{ "cells": [ { "cell_type": "code", "source": [ "# Credit\n", "## Base code https://devocean.sk.com/blog/techBoardDetail.do?ID=165703\n", "## Updated by Yunho Maeng, yunhomaeng@yonsei.ac.kr" ], "metadata": { "id": "ABqAyuxVJ81s" }, "id": "ABqAyuxVJ81s", "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "id": "9e02c8d1-e653-41a5-a94f-e44c176dbcc5", "metadata": { "id": "9e02c8d1-e653-41a5-a94f-e44c176dbcc5" }, "source": [ "# 1. 개발 환경 설정" ] }, { "cell_type": "markdown", "id": "9fa242e1-7689-4397-b410-d550e79246c3", "metadata": { "id": "9fa242e1-7689-4397-b410-d550e79246c3" }, "source": [ "### 1.1 필수 라이브러리 설치하기" ] }, { "cell_type": "code", "execution_count": 2, "id": "3d405d7a-f2c9-4416-bf88-880812a2b8b5", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3d405d7a-f2c9-4416-bf88-880812a2b8b5", "outputId": "05d4afab-2d0c-49c3-f85f-b00644997ac6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.5/8.5 MB\u001b[0m \u001b[31m75.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m52.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m22.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.9/190.9 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m314.1/314.1 kB\u001b[0m \u001b[31m24.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m155.3/155.3 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.4/103.4 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.0/280.0 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "!pip3 install -q -U transformers==4.38.2\n", "!pip3 install -q -U datasets==2.18.0\n", "!pip3 install -q -U bitsandbytes==0.42.0\n", "!pip3 install -q -U peft==0.9.0\n", "!pip3 install -q -U trl==0.7.11\n", "!pip3 install -q -U accelerate==0.27.2" ] }, { "cell_type": "code", "source": [ "# 구글 드라이브 마운트\n", "\n", "from google.colab import drive\n", "import shutil\n", "import os\n", "\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SjWkITPKOX0a", "outputId": "4909a8e4-52dd-4e2c-da82-2a338f4342e0" }, "id": "SjWkITPKOX0a", "execution_count": 41, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ] }, { "cell_type": "markdown", "id": "13fa79b6-4720-43d1-baae-41d834011c2c", "metadata": { "id": "13fa79b6-4720-43d1-baae-41d834011c2c" }, "source": [ "### 1.2 Import modules" ] }, { "cell_type": "code", "execution_count": 3, "id": "1d7a17e3-b9a1-4a46-8f6e-7710a37a93bf", "metadata": { "id": "1d7a17e3-b9a1-4a46-8f6e-7710a37a93bf" }, "outputs": [], "source": [ "import torch\n", "from datasets import Dataset, load_dataset\n", "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline, TrainingArguments\n", "from peft import LoraConfig, PeftModel\n", "from trl import SFTTrainer" ] }, { "cell_type": "markdown", "id": "5b7f30d7-bfdf-49c5-8c2c-701ad6f15a80", "metadata": { "id": "5b7f30d7-bfdf-49c5-8c2c-701ad6f15a80" }, "source": [ "### 1.3 Huggingface 로그인" ] }, { "cell_type": "code", "execution_count": 4, "id": "6aa22976-7bdf-479d-8c5c-8ab890be537f", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "d78fb194ea584519899fca75316935a5", "eeaef7c987eb495ebbd676284b84bc31", "b1bf1acfbeec4d27b2718f155a4f60ce", "0b92582f5a0e43c58919395a3790e986", "caac082c36f0432f85cb061f0e4cf203", "60f18007db4c40cabbfe41b2a5c90b9c", "2ebba5dee6e24c2f8a5e7e91111adf26", "76861f23355c422183168aa4a5660b9b", "9b4b9867824443179df0472b564e3a97", "931c6d74a0564062a6420af5298b1422", "2772fecde87842c2a80ef4678fcbd4c5", "7d06a99eb059484896f93c19610feb8d", "35a8980320c24fd59db1bf2a2df284f8", "ce789e054ffb4088be8cc036fcb86c48", "c548f62b7cf64fce8c020b573e2df7b8", "f95a739b5b7c4b948b2875c349eeb3cc", "5daa3d4168854c6ebf941921a5e4f936", "2e0908343d34433297e9e2bbc2436fe9", "4cdeb606d9464690b8f4e5a1441410dc", "dd6eca03bb9d4abd99ee35abfdd19a94", "8ba757205acd45acaddd823734082247", "d14dbf68e27944f6bdd24e7782d4ef29", "347b4975b84d428b937a4dc68597193c", "3c6879dbb4ed48a8977253bd04b9eea0", "d2120616526743d6920a2c29013205b7", "2c7b2b17b2d14e10a72563e312057c8d", "d1d6fd83db1d44578d0ea4ef8e9e0a3c", "ac8a46db70004de8beec5005a05259d0", "fa4e79867ed34425a728767878347304", "3acecabb06cd41b5b5da81745ef5d4f4", "0e9990a90c75425b84e473a24e97c98a", "2826f483dd9c40f5bc47702b4d19a94f" ] }, "id": "6aa22976-7bdf-479d-8c5c-8ab890be537f", "outputId": "d92eff69-6d8a-419b-bc9b-505bc0bc3194" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
user\\n다음 글을 요약해주세요 :\\n\\n앵커 정부가 올해 하반기 우리 경제의 버팀목인 수출 확대를 위해 총력을 기울이기로 했습니다. 특히 수출 중소기업의 물류난 해소를 위해 무역금융 규모를 40조 원 이상 확대하고 물류비 지원과 임시선박 투입 등을 추진하기로 했습니다. 류환홍 기자가 보도합니다. 기자 수출은 최고의 실적을 보였지만 수입액이 급증하면서 올해 상반기 우리나라 무역수지는 역대 최악인 103억 달러 적자를 기록했습니다. 정부가 수출확대에 총력을 기울이기로 한 것은 원자재 가격 상승 등 대외 리스크가 가중되는 상황에서 수출 증가세 지속이야말로 한국경제의 회복을 위한 열쇠라고 본 것입니다. 추경호 경제부총리 겸 기획재정부 장관 정부는 우리 경제의 성장엔진인 수출이 높은 증가세를 지속할 수 있도록 총력을 다하겠습니다. 우선 물류 부담 증가 원자재 가격 상승 등 가중되고 있는 대외 리스크에 대해 적극 대응하겠습니다. 특히 중소기업과 중견기업 수출 지원을 위해 무역금융 규모를 연초 목표보다 40조 원 늘린 301조 원까지 확대하고 물류비 부담을 줄이기 위한 대책도 마련했습니다. 이창양 산업통상자원부 장관 국제 해상운임이 안정될 때까지 월 4척 이상의 임시선박을 지속 투입하는 한편 중소기업 전용 선복 적재 용량 도 현재보다 주당 50TEU 늘려 공급하겠습니다. 하반기에 우리 기업들의 수출 기회를 늘리기 위해 2 500여 개 수출기업을 대상으로 해외 전시회 참가를 지원하는 등 마케팅 지원도 벌이기로 했습니다. 정부는 또 이달 중으로 반도체를 비롯한 첨단 산업 육성 전략을 마련해 수출 증가세를 뒷받침하고 에너지 소비를 줄이기 위한 효율화 방안을 마련해 무역수지 개선에 나서기로 했습니다. YTN 류환홍입니다.\\nmodel\\n'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 12 } ], "source": [ "prompt" ] }, { "cell_type": "markdown", "id": "666223ea-2308-4126-a56c-a57fcec65390", "metadata": { "id": "666223ea-2308-4126-a56c-a57fcec65390" }, "source": [ "### 3.3 Gemma-it 추론" ] }, { "cell_type": "code", "execution_count": 13, "id": "a61247af-ce20-47cb-ae80-5a3e40d299f1", "metadata": { "id": "a61247af-ce20-47cb-ae80-5a3e40d299f1" }, "outputs": [], "source": [ "outputs = pipe(\n", " prompt,\n", " do_sample=True,\n", " temperature=0.2,\n", " top_k=50,\n", " top_p=0.95,\n", " add_special_tokens=True\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "id": "df721816-9d14-4890-bc7f-a441b5c02481", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "df721816-9d14-4890-bc7f-a441b5c02481", "outputId": "47fba015-aee7-46d7-ba9a-359c8a2676e3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "**요약:**\n", "\n", "* 앵커 정부는 수출 확대를 위해 총력을 기울이고 있으며, 특히 수출 중소기업의 물류난 해소를 위해 무역금융 규모를 40조 원 이상 확대하고 물류비 지원과 임시선박 투입 등을 추진하기로 했습니다.\n", "* 수출은 최고의 실적을 보였지만 수입액이 급증하면서 올해 상반기 우리나라 무역수지는 역대 최악인 103억 달러 적자를 기록했습니다.\n", "* 정부는 수출확대에 총력을 기울이기 위해 가중되고 있는 대외 리스크에 대해 적극 대응하겠습니다.\n", "* 특히 중소기업과 중견기업 수출 지원을 위해 무역금융 규모를 연초 목표보다 40조 원 늘리고 물류비 부담을 줄이기 위한 대책도 마련했습니다.\n" ] } ], "source": [ "print(outputs[0][\"generated_text\"][len(prompt):])" ] }, { "cell_type": "markdown", "id": "187a1bfb-b47c-448e-8957-86c00cc1df02", "metadata": { "id": "187a1bfb-b47c-448e-8957-86c00cc1df02" }, "source": [ "# 4. Gemma 파인튜닝" ] }, { "cell_type": "markdown", "id": "cc7b19a9-5a04-4d67-8004-de31fe0897a7", "metadata": { "id": "cc7b19a9-5a04-4d67-8004-de31fe0897a7" }, "source": [ "#### 주의: Colab GPU 메모리 한계로 이전장 추론에서 사용했던 메모리를 비워 줘야 파인튜닝을 진행 할 수 있습니다.
 notebook 런타임 세션을 재시작 한 후 1번과 2번의 2.1 항목까지 다시 실행하여 로드 한 후 아래 과정을 진행합니다" ] }, { "cell_type": "code", "source": [ "# !nvidia-smi\n", "import subprocess\n", "\n", "# GPU 상태 확인\n", "def check_gpu():\n", " try:\n", " result = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE)\n", " print(result.stdout.decode('utf-8'))\n", " except FileNotFoundError:\n", " print(\"nvidia-smi not found, ensure you have a GPU instance.\")\n", "\n", "check_gpu()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sYcINOAz4zX5", "outputId": "ed0d3f67-7b03-452e-a216-b28bf866cba7" }, "id": "sYcINOAz4zX5", "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Sat Jul 13 07:09:53 2024 \n", "+---------------------------------------------------------------------------------------+\n", "| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n", "|-----------------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|=========================================+======================+======================|\n", "| 0 NVIDIA A100-SXM4-40GB Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 41C P0 125W / 400W | 11395MiB / 40960MiB | 56% Default |\n", "| | | Disabled |\n", "+-----------------------------------------+----------------------+----------------------+\n", " \n", "+---------------------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=======================================================================================|\n", "+---------------------------------------------------------------------------------------+\n", "\n" ] } ] }, { "cell_type": "markdown", "id": "0a886413-a19c-4966-9e07-ca8cdb23aa16", "metadata": { "id": "0a886413-a19c-4966-9e07-ca8cdb23aa16" }, "source": [ "### 4.1 학습용 프롬프트 조정" ] }, { "cell_type": "code", "execution_count": 16, "id": "a9e4cc4b-a094-4035-906e-3edface3a099", "metadata": { "id": "a9e4cc4b-a094-4035-906e-3edface3a099" }, "outputs": [], "source": [ "def generate_prompt(example):\n", " prompt_list = []\n", " for i in range(len(example['document'])):\n", " prompt_list.append(r\"\"\"user\n", "다음 글을 요약해주세요:\n", "\n", "{}\n", "model\n", "{}\"\"\".format(example['document'][i], example['summary'][i]))\n", " return prompt_list" ] }, { "cell_type": "code", "execution_count": 17, "id": "c45ab1ee-8146-4731-86ec-d673e9a67557", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "c45ab1ee-8146-4731-86ec-d673e9a67557", "outputId": "db0568fc-951e-43df-b204-fae09d1f0f92" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "user\n", "다음 글을 요약해주세요:\n", "\n", "앵커 정부가 올해 하반기 우리 경제의 버팀목인 수출 확대를 위해 총력을 기울이기로 했습니다. 특히 수출 중소기업의 물류난 해소를 위해 무역금융 규모를 40조 원 이상 확대하고 물류비 지원과 임시선박 투입 등을 추진하기로 했습니다. 류환홍 기자가 보도합니다. 기자 수출은 최고의 실적을 보였지만 수입액이 급증하면서 올해 상반기 우리나라 무역수지는 역대 최악인 103억 달러 적자를 기록했습니다. 정부가 수출확대에 총력을 기울이기로 한 것은 원자재 가격 상승 등 대외 리스크가 가중되는 상황에서 수출 증가세 지속이야말로 한국경제의 회복을 위한 열쇠라고 본 것입니다. 추경호 경제부총리 겸 기획재정부 장관 정부는 우리 경제의 성장엔진인 수출이 높은 증가세를 지속할 수 있도록 총력을 다하겠습니다. 우선 물류 부담 증가 원자재 가격 상승 등 가중되고 있는 대외 리스크에 대해 적극 대응하겠습니다. 특히 중소기업과 중견기업 수출 지원을 위해 무역금융 규모를 연초 목표보다 40조 원 늘린 301조 원까지 확대하고 물류비 부담을 줄이기 위한 대책도 마련했습니다. 이창양 산업통상자원부 장관 국제 해상운임이 안정될 때까지 월 4척 이상의 임시선박을 지속 투입하는 한편 중소기업 전용 선복 적재 용량 도 현재보다 주당 50TEU 늘려 공급하겠습니다. 하반기에 우리 기업들의 수출 기회를 늘리기 위해 2 500여 개 수출기업을 대상으로 해외 전시회 참가를 지원하는 등 마케팅 지원도 벌이기로 했습니다. 정부는 또 이달 중으로 반도체를 비롯한 첨단 산업 육성 전략을 마련해 수출 증가세를 뒷받침하고 에너지 소비를 줄이기 위한 효율화 방안을 마련해 무역수지 개선에 나서기로 했습니다. YTN 류환홍입니다.\n", "model\n", "올해 상반기 우리나라 무역수지는 역대 최악인 103억 달러 적자를 기록한 가운데, 정부가 하반기에 우리 경제의 버팀목인 수출 확대를 위해 총력을 기울이기로 결정한 가운데, 특히 수출 중소기업의 물류난 해소를 위해 무역금융 규모를 40조 원 이상 확대하고 물류비 지원과 임시선박 투입 등을 추진하기로 했다.\n" ] } ], "source": [ "train_data = dataset['train']\n", "print(generate_prompt(train_data[:1])[0])" ] }, { "cell_type": "markdown", "id": "1849b4c0-16f3-44f3-bb67-7022f226ec05", "metadata": { "id": "1849b4c0-16f3-44f3-bb67-7022f226ec05" }, "source": [ "### 4.2 QLoRA 설정" ] }, { "cell_type": "code", "execution_count": 18, "id": "5c085b4b-a471-4c5a-afe3-81e8e0c37756", "metadata": { "id": "5c085b4b-a471-4c5a-afe3-81e8e0c37756" }, "outputs": [], "source": [ "lora_config = LoraConfig(\n", " r=6,\n", " target_modules=[\"q_proj\", \"o_proj\", \"k_proj\", \"v_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n", " task_type=\"CAUSAL_LM\",\n", ")\n", "\n", "bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_quant_type=\"nf4\",\n", " bnb_4bit_compute_dtype=torch.float16\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "id": "e10bfd65-00f8-49b6-933c-a27ed4385373", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 104, "referenced_widgets": [ "c39879cd55444e7e9be2896a707b15bd", "2f9755c8456c4f0a844d240a122c6694", "8253abee56074e3eaa654d926175a57f", "24ce3f11f5984d19a80741ecb6b76c2e", "2f85cfbc24a74ed7a3ffa6f018c9a178", "a75362a2219f47a399ef2718894b3a0e", "94c465090bec45bda439fd72f83bc34d", "5ba37c66a9144611a2305d1a7fd9492c", "383c9bec5c024d41acc9bda77d106674", "8c2755efc2f74ca2bece05c7a0d42b21", "8b04416f4bcf4944b6c73a1506150e44" ] }, "id": "e10bfd65-00f8-49b6-933c-a27ed4385373", "outputId": "d8bcc54f-87bf-4a44-a221-a01db6e8a982" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [3000/3000 42:13, Epoch 0/1]\n", "
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StepTraining Loss
1002.439600
2002.108500
3002.051300
4001.939200
5001.931500
6001.925600
7001.859700
8001.852200
9001.860200
10001.864000
11001.817400
12001.776700
13001.731000
14001.721500
15001.732700
16001.779000
17001.699800
18001.664400
19001.654700
20001.695800
21001.717600
22001.697600
23001.677300
24001.627900
25001.662800
26001.630000
27001.642800
28001.673700
29001.632200
30001.619400

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=3000, training_loss=1.7895308176676432, metrics={'train_runtime': 2534.1773, 'train_samples_per_second': 4.735, 'train_steps_per_second': 1.184, 'total_flos': 6.742903609432474e+16, 'train_loss': 1.7895308176676432, 'epoch': 0.54})" ] }, "metadata": {}, "execution_count": 21 } ], "source": [ "trainer.train()" ] }, { "cell_type": "markdown", "id": "dca74e51-15ec-403a-90f1-4b7eeb2c723b", "metadata": { "id": "dca74e51-15ec-403a-90f1-4b7eeb2c723b" }, "source": [ "### 4.4 Finetuned Model 저장" ] }, { "cell_type": "code", "execution_count": 22, "id": "f2bba87d-d95c-4a57-9eb1-c02d81ad7bfb", "metadata": { "id": "f2bba87d-d95c-4a57-9eb1-c02d81ad7bfb", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "034a5c88-6651-467e-d2da-f23c796f6b57" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", " warnings.warn(\n" ] } ], "source": [ "ADAPTER_MODEL = \"lora_adapter\"\n", "\n", "trainer.model.save_pretrained(ADAPTER_MODEL)" ] }, { "cell_type": "code", "execution_count": 26, "id": "6a9fcda0-1d7a-4443-9b1c-7d45490daafb", "metadata": { "id": "6a9fcda0-1d7a-4443-9b1c-7d45490daafb", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f77eea88-1a14-4b94-caf8-2a2376fa0fa7" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "adapter_model.safetensors\t Size: 29450584 bytes\n", "README.md\t Size: 5091 bytes\n", "adapter_config.json\t Size: 689 bytes\n" ] } ], "source": [ "# !ls -alh lora_adapter\n", "import os\n", "\n", "# 디렉터리 내용 확인\n", "def list_directory_contents(directory):\n", " try:\n", " with os.scandir(directory) as entries:\n", " for entry in entries:\n", " info = entry.stat()\n", " print(f\"{entry.name}\\t Size: {info.st_size} bytes\")\n", " except FileNotFoundError:\n", " print(f\"Directory '{directory}' not found.\")\n", "\n", "ADAPTER_MODEL = \"lora_adapter\"\n", "list_directory_contents(ADAPTER_MODEL)" ] }, { "cell_type": "code", "execution_count": 27, "id": "a9a2a6d7-ece4-472a-981f-fb6599d1d307", "metadata": { "id": "a9a2a6d7-ece4-472a-981f-fb6599d1d307", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "d83f27176aac4ce1a0ce373c0521f448", "3432e44ff4e84e8db6439d46a299b1dd", "74fb934e195b4535be099894f1d1a016", "973c9e71beb045f0b30739a3f4da06fd", "87e66fe990c841b0af70e3072b6d531f", "10c474866a3c46989753a3b594873ff4", "430d8d46284a422a887de79a179d2ffd", "b7fc7f2d31b54b8f8341abb83a1f6821", "856f1c4dae8b4566bc09679c40148e0a", "76c2cc35d7934c1c8d69e3d62cb32b1f", "72e2e556c0854cbab198ae1d2a527dab" ] }, "outputId": "df2c540e-4ff1-48c7-ded8-c436f78d16e1" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00 notebook 런타임 세션을 재시작 한 후 1번과 2번의 2.1 항목까지 다시 실행하여 로드 한 후 아래 과정을 진행합니다" ] }, { "cell_type": "code", "execution_count": 31, "id": "906ed4dd-270f-4000-84de-ede6885c0be5", "metadata": { "id": "906ed4dd-270f-4000-84de-ede6885c0be5", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9fc93e12-c20b-4df0-f642-10d8ff4ef737" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Sat Jul 13 08:11:51 2024 \n", "+---------------------------------------------------------------------------------------+\n", "| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n", "|-----------------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|=========================================+======================+======================|\n", "| 0 NVIDIA A100-SXM4-40GB Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 39C P0 53W / 400W | 18449MiB / 40960MiB | 0% Default |\n", "| | | Disabled |\n", "+-----------------------------------------+----------------------+----------------------+\n", " \n", "+---------------------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=======================================================================================|\n", "+---------------------------------------------------------------------------------------+\n", "\n" ] } ], "source": [ "# !nvidia-smi\n", "import subprocess\n", "\n", "# GPU 상태 확인\n", "def check_gpu():\n", " try:\n", " result = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE)\n", " print(result.stdout.decode('utf-8'))\n", " except FileNotFoundError:\n", " print(\"nvidia-smi not found, ensure you have a GPU instance.\")\n", "\n", "check_gpu()" ] }, { "cell_type": "markdown", "id": "78399236-63b5-41af-9cee-a7233e23a9db", "metadata": { "id": "78399236-63b5-41af-9cee-a7233e23a9db" }, "source": [ "### 5.1 Fine-tuned 모델 로드" ] }, { "cell_type": "code", "execution_count": 32, "id": "76d5ba97-91ca-48c3-b9a2-ba9bea6d7b09", "metadata": { "id": "76d5ba97-91ca-48c3-b9a2-ba9bea6d7b09", "colab": { "base_uri": "https://localhost:8080/", "height": 104, "referenced_widgets": [ "1fae26b44ce144c1afe26fc6d87bc4c9", "cdb6b8a160974912b7f5646b989d2142", "cc3f97a2e623494cad8591f63adfde90", "7f2c674bef754cf6ad867efec44abae4", "579bb488cea94912a128a53c6103de8f", "3faa57373d3648b7b30405dfded60a5b", "5b392e95468441c5b1f7d7696f92cbcb", "8dc06b4f1b9741c68bf63624476a09f1", "b1b3817be3e146189548aafd8b908d5f", "5d54bb59f5f148209ca0c20078f21262", "3db996ed5cae4350b9115b21a6c040e6" ] }, "outputId": "cd058a27-4a2d-4b1d-d68e-9b7a41a2e4d3" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00
Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.

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