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| "cell_type": "markdown", | |
| "source": [ | |
| "# 無料で使えるLLMでもChatGPTっぽい会話がしたい！\n", | |
| "## はじめに\n", | |
| "\n", | |
| "現在，様々なLLM（Large Language Model; 大規模言語モデル）が[Hugging Face](https://huggingface.co/)で公開されています．今回はこのような公開されたLLMを使って，ChatGPTのような対話アプリケーションを作成します．しかしながら，LLMは学習時に利用した文書の情報しか持っていないので，自然な文章を出力することはできても，内容自体は適当なことが多々ます... \n", | |
| "そこでここではいくつかの選考事例を参考に，前述の課題を幾らか解消することができる技術であるRAGを試します．\n", | |
| "\n", | |
| "### LLMって何？\n", | |
| "> 大規模言語モデル（Large Language Models、LLM）とは、非常に巨大なデータセットとディープラーニング技術を用いて構築された言語モデルです。ここでいう「大規模」とは、従来の自然言語モデルと比べ、後述する3つの要素「計算量」「データ量」「パラメータ数」を大幅に増やして構築されていることに由来します。大規模言語モデルは、人間に近い流暢な会話が可能であり、自然言語を用いたさまざまな処理を高精度で行えることから、世界中で注目を集めています。 \n", | |
| "引用元： [大規模言語モデル | 用語解説 | 野村総合研究所(NRI)](https://www.nri.com/jp/knowledge/glossary/lst/ta/llm)\n", | |
| "\n", | |
| "Deep Learningと呼ばれる層の深いニューラルネットワークの研究の中で，transformerというニューラルネットワークのアーキテクチャが生まれました．これはネットワークの大きさや訓練に使うデータの量に従って性能が伸びることがわかっている現在最も注目されている技術です（ホントか？）．これ使い，インターネットから収集できる大量のテキストデータを使って訓練された言語モデルのことを現在はLLMと呼んでいるようです．\n", | |
| "\n", | |
| "### 言語モデルって何？\n", | |
| "> 「言語モデル」とは、文章の並び方に確率を割り当てる確率モデルです。例えば、ある画像からそれが猫かどうかを当てる「予測モデル」を考えると、猫に近い画像は猫であるという確率を高く割り当て、犬に近い画像は猫であるという確率を低く割り当てます。同様に、「言語モデル」の場合、より自然な文章の並びに対して高い確率を割り当て、文章として成立しない並びには低い確率を割り当てます。 \n", | |
| "引用： [大規模言語モデル | 用語解説 | 野村総合研究所(NRI)](https://www.nri.com/jp/knowledge/glossary/lst/ta/llm)\n", | |
| "\n", | |
| "基本的には，文書を生成するための機械学習モデルです．ある単語をニューラルネットワークに入力すると，このニューラルネットワークはその次に来るであろう自然な単語を予測します．\n", | |
| "\n", | |
| "\n", | |
| "### 待て待て... そもそも __RAGって何？__ :\n", | |
| "\n", | |
| "- 問題１：LLMは学習コーパスに入っている内容しか答えられないよ！じゃあどうする？\n", | |
| "- 回答1:質問する時に，回答の参考にして欲しい資料を一緒に渡せばいいんだ．\n", | |
| "- 問題２：でもそうすると読むのに時間がかかりそう...\n", | |
| "- 回答2：渡す文書群の中の一つ一つの文書をベクトル化しておいて，質問と類似した内容の文書をベクトル検索させよう．近い順に上位n個の文書を元に回答させよう．\n", | |
| "\n", | |
| "> Retrieval-augmented Generation（RAG、検索により強化した文章生成）は、LLMが持つ知識の内部表現を補うために外部の知識ソースにモデルを接地させる（グラウンドさせる）ことで、LLMが生成する回答の質を向上するAIのフレームワークです。 \n", | |
| "引用： [Retrieval-Augmented Generation(RAG)とは？ | IBM](https://www.ibm.com/blogs/solutions/jp-ja/retrieval-augmented-generation-rag/)\n", | |
| "\n", | |
| "> RAGモデルは、情報の検索とテキスト生成の2つの要素を組み合わせたものです。まずユーザーが質問を入力すると、検索コンポーネントが事前にインデックスされた大規模なデータベースや情報源から、関連情報を含むデータを検索します。〜 検索結果は統合され、その情報を利用してT5やBARTなどのTransformerベースの生成コンポーネントが回答を生成します。検索コンポーネントと事前にトレーニングされた生成コンポーネントにより、ユーザーの質問に詳細かつ高い精度で回答できます。 \n", | |
| "引用： [AIにおけるRAGとは？仕組みやメリット、デメリット、その他の単語との違いを解説 | WEEL](https://weel.co.jp/media/rag)" | |
| ], | |
| "metadata": { | |
| "id": "9zsXWu9tmTcu" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 環境構築" | |
| ], | |
| "metadata": { | |
| "id": "q1MNwOUnqsim" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### パッケージのinstall" | |
| ], | |
| "metadata": { | |
| "id": "ETW152gTHsx6" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# To solve for an error encountered: `NotImplementedError: A UTF-8 locale is required. Got ANSI_X3.4-1968`\n", | |
| "import locale\n", | |
| "locale.getpreferredencoding = lambda: \"UTF-8\"" | |
| ], | |
| "metadata": { | |
| "id": "5GVnFdSWrTx4" | |
| }, | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "このノートブックではcolab T4での実行を想定しています．[T4](https://www.ask-corp.jp/products/nvidia/nvidia-tesla/nvidia-tesla-t4.html)はVRAMが16GBしかないので，一般的にパラメータ数が少ない部類の7Bモデル（BはBillionのBです．billion = 10億なので7Bモデルはパラメータ数が70億）でもfloat32bitで読み込むと $70 \\text{[億個]} \\times 4 \\text{[byte]}=280\\text{[億byte]}$なので28GB必要になり（1KBを1024Bとするなら26GBくらい），どちらにせよT4ではメモリ不足になってしまいます．\n", | |
| "\n", | |
| "そこで今回は，あらかじめ量子化されたパラメータを利用することで対応します．そのために必要そうなパッケージをinstallしましょう．" | |
| ], | |
| "metadata": { | |
| "id": "HWEPUfKFX38s" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# 必要なライブラリをインストール\n", | |
| "%pip install cython\n", | |
| "%pip install llmx\n", | |
| "%pip install transformers langchain accelerate bitsandbytes pypdf tiktoken sentence_transformers faiss-gpu trafilatura auto-gptq --quiet" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Ph1X37I5mbu6", | |
| "outputId": "fd4befe2-0ead-467f-eeb8-d0b1d54e4e37" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Requirement already satisfied: cython in /usr/local/lib/python3.10/dist-packages (3.0.6)\n", | |
| "Requirement already satisfied: llmx in /usr/local/lib/python3.10/dist-packages (0.0.15a0)\n", | |
| "Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from llmx) (1.10.13)\n", | |
| "Collecting openai (from llmx)\n", | |
| " Downloading openai-1.6.0-py3-none-any.whl (225 kB)\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.4/225.4 kB\u001b[0m \u001b[31m1.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25hCollecting tiktoken (from llmx)\n", | |
| " Downloading tiktoken-0.5.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB)\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25hRequirement already satisfied: diskcache in /usr/local/lib/python3.10/dist-packages (from llmx) (5.6.3)\n", | |
| "Collecting cohere (from llmx)\n", | |
| " Downloading cohere-4.39-py3-none-any.whl (51 kB)\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.7/51.7 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25hRequirement already satisfied: google.auth in /usr/local/lib/python3.10/dist-packages (from llmx) (2.17.3)\n", | |
| "Requirement already satisfied: typer in /usr/local/lib/python3.10/dist-packages (from llmx) (0.9.0)\n", | |
| "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from llmx) (6.0.1)\n", | |
| "Requirement already satisfied: aiohttp<4.0,>=3.0 in /usr/local/lib/python3.10/dist-packages (from cohere->llmx) (3.9.1)\n", | |
| "Collecting backoff<3.0,>=2.0 (from cohere->llmx)\n", | |
| " Downloading backoff-2.2.1-py3-none-any.whl (15 kB)\n", | |
| "Collecting fastavro<2.0,>=1.8 (from cohere->llmx)\n", | |
| " Downloading fastavro-1.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n", | |
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| "Installing collected packages: typing-extensions, importlib_metadata, h11, fastavro, backoff, tiktoken, httpcore, httpx, cohere, openai\n", | |
| " Attempting uninstall: typing-extensions\n", | |
| " Found existing installation: typing_extensions 4.5.0\n", | |
| " Uninstalling typing_extensions-4.5.0:\n", | |
| " Successfully uninstalled typing_extensions-4.5.0\n", | |
| " Attempting uninstall: importlib_metadata\n", | |
| " Found existing installation: importlib-metadata 7.0.0\n", | |
| " Uninstalling importlib-metadata-7.0.0:\n", | |
| " Successfully uninstalled importlib-metadata-7.0.0\n", | |
| "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", | |
| "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n", | |
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| "\u001b[?25h Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### パッケージのimport\n", | |
| "\n", | |
| "必要なパッケージをimportします．~~もしもここでエラーが発生した場合は，runtimeを再起動してください．~~ \n", | |
| "importする前（下のセルを実行する前）に，ここで必ずruntimeを再起動してください．しないとtorchが以下のようなエラーを吐きます．\n", | |
| "\n", | |
| "```python\n", | |
| "---------------------------------------------------------------------------\n", | |
| "TypeError Traceback (most recent call last)\n", | |
| "<ipython-input-11-20d355ad6c79> in <cell line: 2>()\n", | |
| " 1 from IPython.display import Markdown\n", | |
| "----> 2 import torch\n", | |
| " 3 print(torch.__version__)\n", | |
| " 4 from transformers import AutoTokenizer, pipeline\n", | |
| " 5 from auto_gptq import AutoGPTQForCausalLM\n", | |
| "\n", | |
| "12 frames\n", | |
| "/usr/lib/python3.10/_pyio.py in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through)\n", | |
| " 2043 encoding = \"utf-8\"\n", | |
| " 2044 else:\n", | |
| "-> 2045 encoding = locale.getpreferredencoding(False)\n", | |
| " 2046\n", | |
| " 2047 if not isinstance(encoding, str):\n", | |
| "\n", | |
| "TypeError: <lambda>() takes 0 positional arguments but 1 was given\n", | |
| "```\n", | |
| "\n", | |
| "runtime再起動の手順： 「ランタイム」⇨「セッションを再開する」" | |
| ], | |
| "metadata": { | |
| "id": "D-Ul8kbomTZ0" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from IPython.display import Markdown\n", | |
| "import torch\n", | |
| "print(torch.__version__)\n", | |
| "from transformers import AutoTokenizer, pipeline\n", | |
| "from auto_gptq import AutoGPTQForCausalLM\n", | |
| "from langchain.document_loaders import TextLoader\n", | |
| "from langchain.text_splitter import CharacterTextSplitter\n", | |
| "from langchain.vectorstores import FAISS\n", | |
| "from langchain.chains import RetrievalQA\n", | |
| "from langchain.embeddings import HuggingFaceEmbeddings\n", | |
| "from langchain.llms.huggingface_pipeline import HuggingFacePipeline\n", | |
| "from langchain import PromptTemplate\n", | |
| "from trafilatura import fetch_url, extract" | |
| ], | |
| "metadata": { | |
| "id": "pflcmpCGmsPK", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "8023a108-0d60-4a61-e370-324a86889750" | |
| }, | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "2.1.0+cu121\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!ldconfig /usr/lib64-nvidia" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "nbt_9tPUmJiZ", | |
| "outputId": "79b429f9-3e2c-4b10-8af6-40acd8fef9a9" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", | |
| "\n", | |
| "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n", | |
| "\n", | |
| "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", | |
| "\n", | |
| "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", | |
| "\n", | |
| "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", | |
| "\n", | |
| "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "出力のストリーミング対応のためのあれこれ... （結局失敗した）" | |
| ], | |
| "metadata": { | |
| "id": "tpWf_yODnZIp" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from transformers import TextIteratorStreamer\n", | |
| "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler" | |
| ], | |
| "metadata": { | |
| "id": "K6jCIp05nZvB" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "transformersが出す警告を非表示にする:" | |
| ], | |
| "metadata": { | |
| "id": "rYlIZuuBrAn1" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ/discussions/15#6498748e5e95532769f64b83\n", | |
| "from transformers import logging\n", | |
| "logging.set_verbosity(logging.CRITICAL)" | |
| ], | |
| "metadata": { | |
| "id": "g-8ICmygo3E4" | |
| }, | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 4bit量子化LLMの準備" | |
| ], | |
| "metadata": { | |
| "id": "v_qnYiFgmTXL" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### youri-7b-intruction-qptqの読み込み" | |
| ], | |
| "metadata": { | |
| "id": "HeN8F1b9Hd45" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "いろんなLLMが公開されていますが，日本語で使いやすいものとしてはELYZA社が公開してくださっている[ELYZA-japanese-Llama-2-7b](https://zenn.dev/elyza/articles/2fd451c944649d)が有名です．Web上の多くの日本語記事ではこれが使われていますし，私もこれをよく使うのですが，今回は毛色を変えてrinna社が公開してくださっている[youri-7b-instruction-gptq](https://rinna.co.jp/news/2023/10/20231031.html) を利用します．\n", | |
| "\n", | |
| "https://huggingface.co/TheBloke/Llama-2-70B-Chat-GPTQ/discussions/3 を参考に，以下のようにしてLLMの量子化済みパラメータを読み込んでクラスを初期化します．\n", | |
| "\n", | |
| "```{warning}\n", | |
| "パラメータをダウンロードするのに時間がかかるので，コーヒーでも飲んで待っていてください．\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "ErNtF3MCrK2t" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "id": "SjUnCm3ZmQkB", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 241, | |
| "referenced_widgets": [ | |
| "29df39cce617410f9dfa8e50ba662094", | |
| "dc3dbf6a13ba4c0aa8fcb8c2a328d812", | |
| "d48dd51da06643ab8b7964146b72fe0e", | |
| "7463e8951ab143f79c838eee634bb637", | |
| "3d1cb5ebd2eb446496d25c2dacc403b2", | |
| "19a3fe876111495b81beddec04ec3e68", | |
| "f40fb18c95b944b2a4971288fd20ab14", | |
| "c580aa74f788485b980787b569080682", | |
| "438fc307b8154efaacdf9424c4712b59", | |
| "32f31d4a66ce4d5890fd207f942d36a7", | |
| "d25462e9c0714fa6b29d4db0a5a3a0e4", | |
| "ff6fcba760634733b717bf3957593cb0", | |
| "6d4f2f271766465883668847669252a7", | |
| "d1b559dcdd0443bbaee67df46d6dd4be", | |
| "3a878640ced84f14b43e6d874cb634ef", | |
| "6a02b5f7867a44b6a8d8fa87a20d9ea9", | |
| "d1c95f80161e4d68aa06c38682adf1c3", | |
| "bcfe1223adcc4f25ac1f85fdb34b0d0d", | |
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| "1c3e95980c1a4ce08a5a3d9d7dcaa4be", | |
| "beca4a43b08e4c649b0b1c153ce45b47", | |
| "f2d0b85462434300b7c105b959b47874", | |
| "e67d68f1da6f432e80e44a8f392f3e28", | |
| "98a835ccdb9141a59ac9dd83355de21d", | |
| "68d92d1e18fd4f6c942311ed31de9ddf", | |
| "04dcda848e4b4e50ad325e44c93bdcb9", | |
| "798758400cb44412a5b9ce62fb3d7207", | |
| "b56adde9743b4b1d83b8310660d6c871", | |
| "073b996a9fcb4baba50dcaf925ef4bb7", | |
| "20c85b03b8414d3c8274f327186b8cc6", | |
| "55c970dcbc534eb399e57e1841b6e6c3", | |
| "685a466fefc34b30baf1e330e8625283", | |
| "8ead38e17ec94a89b7271bf53e7557df", | |
| "ee95a489b4c945908f7c0ebd732f2d6f", | |
| "64b9dab9486142c1b452ba6efff81de1", | |
| "fdc20faffb154c0a9635233761b229f7", | |
| "6cb44c07589c41c687d93f38fba27d43", | |
| "b6e1075cd0284eec9a449595a9945954", | |
| "2e2de9984c5b4e8d967c48ae97370d82", | |
| "3cd2bd8ed0bd4c8b8e8c9e4b9a1710e7", | |
| "5aad76b64cee4bc4bd3ce1b1c81d6185", | |
| "c0fd5d58b30346c597309d19804ac938", | |
| "2090a006d3e6407a9262be6eaab70193", | |
| "ce51b221faf14711984360a6dd0aa734", | |
| "acd48a4353b64f3fa4bbe3947470b3de", | |
| "62f2172c26ee4ee89ee55db0c7a81858", | |
| "095566c33bbe4dcb95a4236194b0dc6b", | |
| "bbdbc4858d0240a0953b42c90142de92", | |
| "ff1a4f48adf3405cb4ac60d0c51a2f49", | |
| "c9e77789d662489c95242b81f803c79a", | |
| "9d1a030a9d0e491cba893f6d6d797f97", | |
| "f0a11aa7d26447df8b4a3635649c7a07", | |
| "8940a27399a642b3ad444efd4474740a", | |
| "aa31c31cb8114d78845c17d4bfe4e8c7", | |
| "73fafe74d99e41c09da3f9bafe44d10f", | |
| "3bf0498874354ccc8a82db48f1a5dbc3", | |
| "99de7c5b60fc4f50aeb0a80f2cd1e5d3", | |
| "03c85df1bfa44d3a90c2274a50185d29", | |
| "b218d73891b74cdcb4931d9415754221", | |
| "04076e6ef691408095986969e5321d52", | |
| "3c6041a692434abe98ea15d67e9feef0", | |
| "3aa83301ee7e43858ffc55ec31732993", | |
| "8c7ec7f085cc446dbd4ad582e9c075e1", | |
| "31935f9ddaf24d7280bc931d8c11ce4c", | |
| "a89488cab271442c8e050f714839c35c", | |
| "8bb115199c94459d8b58cf17ca0e2db0", | |
| "91cb272007ff4a42b082cc2e34656611", | |
| "e012962e303f441fb680e330a53cb68c", | |
| "676f30ddda4847fda58cb31f83f7f0f4", | |
| "241f3308b33e41bc95505455f69d84ce", | |
| "2eab198fa96f4324aacaffd914edd949", | |
| "b719a412711b4d02875ca48c63afae43", | |
| "d91ab8449ea44749aa4ac4743a28cbbe", | |
| "589776d9c9794755b1fa81651abe8dd4", | |
| "502aa09b33ad440daa9c5f438cdddccf", | |
| "c6ce54ee2e8f4afcb5ad46334436edea", | |
| "b0daace26daf4368bffe69fdeae54ccf" | |
| ] | |
| }, | |
| "outputId": "1cf8ee89-29b2-48d8-ec22-02e8a1dd88c7" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "tokenizer_config.json: 0%| | 0.00/866 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "29df39cce617410f9dfa8e50ba662094" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "tokenizer.model: 0%| | 0.00/500k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "ff6fcba760634733b717bf3957593cb0" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "tokenizer.json: 0%| | 0.00/1.84M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "e67d68f1da6f432e80e44a8f392f3e28" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "ee95a489b4c945908f7c0ebd732f2d6f" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "config.json: 0%| | 0.00/630 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "acd48a4353b64f3fa4bbe3947470b3de" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "3bf0498874354ccc8a82db48f1a5dbc3" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "gptq_model-4bit-128g.safetensors: 0%| | 0.00/3.90G [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "91cb272007ff4a42b082cc2e34656611" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "tokenizer = AutoTokenizer.from_pretrained(\"rinna/youri-7b-chat-gptq\") # tokenizerを読み込み\n", | |
| "# https://huggingface.co/TheBloke/Llama-2-70B-Chat-GPTQ/discussions/3\n", | |
| "model = AutoGPTQForCausalLM.from_quantized(\n", | |
| " \"rinna/youri-7b-chat-gptq\",\n", | |
| " use_safetensors=True,\n", | |
| " trust_remote_code=True,\n", | |
| " inject_fused_attention=False,\n", | |
| " device=\"cuda:0\",\n", | |
| " use_triton=True,\n", | |
| " quantize_config=None,\n", | |
| " callbacks=[StreamingStdOutCallbackHandler()]\n", | |
| " ) # 量子化済みモデルを読み込み" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### youriのデモンストレーション" | |
| ], | |
| "metadata": { | |
| "id": "nn1aVJzdHmKF" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "ここで一度，[youriのhuggingfaceページ](https://huggingface.co/rinna/youri-7b-instruction-gptq)を真似てデモを動作してみます．" | |
| ], | |
| "metadata": { | |
| "id": "n1lIHQ03gNtR" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "%time\n", | |
| "instruction = \"次の日本語を英語に翻訳してください。\"\n", | |
| "input = \"大規模言語モデル（だいきぼげんごモデル、英: large language model、LLM）は、多数のパラメータ（数千万から数十億）を持つ人工ニューラルネットワークで構成されるコンピュータ言語モデルで、膨大なラベルなしテキストを使用して自己教師あり学習または半教師あり学習によって訓練が行われる。\"\n", | |
| "prompt = f\"\"\"\n", | |
| "以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさWい。\n", | |
| "\n", | |
| "### 指示:\n", | |
| "{instruction}\n", | |
| "\n", | |
| "### 入力:\n", | |
| "{input}\n", | |
| "\n", | |
| "### 応答:\n", | |
| "\"\"\"\n", | |
| "token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors=\"pt\")\n", | |
| "\n", | |
| "with torch.no_grad():\n", | |
| " output_ids = model.generate(\n", | |
| " input_ids=token_ids.to(model.device),\n", | |
| " max_new_tokens=200,\n", | |
| " do_sample=True,\n", | |
| " temperature=0.5,\n", | |
| " pad_token_id=tokenizer.pad_token_id,\n", | |
| " bos_token_id=tokenizer.bos_token_id,\n", | |
| " eos_token_id=tokenizer.eos_token_id\n", | |
| " )\n", | |
| "\n", | |
| "output = tokenizer.decode(output_ids.tolist()[0])\n", | |
| "print(output)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "sRUXye2ugKSj", | |
| "outputId": "ecd4dc52-745f-4527-ffce-74f0fd2ac54d" | |
| }, | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs\n", | |
| "Wall time: 4.77 µs\n", | |
| "\n", | |
| "以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさWい。\n", | |
| "\n", | |
| "### 指示:\n", | |
| "次の日本語を英語に翻訳してください。\n", | |
| "\n", | |
| "### 入力:\n", | |
| "大規模言語モデル（だいきぼげんごモデル、英: large language model、LLM）は、多数のパラメータ（数千万から数十億）を持つ人工ニューラルネットワークで構成されるコンピュータ言語モデルで、膨大なラベルなしテキストを使用して自己教師あり学習または半教師あり学習によって訓練が行われる。\n", | |
| "\n", | |
| "### 応答:\n", | |
| " The large language model (LLM) is a computer language model consisting of a large artificial neural network with millions or billions of parameters that is trained by self-supervised learning or semi-supervised learning using vast unlabeled text.</s>\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "%time\n", | |
| "instruction = \"次の質問に答えてください．\"\n", | |
| "input = \"LLMとはなんですか？どんな点がこれまでの自然言語処理と違いますか？\"\n", | |
| "prompt = f\"\"\"\n", | |
| "以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさWい。\n", | |
| "\n", | |
| "### 指示:\n", | |
| "{instruction}\n", | |
| "\n", | |
| "### 入力:\n", | |
| "{input}\n", | |
| "\n", | |
| "### 応答:\n", | |
| "\"\"\"\n", | |
| "token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors=\"pt\")\n", | |
| "\n", | |
| "with torch.no_grad():\n", | |
| " output_ids = model.generate(\n", | |
| " input_ids=token_ids.to(model.device),\n", | |
| " max_new_tokens=200,\n", | |
| " do_sample=True,\n", | |
| " temperature=0.5,\n", | |
| " pad_token_id=tokenizer.pad_token_id,\n", | |
| " bos_token_id=tokenizer.bos_token_id,\n", | |
| " eos_token_id=tokenizer.eos_token_id\n", | |
| " )\n", | |
| "\n", | |
| "output = tokenizer.decode(output_ids.tolist()[0])\n", | |
| "print(output)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "oCcnvlTYgyZM", | |
| "outputId": "b93be9dc-6584-4cc5-a367-5b583cf4b45d" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs\n", | |
| "Wall time: 7.39 µs\n", | |
| "\n", | |
| "以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさWい。\n", | |
| "\n", | |
| "### 指示:\n", | |
| "次の質問に答えてください．\n", | |
| "\n", | |
| "### 入力:\n", | |
| "LLMとはなんですか？どんな点がこれまでの自然言語処理と違いますか？\n", | |
| "\n", | |
| "### 応答:\n", | |
| " LLMとは、Large Language Modelの略で、過去に学習したテキストから、新しいテキストを生成するために使用されます。LLMは、従来の自然言語処理（NLP）システムとは異なり、単にテキストを解析して、そのテキストの文脈を理解するために使用するのではなく、単にテキストを生成します。\n", | |
| "\n", | |
| "LLMは、異なる言語に対しても同様に機能し、さまざまな言語に対応したLLMが存在\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "torchを介してGPUを使っているので， `%time`では適切に実行時間を計測できないことに注意が必要です．" | |
| ], | |
| "metadata": { | |
| "id": "AlfcBhjmhQCq" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## RAGの準備" | |
| ], | |
| "metadata": { | |
| "id": "HAE_4IKmn6Tu" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### webからコーパスを収集する" | |
| ], | |
| "metadata": { | |
| "id": "dn3gsJmuHRXe" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "```{margin}\n", | |
| "最近，呪術廻戦のアニメを見てハマりました．なので今回はネタとしてこれを使います．\n", | |
| "```\n", | |
| "ここでは呪術廻戦のwikipediaページから文書を取り出します．もちろん，例えばニコニコ大百科からネットミーム記事を取り出すことも可能ですし，2ch（今は5ch？）からテキストデータを持ってくることも可能でしょう．\n", | |
| "\n", | |
| "好きなものに変えて試してみてください．" | |
| ], | |
| "metadata": { | |
| "id": "DsECFntoAco0" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "url = \"https://ja.wikipedia.org/wiki/%E5%91%AA%E8%A1%93%E5%BB%BB%E6%88%A6\"\n", | |
| "filename = 'textfile.txt'\n", | |
| "\n", | |
| "document = fetch_url(url)\n", | |
| "text = extract(document)" | |
| ], | |
| "metadata": { | |
| "id": "RierPbHYnve-" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "どんなテキストを取り出したのか，ちょっと確認してみましょう．" | |
| ], | |
| "metadata": { | |
| "id": "VHYKLixuc_h-" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(text[1000:2000])\n", | |
| "\n", | |
| "with open(filename, 'w', encoding='utf-8') as f:\n", | |
| " f.write(text)" | |
| ], | |
| "metadata": { | |
| "id": "DYlrvZxScya8", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "1771efbd-4aa0-44b7-9b39-913cd9f0a8b4" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "行当日、夏油は里香を手に入れるため、高専を襲撃する。安全のため高専に残っていた乙骨は激戦の末に夏油を倒し、同時に里香の解呪に成功し、物語は幕を閉じる。\n", | |
| "呪術廻戦\n", | |
| "始まり（1巻1話 - 5話）\n", | |
| "常人離れした身体能力を持つ高校生・虎杖悠仁は、両親の顔を知らず、祖父に育てられた。祖父が逝去した夜、虎杖の学校に眠る「呪物」の封印が解かれ、人を襲う化物・呪霊が現れてしまう。虎杖は「呪物」回収のために現れた呪術師の伏黒恵と共に、取り残された先輩を救うため校舎へ乗り込む。しかし、そこで窮地に追い込まれてしまい、虎杖は力を得るため自ら呪物「宿儺の指」を食べ、特級呪物・両面宿儺が復活する[5]。\n", | |
| "その後虎杖は「宿儺の器」として呪術師に捕らえられ、死刑を宣告される。しかし五条の提案により、「すべての宿儺の指を食してから死ぬ」という猶予が与えられる。\n", | |
| "かくして虎杖は都立呪術高専に入学し、呪術師としての人生をスタートさせる。\n", | |
| "呪胎戴天（じゅたいたいてん）編（1巻6話 - 2巻9話）\n", | |
| "6話「呪胎戴天」から9話「呪胎戴天―肆―」まで\n", | |
| "2018年7月、西東京市の少年院に特級呪霊の呪胎が出現し、虎杖、伏黒、そして釘崎野薔薇が人命救出のため派遣される。\n", | |
| "3人は少年院の内部に入り、完全変態を遂げた特級呪霊と対峙する。虎杖は伏黒と釘崎を脱出させ、肉体の主導権を宿儺に渡して特級呪霊を祓わせる。しかし、今度は宿儺が暴れ始め、虎杖は抵抗した末に宿儺と共に命を落とす。\n", | |
| "虎杖死亡後（2巻10話 - 3巻18話）\n", | |
| "少年院での事件後、死亡した虎杖は宿儺と契約を結んだことで蘇生し、9月に京都校と行われる「交流会」に向けて、五条のもとで特訓を始める。伏黒と釘崎も、二年生の狗巻棘、禪院真希、パンダのもとで特訓していた。\n", | |
| "一方では、かつて「百鬼夜行」を起こした夏油が、人間の殲滅を目論む特級呪霊の漏瑚らと手を組み暗躍していた。夏油達は五条悟封印の計画を立て、実行日の10月31日に向けて、その準備に入る。\n", | |
| "幼魚と逆罰（ようぎょとさかばち）編（3巻19話 - 4巻31話）\n", | |
| "2018年9月、神奈川県川崎市で変死体が発見され、虎杖と1級呪術師の七海建人が派遣される。\n", | |
| "調査の結果、事件の犯人である特級呪霊・真人と、現場で事件の一部始終を見ていた高校生・吉野順平の存在が明らかとなる。順平は自身を虐めた者への復讐の為に真人に接触していたが、\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "抽出したテキストをテキストファイルに保存出来ました。ここままだとwikipedia記事が一つだけなので，RAGでベクトル検索がどうこうという話になりません．なので[LangChainのTextSplitterを試す | npaka](https://note.com/npaka/n/nda9dc5eae1df) を参考に，このテキストファイルに対してLangchainのTextSplitterを使って，ファイルをいくつかに分割します．\n", | |
| "\n", | |
| "(分割したファイルをchunkと呼んでいるみたいです．)" | |
| ], | |
| "metadata": { | |
| "id": "kiiivuXPoDQA" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "loader = TextLoader(filename, encoding='utf-8')\n", | |
| "documents = loader.load()\n", | |
| "\n", | |
| "text_splitter = CharacterTextSplitter.from_tiktoken_encoder(\n", | |
| " separator = \"\\n\",\n", | |
| " chunk_size=300,\n", | |
| " chunk_overlap=10,\n", | |
| ")\n", | |
| "texts = text_splitter.split_documents(documents)\n", | |
| "print(len(texts))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "oj5b2HUUoAIq", | |
| "outputId": "274525c8-c6f7-41c2-a4dc-6098731732ba" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "WARNING:langchain.text_splitter:Created a chunk of size 354, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 322, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 448, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 430, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 724, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 356, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 580, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 330, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 674, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 454, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 383, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 311, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 410, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 476, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 358, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 407, which is longer than the specified 300\n", | |
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| "WARNING:langchain.text_splitter:Created a chunk of size 328, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 442, which is longer than the specified 300\n", | |
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| "WARNING:langchain.text_splitter:Created a chunk of size 381, which is longer than the specified 300\n", | |
| "WARNING:langchain.text_splitter:Created a chunk of size 649, which is longer than the specified 300\n", | |
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| "WARNING:langchain.text_splitter:Created a chunk of size 410, which is longer than the specified 300\n", | |
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| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "385\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "分割したchunkを見てみましょう．試しに100番目のchunkを表示します．" | |
| ], | |
| "metadata": { | |
| "id": "ieDdr-xhelCC" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "texts[100]" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Hk59ZklgoFI3", | |
| "outputId": "88706d5c-1cab-49f6-dce9-6719a557f1d3" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Document(page_content='- 渋谷事変終結後に帰国。上層部から虎杖の処刑執行人に任命され、虎杖の前に姿を現し、心臓を止めると同時に反転術式で治癒した。その後、虎杖達と別ルートで死滅回遊に参加する。仙台結界にて烏鷲亨子（うろ たかこ）、石流龍（いしごおり りゅう）、黒沐死（くろうろし）を倒し得点を獲得。\\n- 禪院 真希（ぜんいん まき）\\n- 声 - 小松未可子[6][22]', metadata={'source': 'textfile.txt'})" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 11 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### 文書埋め込みベクトルの作成とFAISSを使ったベクトルDBの用意\n", | |
| "\n", | |
| "chunkを文書埋め込みベクトル（embedding）に変換します．このembeddingに変換する作業には，別の言語モデルを利用します．色々なモデルがあるようなのですが，ここではembeddingの生成に `intfloat/multilingual-e5-large` を使います。（これが一番よく使われているようだったので）\n", | |
| "\n", | |
| "例えばchunkが100個できたならば，埋め込みベクトルも100個できるはずです．これと質問との類似度を計算して，近い順に上位n個を利用しようという話です．\n", | |
| "\n", | |
| "例えばベクトル同士の類似度を計るには，ここでどんな指標で類似度を測っているのは知りませんが，cosine類似度を利用するのが一般的ですよね．\n", | |
| "\n", | |
| "ベクトルDBには `FAISS` のライブラリを用います．" | |
| ], | |
| "metadata": { | |
| "id": "ViO12xJvoLOW" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "embeddings = HuggingFaceEmbeddings(model_name=\"intfloat/multilingual-e5-large\")\n", | |
| "db = FAISS.from_documents(texts, embeddings)\n", | |
| "\n", | |
| "# kは類似チャンクを何個読み込むかを指定するパラメータです．\n", | |
| "retriever = db.as_retriever(search_kwargs={\"k\": 5})" | |
| ], | |
| "metadata": { | |
| "id": "o0NV5V5XoJR8", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 625, | |
| "referenced_widgets": [ | |
| "6b53b50c529b41b18f904b31a8cbb1af", | |
| "8bd58a2bd86d4652a5bd673451721310", | |
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| "5c0a220539db4c38878b0c675526697a", | |
| "72f941c723654ea68de44b2f23fbd865", | |
| "0002040b59f2438b8569b667c624ba96", | |
| "9d53978c0fe9418599a533a0e3c95775", | |
| "f2fdb860ad0a42d4a7fd2f56213741f7", | |
| "6adec63b2aea4b72849879987e90b84b", | |
| "a2c215a3a1f748caa307896381dba68a", | |
| "ab9a12b239f04cb29ffa652b71627875", | |
| "c549108546d54c369932457b2d0e7f90", | |
| "271c6c1ada7b4ea3b724ee05623de547", | |
| "b59b78722600444dbd145779b39ae413", | |
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| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "yuuri用にプロンプトテンプレートを用意します．" | |
| ], | |
| "metadata": { | |
| "id": "QY6TDO3SoVRZ" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "B_INST, E_INST = \"[INST]\", \"[/INST]\"\n", | |
| "B_SYS, E_SYS = \"<<SYS>>\\n\", \"\\n<</SYS>>\\n\\n\"\n", | |
| "DEFAULT_SYSTEM_PROMPT = \"参考情報を元に、ユーザーからの質問にできるだけ正確に答えてください。\"\n", | |
| "text = \"{context}\\n質問: {question}\"\n", | |
| "template = \"{bos_token}{b_inst} {system}{prompt} {e_inst} \".format(\n", | |
| " bos_token=tokenizer.bos_token,\n", | |
| " b_inst=B_INST,\n", | |
| " system=f\"{B_SYS}{DEFAULT_SYSTEM_PROMPT}{E_SYS}\",\n", | |
| " prompt=text,\n", | |
| " e_inst=E_INST,\n", | |
| ")\n", | |
| "print(template)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "wMILFM8ooM6w", | |
| "outputId": "6f5f7bbe-065a-4772-8df5-b856a2c88e69" | |
| }, | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<s>[INST] <<SYS>>\n", | |
| "参考情報を元に、ユーザーからの質問にできるだけ正確に答えてください。\n", | |
| "<</SYS>>\n", | |
| "\n", | |
| "{context}\n", | |
| "質問: {question} [/INST] \n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## LLMとChainの指定" | |
| ], | |
| "metadata": { | |
| "id": "HJNXXDZuoa58" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "[ここ](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fnote.com%2Falexweberk%2Fn%2Fn3cffc010e9e9) のパクリです．" | |
| ], | |
| "metadata": { | |
| "id": "zJc-iyEcPlNN" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "pipe = pipeline(\n", | |
| " \"text-generation\",\n", | |
| " model=model,\n", | |
| " tokenizer=tokenizer,\n", | |
| " max_new_tokens=512,\n", | |
| " #streamer=TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # added\n", | |
| ")\n", | |
| "\n", | |
| "PROMPT = PromptTemplate(\n", | |
| " template=template,\n", | |
| " input_variables=[\"context\", \"question\"],\n", | |
| " template_format=\"f-string\"\n", | |
| ")\n", | |
| "\n", | |
| "chain_type_kwargs = {\"prompt\": PROMPT}\n", | |
| "\n", | |
| "qa = RetrievalQA.from_chain_type(\n", | |
| " llm=HuggingFacePipeline(\n", | |
| " pipeline=pipe,\n", | |
| " model_kwargs=dict(\n", | |
| " temperature=0.0,\n", | |
| " do_sample=True,\n", | |
| " max_length=512,\n", | |
| " repetition_penalty=1.1\n", | |
| "\n", | |
| " )\n", | |
| " ),\n", | |
| " retriever=retriever,\n", | |
| " chain_type=\"stuff\",\n", | |
| " return_source_documents=True,\n", | |
| " chain_type_kwargs=chain_type_kwargs,\n", | |
| " verbose=True,\n", | |
| ")\n", | |
| "\n", | |
| "def qa_standalone(question):\n", | |
| " inputs = template.format(context='', question=question)\n", | |
| " inputs = tokenizer(inputs, return_tensors='pt').to(model.device)\n", | |
| "\n", | |
| " with torch.no_grad():\n", | |
| " output_ids = model.generate(\n", | |
| " **inputs,\n", | |
| " max_new_tokens=512,\n", | |
| " pad_token_id=tokenizer.pad_token_id,\n", | |
| " eos_token_id=tokenizer.eos_token_id,\n", | |
| " )\n", | |
| " output = tokenizer.decode(output_ids.tolist()[0], skip_special_tokens=True)\n", | |
| " return output\n", | |
| "\n", | |
| "\n", | |
| "llmprint = lambda result: display(Markdown(\n", | |
| " f\"\"\"### Answer:\n", | |
| "\n", | |
| "{result['result']}\n", | |
| "---\n", | |
| "### Source：\n", | |
| "{result['source_documents']}\n", | |
| "\"\"\"))" | |
| ], | |
| "metadata": { | |
| "id": "3eiU_x9rocAe" | |
| }, | |
| "execution_count": 14, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Q and A デモンストレーション" | |
| ], | |
| "metadata": { | |
| "id": "1pj_nQJhwPvm" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "res = qa(\"呪術廻戦という漫画について簡単に説明してください．\")\n", | |
| "llmprint(res)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 513 | |
| }, | |
| "id": "n6zpvo72rICo", | |
| "outputId": "d3a1e367-1109-47ca-c4e0-03304088b357" | |
| }, | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n", | |
| "\n", | |
| "\u001b[1m> Finished chain.\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.Markdown object>" | |
| ], | |
| "text/markdown": "### Answer:\n\n\n答え: 呪術廻戦は、芥見下々による日本の漫画作品です。『週刊少年ジャンプ』（集英社）にて2018年14号から連載中。人間の負の感情から生まれる化け物・呪霊を呪術を使って祓う呪術師の闘いを描いた、ダークファンタジー・バトル漫画。略称は「呪術」です。\n---\n### Source：\n[Document(page_content='|ジャンプGIGA\\n|レーベル\\n|ジャンプ コミックス\\n|発表号\\n|2017 vol.1 - vol.4\\n|巻数\\n|全1巻\\n|テンプレート - ノート\\n|プロジェクト\\n|漫画\\n|ポータル\\n|漫画\\n『呪術廻戦』（じゅじゅつかいせん）は、芥見下々による日本の漫画作品。『週刊少年ジャンプ』（集英社）にて2018年14号から連載中。人間の負の感情から生まれる化け物・呪霊を呪術を使って祓う呪術師の闘いを描いた、ダークファンタジー・バトル漫画。略称は「呪術」[1]。', metadata={'source': 'textfile.txt'}), Document(page_content='呪術廻戦\\n始まり（1巻1話 - 5話）', metadata={'source': 'textfile.txt'}), Document(page_content='呪術廻戦\\n|呪術廻戦\\n|ジャンル\\n|少年漫画\\nダーク・ファンタジー\\n呪術・バトル\\n|漫画\\n|作者\\n|芥見下々\\n|出版社\\n|集英社\\n|掲載誌\\n|週刊少年ジャンプ\\n|レーベル\\n|ジャンプ コミックス\\n|発表号\\n|2018年14号 -\\n|発表期間\\n|2018年3月5日 -\\n|巻数\\n|既刊24巻（2023年10月現在）\\n|漫画：東京都立呪術高等専門学校\\n|作者\\n|芥見下々\\n|出版社\\n|集英社\\n|掲載誌\\n|ジャンプGIGA', metadata={'source': 'textfile.txt'}), Document(page_content='書誌情報\\nコミック\\n- 芥見下々 『呪術廻戦』 集英社〈ジャンプ コミックス〉、既刊24巻（2023年10月4日現在）\\n- 「両面宿儺」2018年7月9日発行（7月4日発売[集 1]）、ISBN 978-4-08-881516-9\\n- 「呪胎戴天」2018年9月9日発行（9月4日発売[集 2]）、ISBN 978-4-08-881608-1\\n- 「幼魚と逆罰」2018年12月9日発行（12月4日発売[集 3]）、ISBN 978-4-08-881666-1', metadata={'source': 'textfile.txt'}), Document(page_content='- ^ a b c d e f “漫画『呪術廻戦』累計850万部突破 1巻から新人作家として異例の売れ行き”. ORICON NEWS (2020年10月2日). 2020年10月13日閲覧。\\n- ^ “『呪術廻戦』五条悟と夏油傑の対比が意味するものは？ 痛みを伴う“組織改革の物語”を読む(2ページ目)”. Real Sound (2020年6月8日). 2020年10月28日閲覧。', metadata={'source': 'textfile.txt'})]\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "res = qa(\"加茂憲倫とは何者ですか？\")\n", | |
| "llmprint(res)" | |
| ], | |
| "metadata": { | |
| "id": "SBNSj3MsomYC" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "res = qa(\"五条悟の持つ六眼とはどんな能力を持っていますか？\")\n", | |
| "llmprint(res)" | |
| ], | |
| "metadata": { | |
| "id": "z7-8_il5oyFb" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "RAGを使わない回答例：" | |
| ], | |
| "metadata": { | |
| "id": "ww_ssaVbwIW-" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "result = qa_standalone(\"五条悟の持つ六眼とはどんな能力を持っていますか？\")\n", | |
| "print(result)" | |
| ], | |
| "metadata": { | |
| "id": "RvwOfHhso1ZP" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "「答え: 六眼は、視覚、聴覚、嗅覚、味覚、触覚、そして心を読むことができる能力です。」...多分，第六感のことじゃないかな..." | |
| ], | |
| "metadata": { | |
| "id": "Vatb3lG4wKkD" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## まとめ\n", | |
| "\n", | |
| "使い方が悪いようで，期待した回答とは程遠いものを出力させてしまいました．RAGの使い方をもっと洗練させることで，いくらか良い回答文を生成させることはできるんじゃないかなと思います．また，無料版colabのT4でも動作させられるLLMはこれ以外にもたくさんあります．是非遊んでみてください．" | |
| ], | |
| "metadata": { | |
| "id": "8dEdlzl4V4Fc" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "TODO：\n", | |
| "\n", | |
| "- 回答のStreaming出力\n", | |
| "- より大規模なテキストデータをRAGで活用する\n", | |
| "\n", | |
| "## 参考文献\n", | |
| "- [LangChainを使ったRAGをElyza 7bを用いて試してみた | alexweberk](https://note.com/alexweberk/n/n3cffc010e9e9)\n", | |
| "- [Llama-2-70B-Chat-GPTQ/discussions/](https://huggingface.co/TheBloke/Llama-2-70B-Chat-GPTQ/discussions/3)\n", | |
| "- [LangChainのTextSplitterを試す | npaka](https://note.com/npaka/n/nda9dc5eae1df)" | |
| ], | |
| "metadata": { | |
| "id": "U7UYQvfYv06-" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "VUaqQTiNwEBv" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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この記事は一関高専 Advent Calendar 2023 の8日目の記事のつもりで書いたものです（遅れてごめんね）