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OpenCALM-7B-LoRA-train.ipynb
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/masuidrive/57365dde1cf26a7f97dfc64ed51173eb/opencalm-7b-lora-train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "flWndcn3uhE_" | |
| }, | |
| "source": [ | |
| "# OpenCALMを🤗PEFTのLoRAでFine tuningするコード\n", | |
| "\n", | |
| "OpenCALMでテストしていますが、[GPT-NeoX](https://github.com/EleutherAI/gpt-neox)ベースのモデルであれば、このスクリプトでLoRAを作れると思います。\n", | |
| "\n", | |
| "学習環境にはメインメモリ 15GB、GPUメモリ 40GB必要なので、Google ColabではA100 ハイメモリを選んでください。\n", | |
| "\n", | |
| "これは1 ecpoch回してるので2時間ぐらいかかります。\n", | |
| "\n", | |
| "学習用のデータセットには[kunishou/databricks-dolly-15k-ja](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja)と[kunishou/hh-rlhf-49k-ja](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja)を使わせていただいています。\n", | |
| "\n", | |
| "詳しくは https://note.com/masuidrive の記事を参照してください。\n", | |
| "\n", | |
| "\n", | |
| "### 参考サイト:\n", | |
| "- https://note.com/masayuki_abe/n/n445401d7d198\n", | |
| "- https://zenn.dev/tyaahan/articles/a8d99900000002\n", | |
| "- https://github.com/huggingface/peft/\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "tHcDk1lvaNFG" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from datetime import datetime\n", | |
| "\n", | |
| "# 生成されたモデルをGoogle Driveに保存する\n", | |
| "SAVE_TO_GDRIVE = True\n", | |
| "\n", | |
| "# 生成されたモデルをHugging FaceにPushする\n", | |
| "# Trueにした場合は、実行前に下の「Huggingfaceにログイン」のTokenを入力してLoginを押してください。\n", | |
| "PUSH_TO_HF = False\n", | |
| "\n", | |
| "# 開発版のtransformers/prftを使う\n", | |
| "USE_TRANSFORMERS_DEV = False\n", | |
| "\n", | |
| "# ベースのモデルのhuggingfaceのrepo名\n", | |
| "BASE_MODEL_NAME = \"cyberagent/open-calm-7b\"\n", | |
| "\n", | |
| "LORA_R = 4\n", | |
| "LORA_ALPHA = 16\n", | |
| "LORA_DROPOUT = 0.05\n", | |
| "\n", | |
| "BATCH_SIZE = 32\n", | |
| "MICRO_BATCH_SIZE = 4\n", | |
| "EPOCHS = 1\n", | |
| "LEARNING_RATE = 3e-4\n", | |
| "\n", | |
| "WARMUP_STEPS = 200\n", | |
| "MAX_STEPS = -1\n", | |
| "EVAL_STEPS = 500\n", | |
| "\n", | |
| "# Fine tuningした後のモデル名 == ディレクトリ名\n", | |
| "from datetime import datetime\n", | |
| "print(MODEL_NAME)\n", | |
| "\n", | |
| "# モデルを保存するためのディレクトリ\n", | |
| "PEFT_MODEL_PATH = f\"/content/drive/MyDrive/Developments/{MODEL_NAME}\"\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "iP1rKpgJwW2e" | |
| }, | |
| "source": [ | |
| "# 環境構築" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Huggingfaceにログイン" | |
| ], | |
| "metadata": { | |
| "id": "ExACAmM_sEOe" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import huggingface_hub\n", | |
| "\n", | |
| "huggingface_hub.login()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 471, | |
| "referenced_widgets": [ | |
| "7fac42a3a564476dabe595d7de8b94d5", | |
| "66be33766f2b4d72a88d4b1503d0aea4", | |
| "a5ddc50de2ea4fc984b1525c82a83d76", | |
| "c9c6627da41b47929fe7508c408be2f8", | |
| "4b4988e84d5b49329a90d77c61d7148c", | |
| "6e091c8468384f268849ee2351f08ad9", | |
| "5bf291010f3c4e7c9ac6fb603a6f4fa5", | |
| "dacb9aec98d84fe4a5b68bbe19733cd8", | |
| "153f2191998a41e89d7fcabb534b0e9e", | |
| "a3888ceb3d4b4172b7d5f400e7bcbb37", | |
| "1818c32fe2614268a6dc51c316b8c7a3", | |
| "82724763ee0b43d8acd7eb44511b0804", | |
| "69dca32577f141ef82880a75a89bca6c", | |
| "fab2462e38a74d05809a9d94d7f46907", | |
| "1bdab35f422a4a31a50ac76ecee05728", | |
| "55b7d72a05124f6d99226f492e130773", | |
| "33bd4b226d414db9845e6b3af37ca6b5" | |
| ] | |
| }, | |
| "id": "ZURrocNMsD-Q", | |
| "outputId": "6ae95751-8f49-4dd4-a392-23b5037ab707" | |
| }, | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "7fac42a3a564476dabe595d7de8b94d5" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Google Driveをマウント" | |
| ], | |
| "metadata": { | |
| "id": "fnDlHN58s4sb" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "6cU9_T4cHRhv" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from google.colab import drive\n", | |
| "\n", | |
| "if SAVE_TO_GDRIVE:\n", | |
| " drive.mount('/content/drive')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 必要なライブラリをイントール" | |
| ], | |
| "metadata": { | |
| "id": "m48vhzcas-bW" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "id": "1tfuxZlPL1EE" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "!pip install -q -U bitsandbytes datasets loralib sacrebleu huggingface_hub " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## transformers/peftは開発版も利用可" | |
| ], | |
| "metadata": { | |
| "id": "03LsSv5xtBvV" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "u4knhaK0CK3l", | |
| "outputId": "18cc98b0-f7ea-4e1c-f9a8-b3bd8422f41b" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Use released transformers and peft\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "if USE_TRANSFORMERS_DEV:\n", | |
| " print(\"Use transformers@main and peft@main\")\n", | |
| " !pip install -q -U git+https://github.com/huggingface/transformers.git\n", | |
| " !pip install -q -U git+https://github.com/huggingface/peft.git\n", | |
| " !pip install -q -U git+https://github.com/huggingface/accelerate.gitelse:\n", | |
| " print(\"Use released transformers and peft\")\n", | |
| " !pip install -q -U transformers peft accelerate" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "XmbL4Jy1wLVO" | |
| }, | |
| "source": [ | |
| "# Base modelとTokenizerを読み込み" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 77, | |
| "referenced_widgets": [ | |
| "7f2540fc935c4fe28777facec0da7b52", | |
| "0ad588d09b294a38acf8a5426b1e9220", | |
| "8ba8236a66434d9ab107570d175be993", | |
| "4d68abebc8a4498b919401cbbed50353", | |
| "7bfd8d3a341246c2ae00884f26d4d971", | |
| "a7337602bb834e7bb06af4e0d8433fb9", | |
| "4d205cec54c84024b34cc64b589a76a3", | |
| "a0319124c4404e4a9b241403cd91e9f6", | |
| "6b5b52517c364d2aa6a217d9e012e660", | |
| "72a5dd4f94dd43088b1151e5d4432228", | |
| "5cf60b9df6644765963b49490558be80" | |
| ] | |
| }, | |
| "id": "P2h6xYCgL12q", | |
| "outputId": "35fea3ba-01b1-4951-aa30-8cf1dfed5efc" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "7f2540fc935c4fe28777facec0da7b52" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "import torch\n", | |
| "from transformers import AutoTokenizer, AutoModelForCausalLM\n", | |
| "\n", | |
| "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_NAME)\n", | |
| "model = AutoModelForCausalLM.from_pretrained(\n", | |
| " BASE_MODEL_NAME, \n", | |
| " torch_dtype=torch.float16,\n", | |
| " device_map='auto',\n", | |
| " offload_folder=\"./offload\"\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Fine tuningで使う項目をfp32にする" | |
| ], | |
| "metadata": { | |
| "id": "v2gnmCAGTlMz" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "70sETUBcMwPP", | |
| "outputId": "5a3ad000-fddf-47f8-be2b-e778aaf52cd2" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "GPTNeoXForCausalLM(\n", | |
| " (gpt_neox): GPTNeoXModel(\n", | |
| " (embed_in): Embedding(52224, 4096)\n", | |
| " (layers): ModuleList(\n", | |
| " (0-31): 32 x GPTNeoXLayer(\n", | |
| " (input_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", | |
| " (post_attention_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", | |
| " (attention): GPTNeoXAttention(\n", | |
| " (rotary_emb): RotaryEmbedding()\n", | |
| " (query_key_value): Linear(in_features=4096, out_features=12288, bias=True)\n", | |
| " (dense): Linear(in_features=4096, out_features=4096, bias=True)\n", | |
| " )\n", | |
| " (mlp): GPTNeoXMLP(\n", | |
| " (dense_h_to_4h): Linear(in_features=4096, out_features=16384, bias=True)\n", | |
| " (dense_4h_to_h): Linear(in_features=16384, out_features=4096, bias=True)\n", | |
| " (act): GELUActivation()\n", | |
| " )\n", | |
| " )\n", | |
| " )\n", | |
| " (final_layer_norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", | |
| " )\n", | |
| " (embed_out): Linear(in_features=4096, out_features=52224, bias=False)\n", | |
| ")\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from torch import nn\n", | |
| "\n", | |
| "for param in model.parameters():\n", | |
| " param.requires_grad = False\n", | |
| " if param.ndim == 1:\n", | |
| " param.data = param.data.to(torch.float32)\n", | |
| "\n", | |
| "model.gradient_checkpointing_enable()\n", | |
| "model.enable_input_require_grads()\n", | |
| "\n", | |
| "class CastOutputToFloat(nn.Sequential):\n", | |
| " def forward(self, x): return super().forward(x).to(torch.float32)\n", | |
| "\n", | |
| "print(model)\n", | |
| "\n", | |
| "# embed_outがないと言われる場合は、下記のモデル情報を見て最後の層の名前を指定してみてください。\n", | |
| "model.lm_head = CastOutputToFloat(model.embed_out)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "GGIYtMpIwpaB" | |
| }, | |
| "source": [ | |
| "# 教師データとテストデータの作成" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## データから学習用プロンプトを生成する" | |
| ], | |
| "metadata": { | |
| "id": "ioPYMJ9-UJEG" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "id": "-qcVT4UeVAq1" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "\"\"\"\n", | |
| "input/instruction/outputからpromptを作る\n", | |
| "\"\"\"\n", | |
| "def generate_prompt(instruction, input=None, response=None):\n", | |
| " def add_escape(text):\n", | |
| " return text.replace('### Response', '### Response')\n", | |
| "\n", | |
| " if input:\n", | |
| " prompt = f\"\"\"\n", | |
| "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", | |
| "\n", | |
| "### Instruction:\n", | |
| "{add_escape(instruction.strip())}\n", | |
| "\n", | |
| "### Input:\n", | |
| "{add_escape(input.strip())}\n", | |
| "\"\"\".strip()\n", | |
| " else:\n", | |
| " prompt = f\"\"\"\n", | |
| "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n", | |
| "\n", | |
| "### Instruction:\n", | |
| "{add_escape(instruction.strip())}\n", | |
| "\"\"\".strip()\n", | |
| "\n", | |
| " if response:\n", | |
| " prompt += f\"\\n\\n### Response:\\n{add_escape(response.strip())}<|endoftext|>\"\n", | |
| " else:\n", | |
| " prompt += f\"\\n\\n### Response:\\n\"\n", | |
| "\n", | |
| " return prompt\n", | |
| "\n", | |
| "def get_response(text):\n", | |
| " marker = f\"### Response:\\n\"\n", | |
| " pos = text.find(marker)\n", | |
| " if pos == -1: # marker not found\n", | |
| " return None\n", | |
| " return text[pos + len(marker):].strip()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## データセットを読み込む" | |
| ], | |
| "metadata": { | |
| "id": "r8e3VwqwUNFG" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 170, | |
| "referenced_widgets": [ | |
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| "78861559c0c04765b4baddb16d0600df", | |
| "c3279597204c477399b4353519e53d07", | |
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| "8ac973636dd54f12ae9cb6b5013bce52", | |
| "c89312f959984f55ae6f9e8559f6da0a", | |
| "85bbbf74a60c400892553ecc090c274b", | |
| "105dd8c37dd44b5c8f9f4add0895d981", | |
| "0370bb33e6c140e69521498490b7fe27", | |
| "5b38829ace324c2284ac41c13bc38c98", | |
| "13cf3b0635984a2383fb250ccd07e208", | |
| "ded431b1f9af4d7db9920df8bfce2cf6", | |
| "2ac9110bdfc642ef86ba945e450bfa8b", | |
| "058845ce93e8412394c90cf32989397c", | |
| "2058196ebccb438495e5f2d1fdca8d2b", | |
| "04471ee57f5f4cb7adc79bc96483451e", | |
| "72155f455f7d45f4a3c0bb7b5830bda1", | |
| "a525674ef3584963854ad5691ad4253a", | |
| "0de30989ac8c4ff6833219f5a20b036e", | |
| "262a1016481e44bb93a50478a989cf26", | |
| "9b96f6653415481fba74241144641abe", | |
| "9b83a516fb55494193d210684a33dfdb", | |
| "ed1621a2026d4a319c05959bff4da0c4", | |
| "33d94defb55740b59ae0f12663f4f15a", | |
| "a61d29a0a21340289eba0e1d5315e9c6", | |
| "cb7d30a0e4404ff7add664e811de2cb4", | |
| "69cb2e882d9b4d5ca909dfd6a43a62a8", | |
| "1055d3258dbd44c087a94a51ea14ee9a", | |
| "dc8104df6c2d46ffb4676d48c8e12077", | |
| "5a14aa0ff6614f0ba4ca609f393b346f", | |
| "fe669cef5fd14b48b10adb5063d90a1a", | |
| "0e8a5bab66de4e7cbb65015422025289", | |
| "1cbc73c4eb7f447fba8fc7dbd77f03ac", | |
| "477d311ed724480dad7e0612434b0061", | |
| "3e18cdffc6a44f0589f3b6ddc568d8a2", | |
| "fcd406cb18db40398455e1c7cc727b9a", | |
| "8f5e0c2ccda74f67aad1db82dd6be2d6" | |
| ] | |
| }, | |
| "id": "WToqCXZIiWx7", | |
| "outputId": "a2f69e32-72e4-4db9-df9b-d41b7119b4c8" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "WARNING:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/kunishou___json/kunishou--hh-rlhf-49k-ja-121ab1bf407afa1a/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4)\n" | |
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| "model_id": "a044d22e9f8049fd99ec855151645190" | |
| } | |
| }, | |
| "metadata": {} | |
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| { | |
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| "model_id": "84ec8229dca843df9c020f9e7fa8282b" | |
| } | |
| }, | |
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| "model_id": "306288592956455989ba587b2ee0b7a5" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "WARNING:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/kunishou___json/kunishou--databricks-dolly-15k-ja-e353786ca55017da/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4)\n" | |
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| "model_id": "dca6e7f23d354657a8eb07be1d11fe4e" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
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| "model_id": "d765d5f8be3d4a7eb91e9fa5a7afa6ec" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "train: 63493\n", | |
| "test: 946\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "version_minor": 0, | |
| "model_id": "04471ee57f5f4cb7adc79bc96483451e" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "69cb2e882d9b4d5ca909dfd6a43a62a8" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "from datasets import DatasetDict, load_dataset, concatenate_datasets\n", | |
| "\n", | |
| "\"\"\"\n", | |
| "input/instruction/outputからpromptを作ってtokenizeする\n", | |
| "\"\"\"\n", | |
| "def tokenize_qa_function(sample):\n", | |
| " context = sample.get('input', '').strip()\n", | |
| " instruction = sample.get('instruction', '').strip()\n", | |
| " output = sample.get('output', '').strip()\n", | |
| "\n", | |
| " prompt = generate_prompt(instruction, context, output)\n", | |
| " return tokenizer(prompt)\n", | |
| "\n", | |
| "\"\"\"\n", | |
| "nameのdatasetを読み込み、alpaca形式のプロンプトにしてtokenizeする\n", | |
| "trainはtest_sizeでtestとsplitする\n", | |
| "\"\"\"\n", | |
| "def process_qa_dataset(name, test_size):\n", | |
| " TOKENIZED_COLUMNS = ['input_ids', 'attention_mask']\n", | |
| " data = load_dataset(name)\n", | |
| " data = data['train'].train_test_split(test_size=test_size)\n", | |
| " remove_columns = [item for item in data['train'].column_names if item not in TOKENIZED_COLUMNS]\n", | |
| " data = data.map(tokenize_qa_function, remove_columns=remove_columns)\n", | |
| " return data\n", | |
| "\n", | |
| "\n", | |
| "# Q&Aのデータセットを読み込み\n", | |
| "datasets = []\n", | |
| "datasets.append(process_qa_dataset(\"kunishou/hh-rlhf-49k-ja\", 0.01))\n", | |
| "datasets.append(process_qa_dataset(\"kunishou/databricks-dolly-15k-ja\", 0.03))\n", | |
| "\n", | |
| "# 読み込んだdatasetを統合\n", | |
| "datasetDict = DatasetDict()\n", | |
| "for key in ['train', 'test']:\n", | |
| " datasetDict[key] = concatenate_datasets(list(map(lambda x: x[key], datasets)))\n", | |
| " print(f\"{key}: {len(datasetDict[key])}\")\n", | |
| "\n", | |
| "\n", | |
| "# トークンサイズを超えるものは削除\n", | |
| "datasetDict = datasetDict.filter(lambda x: len(x['input_ids']) < model.config.max_position_embeddings)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "8PiZ5Jx0_j7B" | |
| }, | |
| "source": [ | |
| "# Fine tuning実行" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## PEFTのLoRAを設定\n", | |
| "\n", | |
| "使うモデルによって、`target_modules`を設定する必要があります。\n", | |
| "\n", | |
| "GPT-NeoXでは`[\"query_key_value\"]`となり、llamaでは`[\"q_proj\", \"v_proj\"]`となるようです。\n", | |
| "\n", | |
| "r, alpha, dropoutがハイパーパラメータです。" | |
| ], | |
| "metadata": { | |
| "id": "9Apa_jUqk_z-" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "id": "1GjeQtlaCVzN", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "13bbbd03-7ebd-44d4-f9a1-24ddbd73e05a" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "===================================BUG REPORT===================================\n", | |
| "Welcome to bitsandbytes. For bug reports, please run\n", | |
| "\n", | |
| "python -m bitsandbytes\n", | |
| "\n", | |
| " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n", | |
| "================================================================================\n", | |
| "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n", | |
| "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n", | |
| "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n", | |
| "CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n", | |
| "CUDA SETUP: Detected CUDA version 118\n", | |
| "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n", | |
| " warn(msg)\n", | |
| "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n", | |
| " warn(msg)\n", | |
| "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//172.28.0.1'), PosixPath('8013'), PosixPath('http')}\n", | |
| " warn(msg)\n", | |
| "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-a100-s-36i4aor5ktipn --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n", | |
| " warn(msg)\n", | |
| "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n", | |
| " warn(msg)\n", | |
| "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n", | |
| " warn(msg)\n", | |
| "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n", | |
| "Either way, this might cause trouble in the future:\n", | |
| "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n", | |
| " warn(msg)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "trainable params: 2097152 || all params: 6874079232 || trainable%: 0.030508115039428164\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from transformers import TrainingArguments\n", | |
| "from peft import get_peft_model, LoraConfig, TaskType\n", | |
| "\n", | |
| "peft_config = LoraConfig(\n", | |
| " task_type=TaskType.CAUSAL_LM,\n", | |
| " inference_mode=False,\n", | |
| " bias=\"none\",\n", | |
| " fan_in_fan_out=False,\n", | |
| " target_modules=[\"query_key_value\"],\n", | |
| " r=LORA_R,\n", | |
| " lora_alpha=LORA_ALPHA,\n", | |
| " lora_dropout=LORA_DROPOUT\n", | |
| ")\n", | |
| "\n", | |
| "model = get_peft_model(model, peft_config)\n", | |
| "model.print_trainable_parameters()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### [未使用] BLEUによりメトリクス\n", | |
| "\n", | |
| "生成された文章の類似度をBLEUで計算する" | |
| ], | |
| "metadata": { | |
| "id": "6vIG8wQTkeEb" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "id": "fmHNZgi-bl7h" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from transformers import Trainer, TrainingArguments\n", | |
| "from sacrebleu import corpus_bleu\n", | |
| "\n", | |
| "def compute_metrics(eval_pred):\n", | |
| " predictions, labels = eval_pred\n", | |
| " decoded_preds = [tokenizer.decode(pred, skip_special_tokens=True) for pred in predictions]\n", | |
| " decoded_labels = [tokenizer.decode(label, skip_special_tokens=True) for label in labels]\n", | |
| " bleu_score = corpus_bleu(decoded_preds, [decoded_labels]).score\n", | |
| " print(bleu_score)\n", | |
| " return {'bleu_score': bleu_score}\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 学習実行" | |
| ], | |
| "metadata": { | |
| "id": "4NMEMAlVkzh4" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "id": "JRueK_oXM8F1", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 242 | |
| }, | |
| "outputId": "28ea3702-1d1f-4d7f-f0ac-9471f5bdc8b2" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:407: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", | |
| " warnings.warn(\n", | |
| "You're using a GPTNeoXTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div>\n", | |
| " \n", | |
| " <progress value='1982' max='1982' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", | |
| " [1982/1982 2:01:21, Epoch 1/1]\n", | |
| " </div>\n", | |
| " <table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>Step</th>\n", | |
| " <th>Training Loss</th>\n", | |
| " <th>Validation Loss</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>500</td>\n", | |
| " <td>2.086600</td>\n", | |
| " <td>2.096590</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1000</td>\n", | |
| " <td>2.019400</td>\n", | |
| " <td>2.067588</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1500</td>\n", | |
| " <td>2.028700</td>\n", | |
| " <td>2.052838</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table><p>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "import transformers\n", | |
| "from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling\n", | |
| "\n", | |
| "# 学習の設定\n", | |
| "trainer = Trainer(\n", | |
| " model=model, \n", | |
| " train_dataset=datasetDict['train'],\n", | |
| " eval_dataset=datasetDict['test'],\n", | |
| " args=TrainingArguments(\n", | |
| " per_device_train_batch_size=MICRO_BATCH_SIZE, \n", | |
| " gradient_accumulation_steps=BATCH_SIZE // MICRO_BATCH_SIZE,\n", | |
| " warmup_steps=WARMUP_STEPS,\n", | |
| " max_steps=MAX_STEPS,\n", | |
| " learning_rate=LEARNING_RATE, \n", | |
| " fp16=True,\n", | |
| " num_train_epochs=EPOCHS,\n", | |
| " save_strategy='epoch',\n", | |
| " output_dir=\"result\",\n", | |
| " evaluation_strategy='steps',\n", | |
| " eval_steps=EVAL_STEPS,\n", | |
| " logging_dir='./logs',\n", | |
| " logging_steps=100,\n", | |
| " ),\n", | |
| " data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),\n", | |
| " # compute_metrics=compute_metrics\n", | |
| ")\n", | |
| "\n", | |
| "# 学習開始\n", | |
| "model.config.use_cache = False\n", | |
| "trainer.train()\n", | |
| "model.config.use_cache = True\n", | |
| "\n", | |
| "# 推論モード\n", | |
| "model.eval()\n", | |
| "\n", | |
| "# Google Driveに保存\n", | |
| "if SAVE_TO_GDRIVE:\n", | |
| " !mkdir -p \"{PEFT_MODEL_PATH}\"\n", | |
| " model.save_pretrained(PEFT_MODEL_PATH)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "7mZzbRuSb0Hc" | |
| }, | |
| "source": [ | |
| "# テスト実行" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": { | |
| "id": "ocqIvRnZNBmT", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "ba2eb954-30cd-4daf-fc92-bf319d103747" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "----\n", | |
| "トヨタ自動車は何年設立ですか？\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "1948年に設立され、現在は世界最大の自動車製造会社であり、世界で最も有名なブランドの一つです。\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "1938年\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "----\n", | |
| "マイナンバーカードの受け取りは免許書を持って行けばいいですか？\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "運転免許証があれば、マイナンバーカードを受け取ることができます。\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "運転免許証、パスポート、顔写真付きの本人確認書類は1点、その他の2点は2点必要です。\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "----\n", | |
| "情報セキュリティ対策に関わる責任者と担当者の役割や権限が明確になっていますか？具体的には、個人情報保護責任者、個人情報保護担当者は任命されていますか？\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "はい。情報セキュリティ管理の責任者として任命された人がいます。\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "はい。\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "----\n", | |
| "情報セキュリティ対策に関わる責任者と担当者の役割や権限が明確になっていますか？具体的には、個人情報保護責任者、個人情報保護担当者は任命されていますか？\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "はい。情報セキュリティ管理の責任者として任命された人がいます。\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "いいえ。\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "----\n", | |
| "地球温暖化とはなんですか？\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "地球温暖化は、大気中の温室効果ガスの濃度が上昇することを指します。温室効果ガスは、太陽からの熱を宇宙に逃がすのを妨げるため、地球の気温が上昇します。\n", | |
| "\n", | |
| "気候変動は、地球の温度を上昇させることです。\n", | |
| "\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "def qa(instruction, context=None):\n", | |
| " prompt = generate_prompt(instruction, context)\n", | |
| "\n", | |
| " batch = tokenizer(prompt, return_tensors='pt')\n", | |
| " with torch.cuda.amp.autocast():\n", | |
| " output_tokens = model.generate(\n", | |
| " **batch,\n", | |
| " max_new_tokens=256,\n", | |
| " temperature = 0.7,\n", | |
| " repetition_penalty=1.05\n", | |
| " )\n", | |
| "\n", | |
| " text = tokenizer.decode(output_tokens[0],pad_token_id=tokenizer.pad_token_id,\n", | |
| "skip_special_tokens=True)\n", | |
| " return get_response(text)\n", | |
| "\n", | |
| "instruction = \"トヨタ自動車は何年設立ですか？\"\n", | |
| "context = \"豊田自動織機製作所自動車部時代は、社名中の「豊田」の読みが「トヨダ」であったため、ロゴや刻印も英語は「TOYODA」であった。エンブレムは漢字の「豊田」を使用していた。しかし、1936年夏に行われた新トヨダマークの公募で、約27,000点の応募作品から選ばれたのは「トヨダ」ではなく「トヨタ」のマークだった。理由として、デザイン的にスマートであること、画数が8画で縁起がいいこと、個人名から離れ社会的存在へと発展することなどが挙げられている[11]。1936年9月25日に「トヨタ（TOYOTA）」の使用が開始され、翌年の自動車部門独立時も「トヨタ自動車工業株式会社」が社名に採用された。\"\n", | |
| "print(\"----\")\n", | |
| "print(instruction)\n", | |
| "print(qa(instruction))\n", | |
| "print(\"\")\n", | |
| "print(qa(instruction, context))\n", | |
| "print(\"\\n\")\n", | |
| "\n", | |
| "instruction = \"マイナンバーカードの受け取りは免許書を持って行けばいいですか？\"\n", | |
| "context = \"マイナンバーカードの受け取りに必要な書類は以下のとおりです。\\n交付通知書\\n本人確認書類（有効期間内のもの）\\n顔写真付きの本人確認書類は1点\\nその他は2点（例：健康保険証＋年金手帳）\\nお持ちの方のみ\\n通知カード\\n住民基本台帳カード\\nマイナンバーカード\\n詳しくは必要な持ち物をご確認ください。\"\n", | |
| "print(\"\\n----\")\n", | |
| "print(instruction)\n", | |
| "print(qa(instruction))\n", | |
| "print(\"\")\n", | |
| "print(qa(instruction, context))\n", | |
| "print(\"\\n\")\n", | |
| "\n", | |
| "\n", | |
| "instruction = \"情報セキュリティ対策に関わる責任者と担当者の役割や権限が明確になっていますか？具体的には、個人情報保護責任者、個人情報保護担当者は任命されていますか？\"\n", | |
| "context = \"個人情報保護管理者: 山形太郎\\n責任と権限\\n・JISQ15001に適合したPMSを構築、運用する\\n・PMSの運用状況や成果を、トップマネジメントに報告する\\n----\\n個人情報保護監査責任者\\n宮崎健吾\\n責任と権限\\n・内部監査計画書の作成\\n・内部監査員の選定\\n・内部監査の指揮 ・内部監査報告書の作成及びトップマネジメントへの報告\\n\"\n", | |
| "print(\"\\n----\")\n", | |
| "print(instruction)\n", | |
| "print(qa(instruction))\n", | |
| "print(\"\")\n", | |
| "print(qa(instruction, context))\n", | |
| "print(\"\\n\")\n", | |
| "\n", | |
| "\n", | |
| "instruction = \"情報セキュリティ対策に関わる責任者と担当者の役割や権限が明確になっていますか？具体的には、個人情報保護責任者、個人情報保護担当者は任命されていますか？\"\n", | |
| "context = \"個人情報保護管理者: 任命なし\\n責任と権限\\n・JISQ15001に適合したPMSを構築、運用する\\n・PMSの運用状況や成果を、トップマネジメントに報告する\\n----\\n個人情報保護監査責任者\\n宮崎健吾\\n責任と権限\\n・内部監査計画書の作成\\n・内部監査員の選定\\n・内部監査の指揮 ・内部監査報告書の作成及びトップマネジメントへの報告\\n\"\n", | |
| "print(\"\\n----\")\n", | |
| "print(instruction)\n", | |
| "print(qa(instruction))\n", | |
| "print(\"\")\n", | |
| "print(qa(instruction, context))\n", | |
| "print(\"\\n\")\n", | |
| "\n", | |
| "instruction = \"地球温暖化とはなんですか？\"\n", | |
| "context = \"\"\"リモートワークでリモートワークができた\n", | |
| "毎日でも食べたいということは毎日でも食べているというわけではない\n", | |
| "今のままではいけないと思っています。だからこそ日本は今のままではいけないと思っている\n", | |
| "約束は守るためにありますから、約束を守るために全力を尽くします\"\"\"\n", | |
| "print(\"\\n----\")\n", | |
| "print(instruction)\n", | |
| "print(qa(instruction))\n", | |
| "print(\"\")\n", | |
| "print(qa(instruction, context))\n", | |
| "print(\"\\n\")\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Huggingfaceにアップロード\n", | |
| "上で`PUSH_TO_HF`をTrueにしておいた場合、生成されたモデルをHuggingFaceにて公開する" | |
| ], | |
| "metadata": { | |
| "id": "GFvtG14TilBi" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## リポジトリを作る" | |
| ], | |
| "metadata": { | |
| "id": "uUOsYEHvrsn-" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "if PUSH_TO_HF:\n", | |
| " hub = huggingface_hub.HfApi()\n", | |
| " hub.create_repo(repo_id=MODEL_NAME, exist_ok=True)" | |
| ], | |
| "metadata": { | |
| "id": "0iCUbRaBrsNJ" | |
| }, | |
| "execution_count": 18, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## モデルをアップする\n", | |
| "tokenizerは親モデルのを使うのでpushする必要なし" | |
| ], | |
| "metadata": { | |
| "id": "pabbyNDdr3Gz" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "if PUSH_TO_HF:\n", | |
| " model.push_to_hub(MODEL_NAME)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 137, | |
| "referenced_widgets": [ | |
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| }, | |
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| "text/plain": [ | |
| "Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]" | |
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