6875000.019907-63.879200-99.804367-1.876602-1.926666
100.6671000.050956-0.0026680.7812500.053624-61.041092-100.755806-1.896852-1.953525
110.6555000.1116060.0335840.7500000.078022-61.884716-102.103668-1.900634-1.962237
120.6438000.1490350.0465270.8750000.102507-64.979950-104.774536-1.891555-1.960934
130.6348000.1447010.0233370.9375000.121364-63.798531-103.742249-1.778855-1.833693
140.6215000.1829540.0316970.8750000.151258-71.333099-109.079803-1.901324-1.977052
150.6108000.2327550.0571790.9375000.175576-66.366852-101.796547-1.892416-1.948260
160.5725000.3416220.0792581.0000000.262364-61.240749-102.240089-1.840552-1.915109
170.5669000.3650340.0868160.9062500.278218-64.686630-99.750885-1.856691-1.899691
180.5438000.4585470.1207860.8750000.337762-68.870506-102.014709-1.921505-1.973164
190.5360000.4534140.0939660.8437500.359447-72.129517-104.179985-1.899905-1.952941
200.4685000.6896140.1605461.0000000.529067-60.327698-95.464592-1.875398-1.949434
210.4861000.5837760.0868891.0000000.496888-58.430485-94.424088-1.891454-1.949621
220.4193000.8486590.1634291.0000000.685230-59.735069-95.625793-1.883978-1.953738
230.3681001.0645260.2236430.9687500.840883-65.426880-99.163666-1.880917-1.953981
240.4295000.8574220.1916890.9375000.665733-63.500526-97.119232-1.882411-1.937384
250.3595001.0518840.1710191.0000000.880866-59.690598-93.825394-1.876372-1.940306
260.3694001.0305870.1634020.9375000.867185-66.781578-99.858658-1.902385-1.956554
270.4028000.8906810.1145080.9062500.776173-71.715210-100.529358-1.888062-1.939180
280.3732000.9535550.0705990.8750000.882955-70.100090-96.930428-1.893912-1.941720
290.2874001.2204230.0638301.0000001.156593-60.500408-92.004044-1.910934-1.980496
300.2720001.3100520.0623450.9375001.247708-79.165314-108.299950-1.822885-1.884399
310.2061001.520700-0.0054521.0000001.526153-63.071144-91.908241-1.785687-1.854471
320.2030001.592522-0.0319150.9687501.624437-65.479233-91.025398-1.922925-1.986865
330.2209001.533269-0.0992480.9062501.632518-74.588127-99.629623-1.902008-1.964205
340.2297001.466858-0.1819460.9062501.648804-76.335480-95.496765-1.834822-1.879345
350.2140001.487525-0.3070550.9062501.794579-75.319336-93.538475-1.918821-1.972670
360.0998001.994153-0.5134821.0000002.507635-66.908455-86.157974-1.878010-1.946506
370.0772002.184178-0.5747261.0000002.758904-68.408157-85.516212-1.835259-1.906821
380.0855002.106439-0.7527380.9687502.859177-71.303757-82.281708-1.944694-2.010727
390.0619002.199690-1.0228531.0000003.222543-71.377663-81.943062-1.852974-1.922352
400.0570002.325701-1.2986650.9687503.624366-77.034576-83.035263-1.916527-1.989293
410.0541002.345055-1.4456020.9687503.790657-79.867455-83.643631-1.856608-1.905395
420.0461002.493526-1.7731500.9687504.266676-87.937729-88.179108-1.886164-1.950880
430.0472002.312751-2.0503730.9687504.363124-87.161430-86.129944-1.855935-1.925613
440.0687002.223296-2.1496790.9375004.372974-86.375252-80.280624-1.891995-1.953149
450.0427002.247630-2.5335320.9687504.781161-90.727478-81.450562-1.793966-1.851126
460.0538002.464459-2.9542400.9375005.418699-96.708618-80.554291-1.842345-1.908538
470.0327002.468843-3.2296540.9687505.698498-95.002190-79.113609-1.858419-1.917609
480.1175002.093840-3.1245130.8437505.218353-101.185349-82.650948-1.802338-1.846435
490.0300002.447959-3.9344240.9687506.382382-104.642136-82.287971-1.859661-1.925090
500.0252002.349972-4.3160910.9687506.666064-106.073097-80.433182-1.842398-1.911368
510.0065002.351074-4.6875561.0000007.038630-107.932602-79.370636-1.878582-1.935707
520.0016002.463594-5.3622651.0000007.825859-116.103485-80.034363-1.795781-1.862844
530.0040002.131287-5.4191911.0000007.550478-115.462906-80.987839-1.850861-1.905600
540.0032002.197679-5.8050731.0000008.002752-118.687881-79.826187-1.925845-1.993290
550.0231002.277760-6.0060990.9687508.283859-127.820969-88.483589-1.847210-1.917872
560.0442002.045557-6.1691880.9375008.214745-124.659477-84.166031-1.855102-1.916062
570.0438002.398190-6.5921590.9375008.990349-139.529144-90.947296-1.846677-1.928034
580.0231001.774500-6.6945060.9687508.469006-131.312759-86.181992-1.925915-1.977345
590.0224001.838212-7.2602150.9687509.098426-141.422379-91.776863-1.849625-1.904955
600.0439001.845384-7.2664080.9375009.111793-141.614639-88.987274-1.840355-1.906916
610.0448001.478810-6.8192890.9375008.298099-132.690399-88.374329-1.919538-1.969639
620.0221001.899460-8.1405400.96875010.039999-145.743271-90.252388-1.857570-1.919511
630.0002002.026294-8.4386831.00000010.464976-147.020706-87.274490-1.827286-1.887730
640.0222001.510540-8.2732670.9687509.783808-145.323395-88.450317-1.925784-1.984003
650.0436001.535126-8.4929830.93750010.028109-150.402832-92.309814-1.912131-1.956778
660.0005001.214515-9.0121841.00000010.226698-150.540588-88.528961-1.890113-1.947372
670.0001001.796210-9.4291881.00000011.225397-156.939789-87.245361-1.984745-2.063085
680.0653001.064941-8.5601120.9062509.625053-155.757919-97.472366-1.907721-1.961506
690.0652000.993425-8.3752950.9062509.368720-150.670074-93.864662-1.852926-1.895516
700.0218001.755924-9.2642120.96875011.020137-163.759888-94.422531-1.884260-1.949026
710.0436001.407461-9.2704320.93750010.677893-156.794220-90.694122-1.962400-2.025506
720.0218001.515018-10.0590390.96875011.574058-164.633789-90.771729-1.889076-1.959179
730.0219000.972740-9.5850930.96875010.557834-161.972061-96.145828-1.844425-1.892524
740.0868000.914522-8.7927320.8750009.707254-165.594879-107.097473-1.890044-1.930513
750.0217001.005203-9.9883170.96875010.993520-167.207214-97.347313-1.853193-1.899400
760.0434001.073524-9.7502500.93750010.823773-162.604950-94.317307-1.930668-1.986013
770.0007001.120011-10.2011041.00000011.321115-162.052475-89.903503-1.890304-1.941276
780.0217001.237938-10.6567330.96875011.894670-172.641373-97.203491-1.823810-1.876656
790.0217001.378800-10.7454030.96875012.124205-175.867447-96.711655-1.866561-1.930430
800.0003000.815344-10.5595081.00000011.374853-165.476654-93.585762-1.911207-1.972837
810.0218001.333638-10.5986630.96875011.932303-170.322357-92.381172-1.820395-1.882859
820.0650000.988857-9.9344890.90625010.923346-177.054352-106.560127-1.867344-1.924058
830.0435001.027610-10.3918000.93750011.419410-175.208740-102.094643-1.809247-1.866203
840.0217000.683302-10.9836160.96875011.666917-173.502335-98.309151-1.950875-2.006760
850.0867000.578787-9.7077290.87500010.286515-179.424118-111.117203-1.860974-1.897847
860.0479000.936778-10.5276720.93750011.464449-180.852005-102.993217-1.848799-1.890695
870.0652000.425789-9.8223090.90625010.248099-170.416809-101.552681-1.931349-1.980755
880.0000001.175534-11.8757321.00000013.051266-180.803360-96.029663-1.903106-1.976819
890.0867001.005983-9.9171440.87500010.923127-169.909607-98.544983-1.883621-1.938490
900.0001000.709654-11.3570131.00000012.066666-173.706055-93.608917-1.915069-1.976762
910.0435000.477264-10.7365510.93750011.213816-181.218826-108.760612-1.802605-1.844357
920.0867000.583126-10.0448760.87500010.628002-167.350479-99.650597-1.886728-1.933193
930.0217000.945670-11.7752420.96875012.720912-184.069229-98.403450-1.906602-1.973187
940.0434000.612050-11.0596190.93750011.671669-187.280777-109.401169-1.826414-1.877087
950.0217000.675427-11.4506050.96875012.126032-179.981110-100.109367-1.898841-1.953507
960.0217000.708127-11.3508420.96875012.058968-177.232513-96.715340-1.847462-1.901900
970.0217000.780336-11.2840280.96875012.064363-177.068573-96.441353-1.877259-1.912176
980.0434000.778137-11.0537160.93750011.831852-184.003098-103.773109-1.870908-1.925162
990.0217001.028205-11.5539020.96875012.582107-184.400208-100.089447-1.841471-1.905019
1000.0217000.353516-11.4665620.96875011.820077-183.305206-108.120209-1.876938-1.923044
1010.0434000.740837-11.0657110.93750011.806547-176.933624-97.440826-1.891777-1.920361
1020.0479000.593949-10.9922060.93750011.586155-175.206146-99.258972-1.911206-1.973414
1030.0434000.795841-11.4491540.93750012.244997-180.477692-98.055847-1.866122-1.917832
1040.0433000.777340-11.3096530.93750012.086990-182.164825-101.963196-1.882121-1.934470
1050.0050000.510335-11.9005531.00000012.410889-180.442368-98.240570-1.899688-1.961613
1060.0650000.755192-10.9907640.90625011.745956-190.304184-109.865013-1.858015-1.916487
1070.0000001.065331-12.4212301.00000013.486561-187.107025-98.055679-1.826416-1.885132
1080.0003000.820586-12.1200821.00000012.940668-183.174011-97.344032-1.837826-1.892154
1090.0217000.767417-12.4619540.96875013.229372-187.908081-100.196686-1.864809-1.928480
1100.1083000.333127-10.2066290.84375010.539757-189.532654-120.679733-1.939532-1.981758
1110.0000000.608623-12.2304611.00000012.839085-184.431778-97.517464-1.851851-1.908893
1120.0433000.947884-11.7387100.93750012.686596-188.261505-103.694641-1.801392-1.858541
1130.0000000.559098-12.4414871.00000013.000585-185.031815-95.259071-1.903439-1.963459
1140.0434000.555237-11.7909240.93750012.346161-185.853561-101.292900-1.846780-1.895535
1150.0001000.125599-12.2354461.00000012.361044-182.614731-100.348648-1.934710-1.989699
1160.0217000.642555-12.5260940.96875013.168651-189.841354-103.052399-1.879100-1.914975
1170.0434000.494508-11.3642440.93750011.858752-177.777603-97.124687-1.902839-1.956126
1180.0867000.205861-10.7690800.87500010.974942-178.435059-101.801300-1.822839-1.848876
1190.0433000.934836-12.0075230.93750012.942357-193.854340-108.625793-1.908241-1.976539
1200.0433000.825328-11.7987960.93750012.624125-183.718018-98.565102-1.834815-1.896412
1210.0000000.570292-12.6854421.00000013.255734-188.874359-100.744064-1.840728-1.892937
1220.0220000.273457-12.4480650.96875012.721522-191.319000-103.234337-1.869669-1.924867
1230.0000000.739589-12.4443861.00000013.183975-185.396912-97.543709-1.947350-2.009423
1240.0433000.471773-11.5719460.93750012.043719-185.468109-104.019699-1.850385-1.897007
1250.0223000.531008-11.6333780.96875012.164387-186.837265-103.420105-1.890191-1.948269
1260.0650000.459482-11.5286920.90625011.988175-190.393616-108.979614-1.813566-1.865598
1270.0217000.423156-12.3735670.96875012.796722-190.750916-109.170578-1.897676-1.958830
1280.0650000.275080-11.6024320.90625011.877512-193.196503-111.813087-1.807271-1.854911
1290.0650000.678916-11.7139020.90625012.392818-185.736496-101.049683-1.784050-1.826826
1300.0220000.559590-12.3085110.96875012.868102-184.687592-98.314713-1.856018-1.913242
1310.0434000.097012-11.9260880.93750012.023101-188.560669-105.093018-1.851667-1.904052
1320.0223000.631916-12.2918910.96875012.923808-188.667206-97.602272-1.806342-1.856767
1330.0000000.044295-12.7327991.00000012.777093-187.832306-100.222107-1.891387-1.941179
1340.0000000.164928-12.6516481.00000012.816574-187.047821-98.899582-1.874540-1.929160
1350.0650000.464494-11.5820670.90625012.046560-189.952942-108.556725-1.867593-1.907906
1360.0217000.643924-12.1014310.96875012.745355-185.185471-98.936203-1.871088-1.915239
1370.0000000.903751-13.1321031.00000014.035853-194.401794-100.999130-1.854176-1.924202
1380.0217000.220121-12.0846570.96875012.304777-183.585602-103.130791-1.924400-1.970213
1390.0433000.702169-12.5286250.93750013.230795-191.237183-99.048164-1.875030-1.935427
1400.0217000.188731-12.2518180.96875012.440548-182.995895-100.707130-1.856969-1.898762
1410.0217000.410915-12.6020500.96875013.012966-191.560379-102.686310-1.854935-1.909470
1420.0217000.217397-12.4148850.96875012.632282-190.334549-103.550301-1.847357-1.893018
1430.0434000.188853-12.0077190.93750012.196571-189.096664-107.576408-1.886003-1.934517
1440.0002000.041079-12.7299251.00000012.771004-187.678024-102.643410-1.855476-1.908435
1450.0002000.173133-13.0498471.00000013.222980-190.878754-100.978210-1.942114-1.991033
1460.0217000.385140-12.8660810.96875013.251221-193.286530-102.303047-1.845515-1.901057
1470.0217000.698989-12.8335890.96875013.532578-195.435760-101.922058-1.862593-1.925970
1480.0217000.047423-12.8065020.96875012.853926-190.005981-102.509384-1.928422-1.979353
1490.0217000.114320-12.7145290.96875012.828848-192.607773-104.419044-1.884816-1.930242
1500.0217000.589908-13.1668110.96875013.756720-196.869293-101.493034-1.864991-1.928835
1510.0217000.591138-12.8350530.96875013.426191-197.652100-104.080139-1.834763-1.896774
1520.0217000.310052-13.0039900.96875013.314042-192.777863-103.133842-1.862719-1.927921
1530.0433000.520161-12.3099700.93750012.830130-187.454056-99.989044-1.925316-1.975242
1540.0217000.421074-12.6781780.96875013.099251-200.323975-108.994141-1.800578-1.845382
1550.0000000.322216-13.5347821.00000013.856998-197.107590-101.961754-1.834417-1.891570
1560.1084000.463126-11.1759410.84375011.639068-192.982330-113.173698-1.873569-1.923092
1570.0217000.320651-13.0124640.96875013.333115-193.609802-101.025909-1.886778-1.941732
1580.021700-0.053605-12.8098510.96875012.756245-190.638290-103.844734-1.896181-1.946830
1590.0433000.467275-12.6032890.93750013.070563-195.226746-106.574730-1.899510-1.969057
1600.0219000.472549-13.0253490.96875013.497898-201.843353-109.387970-1.904034-1.945622
1610.0000000.385234-13.3262501.00000013.711485-193.756165-97.907196-1.822776-1.882171
1620.0217000.529081-12.9846160.96875013.513697-192.312454-100.750130-1.870952-1.934668
1630.0217000.331054-12.9723660.96875013.303420-200.855682-108.632874-1.873858-1.921691
1640.0217000.493049-12.6844180.96875013.177465-187.966919-95.420746-1.864515-1.917675
1650.0217000.420934-12.5891320.96875013.010067-188.447311-97.481064-1.849078-1.897848
1660.0000000.676847-12.9279571.00000013.604802-192.431091-100.556358-1.914310-1.972500
1670.0217000.715845-12.7649270.96875013.480774-194.916611-99.511650-1.867445-1.924852

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