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uchidama/model_inference_template_unsloth_20241127_myver.ipynb

Created December 12, 2024 05:00
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model_inference_template_unsloth_20241127_myver.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/uchidama/e126673d841021f4c0cd9f7371abc2a3/model_inference_template_unsloth_20241127_myver.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MljifiTVCT0_"
},
"source": [
"# 推論用コード\n",
"本コードはunslothで学習したqLoRAのアダプタを用いてELYZA-tasks-100-TVの出力を得るためのコードです。 \n",
"Hugging Faceにアダプタをアップロードしてあることが前提となります。\n",
"このコードはunslothライブラリを用いてモデルを読み込み、推論するためのコードとなります。\n",
"このコードで生成されたjsonlファイルは課題の成果として提出可能なフォーマットになっております。\n",
"\n",
"※本コードはGoogle Colabでの動作を想定しており、Omnicampusでの動作を想定しておりません。\n",
"Omnicampus向けのコードは別途用意しております。"
]
},
{
"cell_type": "code",
"source": [
"# Google Driveをマウント\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"id": "0oBAvVQ-KXiT",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "45d087b6-ab38-4bd5-cf8c-8dc3d7bb1101"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "I5B5MOHuBy8b"
},
"outputs": [],
"source": [
"# 必要なライブラリをインストール\n",
"%%capture\n",
"!pip install unsloth\n",
"!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
"!pip install -U torch\n",
"!pip install -U peft"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "GM7SNRtACg9V",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2e639af3-3e6b-4e3b-90fe-3c8bd8cd443b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
"🦥 Unsloth Zoo will now patch everything to make training faster!\n"
]
}
],
"source": [
"# 必要なライブラリを読み込み\n",
"from unsloth import FastLanguageModel\n",
"from peft import PeftModel\n",
"import torch\n",
"import json\n",
"from tqdm import tqdm\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "JmdUATTVCtyr"
},
"outputs": [],
"source": [
"# ベースとなるモデルと学習したLoRAのアダプタ（Hugging FaceのIDを指定）。\n",
"model_id = \"llm-jp/llm-jp-3-13b\"\n",
"adapter_id = \"uchidama/llm-jp-3-13b-it_lora\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "nKR3uTNfIpSz"
},
"outputs": [],
"source": [
"# Hugging Face Token を指定。\n",
"# 下記の URL から Hugging Face Token を取得できますので下記の HF_TOKEN に入れてください。\n",
"# https://huggingface.co/settings/tokens\n",
"HF_TOKEN = \"\" #@param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "TB6Hzx-2B5g8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 580,
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},
"outputId": "26ff99ba-5b70-4738-d838-7c4db184c982"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unsloth: WARNING `trust_remote_code` is True.\n",
"Are you certain you want to do remote code execution?\n",
"==((====))== Unsloth 2024.12.4: Fast Llama patching. Transformers:4.46.3.\n",
" \\\\ /| GPU: NVIDIA L4. Max memory: 22.168 GB. Platform: Linux.\n",
"O^O/ \\_/ \\ Torch: 2.5.1+cu121. CUDA: 8.9. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
"\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.28.post3. FA2 = False]\n",
" \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n",
"Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
]
},
{
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"Loading checkpoint shards: 0%| | 0/6 [00:00<?, ?it/s]"
],
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],
"source": [
"# unslothのFastLanguageModelで元のモデルをロード。\n",
"dtype = None # Noneにしておけば自動で設定\n",
"load_in_4bit = True # 今回は13Bモデルを扱うためTrue\n",
"\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name=model_id,\n",
" dtype=dtype,\n",
" load_in_4bit=load_in_4bit,\n",
" trust_remote_code=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "L0atmvr2IpSz",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
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},
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{
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"model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
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"source": [
"# タスクとなるデータの読み込み。\n",
"# 事前にデータをアップロードしてください。\n",
"datasets = []\n",
"with open(\"./elyza-tasks-100-TV_0.jsonl\", \"r\") as f:\n",
"#with open(\"/content/drive/MyDrive/Colab Notebooks/LLM2024/FinalAssignment/elyza-tasks-100-TV_0.jsonl\", \"r\") as f:\n",
"\n",
" item = \"\"\n",
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]
},
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"metadata": {
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"colab": {
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},
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},
"outputs": [
{
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"name": "stderr",
"text": [
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]
}
],
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"# モデルを用いてタスクの推論。\n",
"\n",
"# 推論するためにモデルのモードを変更\n",
"FastLanguageModel.for_inference(model)\n",
"\n",
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" input = dt[\"input\"]\n",
"\n",
" prompt = f\"\"\"### 指示\\n{input}\\n### 回答\\n\"\"\"\n",
"\n",
" inputs = tokenizer([prompt], return_tensors = \"pt\").to(model.device)\n",
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" outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2)\n",
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" results.append({\"task_id\": dt[\"task_id\"], \"input\": input, \"output\": prediction})"
]
},
{
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"metadata": {
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"# 結果をjsonlで保存。\n",
"\n",
"# ここではadapter_idを元にファイル名を決定しているが、ファイル名は任意で問題なし。\n",
"json_file_id = re.sub(\".*/\", \"\", adapter_id)\n",
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" for result in results:\n",
" json.dump(result, f, ensure_ascii=False)\n",
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