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in /usr/local/lib/python3.10/dist-packages (from datasets==2.19.0) (14.0.2)\n","Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets==2.19.0) (0.6)\n","Collecting dill<0.3.9,>=0.3.0 (from datasets==2.19.0)\n","  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets==2.19.0) (2.1.4)\n","Collecting xxhash (from datasets==2.19.0)\n","  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n","Collecting multiprocess (from datasets==2.19.0)\n","  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n","Collecting fsspec<=2024.3.1,>=2023.1.0 (from fsspec[http]<=2024.3.1,>=2023.1.0->datasets==2.19.0)\n","  Downloading fsspec-2024.3.1-py3-none-any.whl.metadata (6.8 kB)\n","Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets==2.19.0) 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nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate==0.29.3)\n","  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate==0.29.3)\n","  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate==0.29.3)\n","  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate==0.29.3)\n","  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.10.0->accelerate==0.29.3)\n","  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 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nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n","Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n","Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n","Downloading tyro-0.8.8-py3-none-any.whl (104 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.6/104.6 kB\u001b[0m \u001b[31m108.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta 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Found existing installation: accelerate 0.32.1\n","    Uninstalling accelerate-0.32.1:\n","      Successfully uninstalled accelerate-0.32.1\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","gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.3.1 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed accelerate-0.29.3 bitsandbytes-0.43.1 datasets-2.19.0 dill-0.3.8 fsspec-2024.3.1 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105 peft-0.10.0 shtab-1.7.1 transformers-4.44.2 trl-0.8.6 tyro-0.8.8 xxhash-3.5.0\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_epxp4_yImAr","executionInfo":{"status":"ok","timestamp":1724535089263,"user_tz":240,"elapsed":11448,"user":{"displayName":"박병건","userId":"04072544495695427838"}},"outputId":"f198a5d7-ffab-4d67-b934-6690fd725cfc"},"outputs":[{"output_type":"stream","name":"stdout","text":["The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n","Token is valid (permission: write).\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful\n"]}],"source":["import os\n","import torch\n","from datasets import load_dataset\n","\n","from transformers import (\n","    AutoModelForCausalLM,\n","    AutoTokenizer,\n","    BitsAndBytesConfig,\n","    TrainingArguments,\n","    pipeline,\n","    logging,\n",")\n","from peft import LoraConfig\n","from trl import SFTTrainer\n","\n","if 'COLAB_GPU' in os.environ:\n","    !huggingface-cli login\n","else:\n","    notebook_login()"]},{"cell_type":"code","source":["!pip install jsonlines"],"metadata":{"id":"RD4UYml8YNdU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!git clone https://github.com/poloclub/Fine-tuning-LLMs.git\n","\n","if 'COLAB_GPU' in os.environ:\n","    dir_path = '/content/Fine-tuning-LLMs/data/'\n","else:\n","    dir_path = 'Fine-tuning-LLMs/data/'\n","\n","os.chdir(dir_path)\n","\n","####\n","import jsonlines\n","\n","def txt_to_jsonl(txt_file_path, jsonl_file_path):\n","  with open(txt_file_path, 'r') as txt_file, open(jsonl_file_path, 'w') as jsonl_file:\n","    for line in txt_file:\n","      jsonl_file.write(line)\n","\n","txt_to_jsonl('hawaii_wf_1.txt', 'hawaii_wf_1.jsonl')\n","txt_to_jsonl('hawaii_wf_2.txt', 'hawaii_wf_2.jsonl')\n","txt_to_jsonl('hawaii_wf_3.txt', 'hawaii_wf_3.jsonl')\n","txt_to_jsonl('hawaii_wf_4.txt', 'hawaii_wf_4.jsonl')\n","txt_to_jsonl('hawaii_wf_5.txt', 'hawaii_wf_5.jsonl')\n","txt_to_jsonl('hawaii_wf_6.txt', 'hawaii_wf_6.jsonl')\n","txt_to_jsonl('hawaii_wf_7.txt', 'hawaii_wf_7.jsonl')\n","txt_to_jsonl('hawaii_wf_8.txt', 'hawaii_wf_8.jsonl')\n","txt_to_jsonl('hawaii_wf_9.txt', 'hawaii_wf_9.jsonl')\n","txt_to_jsonl('hawaii_wf_10.txt', 'hawaii_wf_10.jsonl')\n","\n","train_dataset = load_dataset('jsonl', data_files={\"train\":[\n","    \"hawaii_wf_1.jsonl\", \"hawaii_wf_2.jsonl\",\n","    \"hawaii_wf_3.jsonl\",\"hawaii_wf_4.jsonl\",\n","    \"hawaii_wf_5.jsonl\",\"hawaii_wf_6.jsonl\",\n","    \"hawaii_wf_7.jsonl\",\"hawaii_wf_8.jsonl\",\n","    \"hawaii_wf_9.jsonl\",\"hawaii_wf_10.jsonl\"\n","]}, split='train')\n","\n","####\n","\n","train_dataset = load_dataset(\"text\", data_files={\"train\":\n","                [\"hawaii_wf_1.txt\", \"hawaii_wf_2.txt\",\n","                 \"hawaii_wf_3.txt\",\"hawaii_wf_4.txt\",\n","                 \"hawaii_wf_5.txt\",\"hawaii_wf_6.txt\",\n","                 \"hawaii_wf_7.txt\",\"hawaii_wf_8.txt\",\n","                \"hawaii_wf_9.txt\",\"hawaii_wf_10.txt\",\"hawaii_wf_11.txt\"]}, split='train')\n","\n","####\n","\n","os.chdir('..')\n","os.chdir('..')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":178,"referenced_widgets":["1c69af8ffc324ec9b9fec8b1b726d2e7","705f023fa1e14789a893eaa67e9d5472","88aa51859db54e60b48be777669697f7","42c68c2afd994ed9b701ee38b9654204","a7c32ac042d145c299b370d36361f615","5ef11c4214cc45318d9d486a1d5827ac","71e899eb264e41999bd3a857381ed873","ff27cfe9540f4545904dd3efd2e83d44","f33fda227f8a4f8c8868fbcdfe06d1cb","870188d2d5e94595bd2f3f374705ec4c","a82e96905b4e42ce99385d87c6965784"]},"id":"Sz3xYtBGSu7-","executionInfo":{"status":"ok","timestamp":1724535096305,"user_tz":240,"elapsed":3097,"user":{"displayName":"박병건","userId":"04072544495695427838"}},"outputId":"c0e6de66-2e0d-4499-aca9-470d0c80cacc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'Fine-tuning-LLMs'...\n","remote: Enumerating objects: 47, done.\u001b[K\n","remote: Counting objects: 100% (47/47), done.\u001b[K\n","remote: Compressing objects: 100% (42/42), done.\u001b[K\n","remote: Total 47 (delta 14), reused 29 (delta 4), pack-reused 0 (from 0)\u001b[K\n","Receiving objects: 100% (47/47), 9.34 MiB | 17.81 MiB/s, done.\n","Resolving deltas: 100% (14/14), done.\n"]},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1c69af8ffc324ec9b9fec8b1b726d2e7"}},"metadata":{}}]},{"cell_type":"code","source":["# Hugging Face Basic Model 한국어 모델\n","# base_model = \"teddylee777/Llama-3-Open-Ko-8B-gguf\"  # 테디님의 Llama3 한국어 파인튜닝 모델\n","base_model = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"               # beomi님의 Llama3 한국어 파인튜닝 모델\n","\n","# 주가 증권 보고서 gemini 데이터셋\n","# hkcode_dataset = \"uiyong/gemini_result_kospi_0517_jsonl\"\n","hkcode_dataset = train_dataset\n","\n","# 새로운 모델 이름\n","new_model = \"Llama3.1-test1\""],"metadata":{"id":"vdyUFmwMI36H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["if torch.cuda.get_device_capability()[0] >= 8:\n","    !pip install -qqq flash-attn\n","    attn_implementation = \"flash_attention_2\"\n","    torch_dtype = torch.bfloat16\n","else:\n","    attn_implementation = \"eager\"\n","    torch_dtype = torch.float16"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eB8UMnM0Kqhc","executionInfo":{"status":"ok","timestamp":1724535124852,"user_tz":240,"elapsed":20578,"user":{"displayName":"박병건","userId":"04072544495695427838"}},"outputId":"fd0aa3f1-ade8-4cc6-acd9-fd5dcd4c4f01"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m117.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m64.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n"]}]},{"cell_type":"code","source":["# QLoRA config\n","quant_config = BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch_dtype,\n","    bnb_4bit_use_double_quant=False,\n",")"],"metadata":{"id":"ESvtu0JsKraI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# llama 데이터 로드\n","# dataset = load_dataset(hkcode_dataset, split=\"train\")\n","dataset = hkcode_dataset\n","\n","# 데이터 확인\n","print( dataset[5] )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fFjNhPvLK3xe","executionInfo":{"status":"ok","timestamp":1724535171854,"user_tz":240,"elapsed":508,"user":{"displayName":"박병건","userId":"04072544495695427838"}},"outputId":"0e097dcf-79c3-41cd-c4b1-2e98ba7dd4d4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'text': 'FAX: (808) 244-6411'}\n"]}]},{"cell_type":"code","source":["# !pip install --upgrade transformers"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":653},"id":"HbQgN0I7LUCr","executionInfo":{"status":"ok","timestamp":1724533009925,"user_tz":240,"elapsed":8109,"user":{"displayName":"박병건","userId":"04072544495695427838"}},"outputId":"779d5b98-0a13-48de-80c6-7ad4621cf432"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.40.1)\n","Collecting transformers\n","  Downloading transformers-4.44.2-py3-none-any.whl.metadata (43 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.4)\n","Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.3.1)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n","Downloading transformers-4.44.2-py3-none-any.whl (9.5 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.5/9.5 MB\u001b[0m \u001b[31m116.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: transformers\n","  Attempting uninstall: transformers\n","    Found existing installation: transformers 4.40.1\n","    Uninstalling transformers-4.40.1:\n","      Successfully uninstalled transformers-4.40.1\n","Successfully installed transformers-4.44.2\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["transformers"]},"id":"0f3faafea424462a83e5c31d0621cb38"}},"metadata":{}}]},{"cell_type":"code","source":["### 모델 로드\n","model = AutoModelForCausalLM.from_pretrained(\n","    base_model,\n","    quantization_config=quant_config,\n","    device_map={\"\": 0}\n",")\n","model.config.use_cache = False\n","model.config.pretraining_tp = 1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":436,"referenced_widgets":["a61318d24a16404f86c57a2a6f3dc024","d7282aa536a94770b13646d357593964","659c2366b9354bcfaeee39718d68e61d","dbdcac6f577049d19a198392038dc85a","5b3bc4693de74f03a44013f35f185292","ea113c44e8fc40e4bdcf6d7be5a4e385","eac5e8c6ea3b4036bf4980dd117d73e0","8b46ca3025f047259720d79924b2533c","ab95d998abc7425bb9f160339d078bc8","83de375433d8415faecd366ad2c4161c","76e2b89cc385437990b939c3e7aa44d7","43e2e26ffe1b4f8a8f91cb013c239da2","c9c2eba7c9804d8ea50c2c9d8a4ec098","998a46911fff45e98a463f73f446d498","b9e74f18474a4d108ad093c393df0243","ea1015e4115644d5a2450afaf8613771","588183eebae541a2becf1832ca01f0d4","91578260f1e844ccbe25e0a8c823e748","a994ee7297ed49efa9780af4fa9d80a1","dddbb17b71c74f8f8e89b4aca4860e6b","dc24c40fc4f44cc3a85f2c2050e4d549","12de3c271b734f61a08644b6545e8e4e","a1cf4f4433c94b2b9845d487986cc15d","c8abb02c194d4c89aa44f0689159db33","7e1c4f49ed214e3bb2dda2ef3d1dc44a","8ad64bcb0753446abad33691f5880f9c","4af3d9300f524761a783b5e3a1578fdb","6f126b3d0c8541f39288fb36f87bb2ba","9f074f6c8d774030a903d03ef2ceb44d","a54c2288e4a64043888659cf98ce191e","9fb6c71c51d54f359e53a5558f58c7b4","608f269d17a74f94b3459435e0fac8b9","a649fcc688b24fc99356e354b4398702","ca01387b5eca4cb8bf64ce9a6e223696","33c47fcf8e75405788a54bc8c7bccd0b","531e6b017ab04595bf5595a1b18eb270","442aae9810284eb39ebcf398225a7f44","db3c9ccd86904067aa0ed155748cca11","c43546bdffd243488ca2599f908deb39","2bd89c32c17c4702abedbecb8693cd40","df7dad62f3b54fc1907e80c73ba214b1","d9d78897fe424234bc0a9c2074002316","16164059c1244ed2b23cb77bb67e6d3f","d329b8b70afb461abe985d2f9280a5d6","1aa87e9fff8945f3943518ee68b48a13","04c93b58967942e48e4016545cb40ba8","b3d1b4dab7894fbbba2b2af4e33801e0","9bb7f84a090c4a97a2f37055201a4599","099a54cbcb964e7c8441c33fe375bdc6","f4d36e2f145e4fa1b2aee4a5e249e4c3","34eef8ed903e4548a420afdf63f6e458","6ee0d86bd57f4b27b3876e40748226d4","32324f19ba454620806936fef5746cf4","7395acbc85c84a6bb9f14ebd0205cb5f","cc7d00b69c0b466c80bd485b899e2289","e8b9ea96628444f9962e354149fecdb2","3abd54ecf9b04d95b94148483340be9d","c0ab158393d649b98c602711d66b70df","02a9ea4cca794468ae2603b5e9cc21aa","5bee25cd81f240b6bacce4acb8eea5ee","9fb199c368664c8fb9f34d7287c2851a","729a1d05314b4cd49bc25b2129a4054c","cddda01087bb4a08ab84a2b61dc42c07","4b6b20f0d7a145f1bb31866c6bfaadd3","d7b4daaa02dd4711a8d6be03ba2bcff8","8c40ef1ffa3f43989b2171c2141a5f31","182f2e859767416f9c07d3339f889646","d7a88e62cedc491d8d8540e54054c9d8","9369291bfa8a4fdc816105ee8cbffe98","54b4f88f6762442da3e77fc8a9c1f111","0afff0a4d88944c6954c7a58d4a6d65c","6916f421ad63454ab74dcb008785905a","3b530192a4f04b84a4111d8a5abfdd95","88d0e053239c49ecbd0f63e08099015f","4a2cbd29c2fc42aabb9b73844713856b","9f07d650083c40398d1fb9364a2b5347","7541e3d163464182ae1a79d152a48091","d89e8cc6379b4aeda74e221b050de4ca","b521202773b7472b9c6b774acb3a7488","fde36211a61b42d69126f63008b26021","0e3fa7646cc5408b9c633249f339cdd5","dd1148f1d42046b4b90f4c52898af746","80c05515c2b541ea93ca108e1804ceff","4f9050f016e743e0b6102959709ddf34","8fbaa1ef84ad438a97044ddbc01e7dc5","ed287315945a42c485a4d3ce5d6c8496","b44f9e46bf1446edb6431fc26831ec39","9779260679a0475ca3d18a15b8869f49","b16179f52cfa424ea0cfa50d145b7154","b5aaae68c1ba41248aa5a801c30f38b1","744bc9d3e5084ea5a2540886afd91323","35b50904d44a4d3f9cf4f61b74065894","7de2af42ba5c4466a2104800e77b31fa","37343b198f0a48aba313459cab1ca6d5","c69e25e216624470ba1a752f2ca57266","2f5fec7443654077b48c6e48b49896d4","a5c8734b1d45448f88a8e2c49cb03f71","e9b20de244144cefb036f773a735a7d0","2c353bfeb4de4d75bfb68502695e9cb4"]},"id":"CXfoQ7n1K510","executionInfo":{"status":"ok","timestamp":1724535237036,"user_tz":240,"elapsed":57550,"user":{"displayName":"박병건","userId":"04072544495695427838"}},"outputId":"480c35b9-c72c-4bf6-c8c1-d5d24d28465b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/855 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a61318d24a16404f86c57a2a6f3dc024"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"43e2e26ffe1b4f8a8f91cb013c239da2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading shards:   0%|          | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a1cf4f4433c94b2b9845d487986cc15d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00004.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ca01387b5eca4cb8bf64ce9a6e223696"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00004.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1aa87e9fff8945f3943518ee68b48a13"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00003-of-00004.safetensors:   0%|          | 0.00/4.92G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e8b9ea96628444f9962e354149fecdb2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00004-of-00004.safetensors:   0%|          | 0.00/1.17G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"182f2e859767416f9c07d3339f889646"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d89e8cc6379b4aeda74e221b050de4ca"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/184 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b16179f52cfa424ea0cfa50d145b7154"}},"metadata":{}}]},{"cell_type":"code","source":["# 토크나이저 로드\n","tokenizer = AutoTokenizer.from_pretrained(\n","              base_model,\n","              trust_remote_code=True)\n","tokenizer.pad_token = tokenizer.eos_token\n","tokenizer.padding_side = \"right\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["5e8813217dc949d18f3626109e84e1df","08283b7602b440a2a0cd4d6e076090cd","1572872c1e39470dbe9bb5189825d948","903d51bccde7461cae42d0096564fd61","e85b696a3de644eda98a5fdac1c3b202","7b373ee5da5d48088cf925627d448b83","03c9c3fbaa5f4b5ab780e8f9f2b6170c","cedfd931d1fe40b9b2ab62ea354b01ae","21110fa407844235bd51b8662367d8bd","a8f1e36c9a0d45dfa887e7ea77c58632","8b9bd13c187648aea9cbd80dd7f2ab43","7cc9f7f5819d4a03a235e04a50d1eaf6","82f251b7fb554def9afd121ce3fb7501","a35c05a055d84aeb987bc76131068078","a6cba80f957049d780be34f6fe7d35e6","f393eec90bec4cbda4ac28c1b3a41b19","8b01754646134db29cd77acb5252a271","eb3c3d9f5016471391b3fc972fc8d5c0","e313f1e0466d4f0ca4a152d9d3a556a7","45c325c8233b4a02af5426a18f969e50","bfe95da5b4e34dcaa0b09f725e237dd0","4f9b3efe3508421993dd037f7027c582","b56d58cc957b4cc59183c5c06ac7d19f","483f57bb1d0344ac8090f5eb768fe779","15310ec1338e46999f9ef9b948280e31","4467c352dadf498d901e49cc98e1236a","35dd7f2348c042b1b7b6a4b1a65344b9","f963d5e7d3ad492290769516a33e4a8e","ac096e13cfed49378f3fba5e4dd03cd0","e91896963d934e6aa284c859b674a647","41328259f0ae4fbaa1de5a835960ef96","17bf1b5382094450801ffa9d944348cb","6ea2f6a0fcae4393b1ec9c7e4cbdd2a6"]},"id":"uiN4fDRxK8t1","executionInfo":{"status":"ok","timestamp":1724535242875,"user_tz":240,"elapsed":3037,"user":{"displayName":"박병건","userId":"04072544495695427838"}},"outputId":"520d0cb1-e411-4912-d850-51c80c69c8d1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 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task_type=\"CAUSAL_LM\",\n",")"],"metadata":{"id":"Jaq75DLNMj8E"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["training_params = TrainingArguments(\n","    output_dir=\"./results\",\n","    num_train_epochs=10,\n","    per_device_train_batch_size=4,\n","    gradient_accumulation_steps=1,\n","    optim=\"paged_adamw_32bit\",\n","    save_steps=25,\n","    logging_steps=25,\n","    learning_rate=2e-4,\n","    weight_decay=0.001,\n","    fp16=False,\n","    bf16=False,\n","    max_grad_norm=0.3,\n","    max_steps=-1,\n","    warmup_ratio=0.03,\n","    group_by_length=True,\n","    lr_scheduler_type=\"constant\",\n","    report_to=\"tensorboard\"\n",")"],"metadata":{"id":"SGENoi0jMk-h"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# 파인튜닝\n","trainer = SFTTrainer(\n","    model=model,\n","    train_dataset=dataset,\n","    peft_config=peft_params,\n","    dataset_text_field=\"text\",\n","    max_seq_length=None,\n","    tokenizer=tokenizer,\n","    args=training_params,\n","    packing=False,\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":106,"referenced_widgets":["eec128dbe8f9407cb6bc6e3d4fcc4cfe","34a977eaa93e4fb4908c5a09a0c86b07","f4fb0186de854acbb85e9d02b0b17cf0","dd63aae2519643319d7441d56526534f","045ffbc694384302a88ec12bd39b75eb","5cbf50578ebb442fa3044dd6c1dfbbd3","fa99d0d6f9fd49b892d32fc769a57834","df81b66a069d4b3fa0e95758f60855a5","caad60063c8247c9bf12cb8b8237932c","ea3f2fc63ec74cbabb0202d259f3b318","db3a1157c9104176bc5d218711b4c92a"]},"id":"LeKEE2FxMnb_","executionInfo":{"status":"ok","timestamp":1724535252328,"user_tz":240,"elapsed":1277,"user":{"displayName":"박병건","userId":"04072544495695427838"}},"outputId":"0e4bd097-bd76-4dcc-c3cc-d713a250a241"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:246: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1939 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eec128dbe8f9407cb6bc6e3d4fcc4cfe"}},"metadata":{}}]},{"cell_type":"code","source":["trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"SK-rlOtqMpPX","executionInfo":{"status":"ok","timestamp":1724537259475,"user_tz":240,"elapsed":2004558,"user":{"displayName":"박병건","userId":"04072544495695427838"}},"outputId":"5a463b43-a21b-4afa-dc1e-327b0f320e89"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='4850' max='4850' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [4850/4850 33:22, Epoch 10/10]\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","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>25</td>\n","      <td>4.187600</td>\n","    </tr>\n","    <tr>\n","      <td>50</td>\n","      <td>4.885100</td>\n","    </tr>\n","    <tr>\n","      <td>75</td>\n","      <td>3.378800</td>\n","    </tr>\n","    <tr>\n","      <td>100</td>\n","      <td>4.115100</td>\n","    </tr>\n","    <tr>\n","      <td>125</td>\n","      <td>3.271300</td>\n","    </tr>\n","    <tr>\n","      <td>150</td>\n","      <td>4.174400</td>\n","    </tr>\n","    <tr>\n","      <td>175</td>\n","      <td>3.108000</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>3.791200</td>\n","    </tr>\n","    <tr>\n","      <td>225</td>\n","      <td>3.051700</td>\n","    </tr>\n","    <tr>\n","      <td>250</td>\n","      <td>3.847300</td>\n","    </tr>\n","    <tr>\n","      <td>275</td>\n","      <td>3.178000</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>4.004200</td>\n","    </tr>\n","    <tr>\n","      <td>325</td>\n","      <td>3.164000</td>\n","    </tr>\n","    <tr>\n","      <td>350</td>\n","      <td>3.942000</td>\n","    </tr>\n","    <tr>\n","      <td>375</td>\n","      <td>3.034500</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>3.320000</td>\n","    </tr>\n","    <tr>\n","      <td>425</td>\n","      <td>2.993800</td>\n","    </tr>\n","    <tr>\n","      <td>450</td>\n","      <td>3.907300</td>\n","    </tr>\n","    <tr>\n","      <td>475</td>\n","      <td>3.103700</td>\n","    </tr>\n","    <tr>\n","      <td>500</td>\n","      <td>3.040200</td>\n","    </tr>\n","    <tr>\n","      <td>525</td>\n","      <td>3.204200</td>\n","    </tr>\n","    <tr>\n","      <td>550</td>\n","      <td>3.039300</td>\n","    </tr>\n","    <tr>\n","      <td>575</td>\n","      <td>3.305300</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>2.990500</td>\n","    </tr>\n","    <tr>\n","      <td>625</td>\n","      <td>3.131400</td>\n","    </tr>\n","    <tr>\n","      <td>650</td>\n","      <td>3.021100</td>\n","    </tr>\n","    <tr>\n","      <td>675</td>\n","      <td>3.190300</td>\n","    </tr>\n","    <tr>\n","      <td>700</td>\n","      <td>2.899500</td>\n","    </tr>\n","    <tr>\n","      <td>725</td>\n","      <td>3.088100</td>\n","    </tr>\n","    <tr>\n","      <td>750</td>\n","      <td>2.892900</td>\n","    </tr>\n","    <tr>\n","      <td>775</td>\n","      <td>3.058100</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>3.158500</td>\n","    </tr>\n","    <tr>\n","      <td>825</td>\n","      <td>2.883900</td>\n","    </tr>\n","    <tr>\n","      <td>850</td>\n","      <td>3.172000</td>\n","    </tr>\n","    <tr>\n","      <td>875</td>\n","      <td>3.143900</td>\n","    </tr>\n","    <tr>\n","      <td>900</td>\n","      <td>3.044700</td>\n","    </tr>\n","    <tr>\n","      <td>925</td>\n","      <td>2.998200</td>\n","    </tr>\n","    <tr>\n","      <td>950</td>\n","      <td>3.015300</td>\n","    </tr>\n","    <tr>\n","      <td>975</td>\n","      <td>2.972000</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>2.619400</td>\n","    </tr>\n","    <tr>\n","      <td>1025</td>\n","      <td>2.590900</td>\n","    </tr>\n","    <tr>\n","      <td>1050</td>\n","      <td>2.696100</td>\n","    </tr>\n","    <tr>\n","      <td>1075</td>\n","      <td>3.062400</td>\n","    </tr>\n","    <tr>\n","      <td>1100</td>\n","      <td>2.557600</td>\n","    </tr>\n","    <tr>\n","      <td>1125</td>\n","      <td>2.787500</td>\n","    </tr>\n","    <tr>\n","      <td>1150</td>\n","      <td>2.674100</td>\n","    </tr>\n","    <tr>\n","      <td>1175</td>\n","      <td>2.670600</td>\n","    </tr>\n","    <tr>\n","      <td>1200</td>\n","      <td>2.632400</td>\n","    </tr>\n","    <tr>\n","      <td>1225</td>\n","      <td>2.744600</td>\n","    </tr>\n","    <tr>\n","      <td>1250</td>\n","      <td>2.770100</td>\n","    </tr>\n","    <tr>\n","      <td>1275</td>\n","      <td>2.801900</td>\n","    </tr>\n","    <tr>\n","      <td>1300</td>\n","      <td>2.677400</td>\n","    </tr>\n","    <tr>\n","      <td>1325</td>\n","      <td>2.712200</td>\n","    </tr>\n","    <tr>\n","      <td>1350</td>\n","      <td>2.601200</td>\n","    </tr>\n","    <tr>\n","      <td>1375</td>\n","      <td>3.169300</td>\n","    </tr>\n","    <tr>\n","      <td>1400</td>\n","      <td>2.752400</td>\n","    </tr>\n","    <tr>\n","      <td>1425</td>\n","      <td>2.860900</td>\n","    </tr>\n","    <tr>\n","      <td>1450</td>\n","      <td>2.627500</td>\n","    </tr>\n","    <tr>\n","      <td>1475</td>\n","      <td>2.399600</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>2.103500</td>\n","    </tr>\n","    <tr>\n","      <td>1525</td>\n","      <td>2.259600</td>\n","    </tr>\n","    <tr>\n","      <td>1550</td>\n","      <td>2.354400</td>\n","    </tr>\n","    <tr>\n","      <td>1575</td>\n","      <td>2.264300</td>\n","    </tr>\n","    <tr>\n","      <td>1600</td>\n","      <td>2.335400</td>\n","    </tr>\n","    <tr>\n","      <td>1625</td>\n","      <td>2.524000</td>\n","    </tr>\n","    <tr>\n","      <td>1650</td>\n","      <td>2.299500</td>\n","    </tr>\n","    <tr>\n","      <td>1675</td>\n","      <td>2.665900</td>\n","    </tr>\n","    <tr>\n","      <td>1700</td>\n","      <td>2.386400</td>\n","    </tr>\n","    <tr>\n","      <td>1725</td>\n","      <td>2.305800</td>\n","    </tr>\n","    <tr>\n","      <td>1750</td>\n","      <td>2.289500</td>\n","    </tr>\n","    <tr>\n","      <td>1775</td>\n","      <td>2.525500</td>\n","    </tr>\n","    <tr>\n","      <td>1800</td>\n","      <td>2.279900</td>\n","    </tr>\n","    <tr>\n","      <td>1825</td>\n","      <td>2.323800</td>\n","    </tr>\n","    <tr>\n","      <td>1850</td>\n","      <td>2.503400</td>\n","    </tr>\n","    <tr>\n","      <td>1875</td>\n","      <td>2.581700</td>\n","    </tr>\n","    <tr>\n","      <td>1900</td>\n","      <td>2.298900</td>\n","    </tr>\n","    <tr>\n","      <td>1925</td>\n","      <td>2.577200</td>\n","    </tr>\n","    <tr>\n","      <td>1950</td>\n","      <td>2.229700</td>\n","    </tr>\n","    <tr>\n","      <td>1975</td>\n","      <td>1.783900</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>2.016000</td>\n","    </tr>\n","    <tr>\n","      <td>2025</td>\n","      <td>1.952600</td>\n","    </tr>\n","    <tr>\n","      <td>2050</td>\n","      <td>2.077100</td>\n","    </tr>\n","    <tr>\n","      <td>2075</td>\n","      <td>2.022100</td>\n","    </tr>\n","    <tr>\n","      <td>2100</td>\n","      <td>1.961000</td>\n","    </tr>\n","    <tr>\n","      <td>2125</td>\n","      <td>2.043300</td>\n","    </tr>\n","    <tr>\n","      <td>2150</td>\n","      <td>2.279000</td>\n","    </tr>\n","    <tr>\n","      <td>2175</td>\n","      <td>1.980800</td>\n","    </tr>\n","    <tr>\n","      <td>2200</td>\n","      <td>2.019300</td>\n","    </tr>\n","    <tr>\n","      <td>2225</td>\n","      <td>1.957700</td>\n","    </tr>\n","    <tr>\n","      <td>2250</td>\n","      <td>1.980200</td>\n","    </tr>\n","    <tr>\n","      <td>2275</td>\n","      <td>2.053300</td>\n","    </tr>\n","    <tr>\n","      <td>2300</td>\n","      <td>2.049400</td>\n","    </tr>\n","    <tr>\n","      <td>2325</td>\n","      <td>1.948500</td>\n","    </tr>\n","    <tr>\n","      <td>2350</td>\n","      <td>2.232200</td>\n","    </tr>\n","    <tr>\n","      <td>2375</td>\n","      <td>2.062400</td>\n","    </tr>\n","    <tr>\n","      <td>2400</td>\n","      <td>2.283100</td>\n","    </tr>\n","    <tr>\n","      <td>2425</td>\n","      <td>2.342700</td>\n","    </tr>\n","    <tr>\n","      <td>2450</td>\n","      <td>1.505600</td>\n","    </tr>\n","    <tr>\n","      <td>2475</td>\n","      <td>1.615700</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>1.492600</td>\n","    </tr>\n","    <tr>\n","      <td>2525</td>\n","      <td>1.941200</td>\n","    </tr>\n","    <tr>\n","      <td>2550</td>\n","      <td>1.585400</td>\n","    </tr>\n","    <tr>\n","      <td>2575</td>\n","      <td>1.732900</td>\n","    </tr>\n","    <tr>\n","      <td>2600</td>\n","      <td>1.563400</td>\n","    </tr>\n","    <tr>\n","      <td>2625</td>\n","      <td>2.158900</td>\n","    </tr>\n","    <tr>\n","      <td>2650</td>\n","      <td>1.590500</td>\n","    </tr>\n","    <tr>\n","      <td>2675</td>\n","      <td>1.775300</td>\n","    </tr>\n","    <tr>\n","      <td>2700</td>\n","      <td>1.592100</td>\n","    </tr>\n","    <tr>\n","      <td>2725</td>\n","      <td>1.839500</td>\n","    </tr>\n","    <tr>\n","      <td>2750</td>\n","      <td>1.584500</td>\n","    </tr>\n","    <tr>\n","      <td>2775</td>\n","      <td>2.147800</td>\n","    </tr>\n","    <tr>\n","      <td>2800</td>\n","      <td>1.567400</td>\n","    </tr>\n","    <tr>\n","      <td>2825</td>\n","      <td>1.939000</td>\n","    </tr>\n","    <tr>\n","      <td>2850</td>\n","      <td>1.641400</td>\n","    </tr>\n","    <tr>\n","      <td>2875</td>\n","      <td>1.895600</td>\n","    </tr>\n","    <tr>\n","      <td>2900</td>\n","      <td>1.720300</td>\n","    </tr>\n","    <tr>\n","      <td>2925</td>\n","      <td>1.647200</td>\n","    </tr>\n","    <tr>\n","      <td>2950</td>\n","      <td>1.272900</td>\n","    </tr>\n","    <tr>\n","      <td>2975</td>\n","      <td>1.467200</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>1.347900</td>\n","    </tr>\n","    <tr>\n","      <td>3025</td>\n","      <td>1.596800</td>\n","    </tr>\n","    <tr>\n","      <td>3050</td>\n","      <td>1.284800</td>\n","    </tr>\n","    <tr>\n","      <td>3075</td>\n","      <td>1.570800</td>\n","    </tr>\n","    <tr>\n","      <td>3100</td>\n","      <td>1.325800</td>\n","    </tr>\n","    <tr>\n","      <td>3125</td>\n","      <td>1.691200</td>\n","    </tr>\n","    <tr>\n","      <td>3150</td>\n","      <td>1.349800</td>\n","    </tr>\n","    <tr>\n","      <td>3175</td>\n","      <td>1.652100</td>\n","    </tr>\n","    <tr>\n","      <td>3200</td>\n","      <td>1.304800</td>\n","    </tr>\n","    <tr>\n","      <td>3225</td>\n","      <td>1.481900</td>\n","    </tr>\n","    <tr>\n","      <td>3250</td>\n","      <td>1.424100</td>\n","    </tr>\n","    <tr>\n","      <td>3275</td>\n","      <td>1.901800</td>\n","    </tr>\n","    <tr>\n","      <td>3300</td>\n","      <td>1.367700</td>\n","    </tr>\n","    <tr>\n","      <td>3325</td>\n","      <td>1.779000</td>\n","    </tr>\n","    <tr>\n","      <td>3350</td>\n","      <td>1.399200</td>\n","    </tr>\n","    <tr>\n","      <td>3375</td>\n","      <td>1.667700</td>\n","    </tr>\n","    <tr>\n","      <td>3400</td>\n","      <td>1.566400</td>\n","    </tr>\n","    <tr>\n","      <td>3425</td>\n","      <td>1.070900</td>\n","    </tr>\n","    <tr>\n","      <td>3450</td>\n","      <td>1.439300</td>\n","    </tr>\n","    <tr>\n","      <td>3475</td>\n","      <td>1.058800</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>1.301900</td>\n","    </tr>\n","    <tr>\n","      <td>3525</td>\n","      <td>1.112700</td>\n","    </tr>\n","    <tr>\n","      <td>3550</td>\n","      <td>1.386100</td>\n","    </tr>\n","    <tr>\n","      <td>3575</td>\n","      <td>1.167300</td>\n","    </tr>\n","    <tr>\n","      <td>3600</td>\n","      <td>1.463700</td>\n","    </tr>\n","    <tr>\n","      <td>3625</td>\n","      <td>1.172900</td>\n","    </tr>\n","    <tr>\n","      <td>3650</td>\n","      <td>1.346400</td>\n","    </tr>\n","    <tr>\n","      <td>3675</td>\n","      <td>1.117000</td>\n","    </tr>\n","    <tr>\n","      <td>3700</td>\n","      <td>1.580100</td>\n","    </tr>\n","    <tr>\n","      <td>3725</td>\n","      <td>1.173400</td>\n","    </tr>\n","    <tr>\n","      <td>3750</td>\n","      <td>1.497400</td>\n","    </tr>\n","    <tr>\n","      <td>3775</td>\n","      <td>1.196600</td>\n","    </tr>\n","    <tr>\n","      <td>3800</td>\n","      <td>1.665400</td>\n","    </tr>\n","    <tr>\n","      <td>3825</td>\n","      <td>1.163500</td>\n","    </tr>\n","    <tr>\n","      <td>3850</td>\n","      <td>1.670500</td>\n","    </tr>\n","    <tr>\n","      <td>3875</td>\n","      <td>1.216000</td>\n","    </tr>\n","    <tr>\n","      <td>3900</td>\n","      <td>1.374400</td>\n","    </tr>\n","    <tr>\n","      <td>3925</td>\n","      <td>1.055500</td>\n","    </tr>\n","    <tr>\n","      <td>3950</td>\n","      <td>1.244000</td>\n","    </tr>\n","    <tr>\n","      <td>3975</td>\n","      <td>1.018100</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>1.296200</td>\n","    </tr>\n","    <tr>\n","      <td>4025</td>\n","      <td>1.064100</td>\n","    </tr>\n","    <tr>\n","      <td>4050</td>\n","      <td>1.095200</td>\n","    </tr>\n","    <tr>\n","      <td>4075</td>\n","      <td>1.020200</td>\n","    </tr>\n","    <tr>\n","      <td>4100</td>\n","      <td>1.396000</td>\n","    </tr>\n","    <tr>\n","      <td>4125</td>\n","      <td>1.096200</td>\n","    </tr>\n","    <tr>\n","      <td>4150</td>\n","      <td>1.479900</td>\n","    </tr>\n","    <tr>\n","      <td>4175</td>\n","      <td>1.002500</td>\n","    </tr>\n","    <tr>\n","      <td>4200</td>\n","      <td>1.274500</td>\n","    </tr>\n","    <tr>\n","      <td>4225</td>\n","      <td>1.091700</td>\n","    </tr>\n","    <tr>\n","      <td>4250</td>\n","      <td>1.537600</td>\n","    </tr>\n","    <tr>\n","      <td>4275</td>\n","      <td>1.111400</td>\n","    </tr>\n","    <tr>\n","      <td>4300</td>\n","      <td>1.538300</td>\n","    </tr>\n","    <tr>\n","      <td>4325</td>\n","      <td>1.075300</td>\n","    </tr>\n","    <tr>\n","      <td>4350</td>\n","      <td>1.344300</td>\n","    </tr>\n","    <tr>\n","      <td>4375</td>\n","      <td>1.251100</td>\n","    </tr>\n","    <tr>\n","      <td>4400</td>\n","      <td>0.836100</td>\n","    </tr>\n","    <tr>\n","      <td>4425</td>\n","      <td>1.206800</td>\n","    </tr>\n","    <tr>\n","      <td>4450</td>\n","      <td>0.851700</td>\n","    </tr>\n","    <tr>\n","      <td>4475</td>\n","      <td>1.503300</td>\n","    </tr>\n","    <tr>\n","      <td>4500</td>\n","      <td>0.901300</td>\n","    </tr>\n","    <tr>\n","      <td>4525</td>\n","      <td>1.104700</td>\n","    </tr>\n","    <tr>\n","      <td>4550</td>\n","      <td>0.856800</td>\n","    </tr>\n","    <tr>\n","      <td>4575</td>\n","      <td>1.432700</td>\n","    </tr>\n","    <tr>\n","      <td>4600</td>\n","      <td>0.893200</td>\n","    </tr>\n","    <tr>\n","      <td>4625</td>\n","      <td>1.339500</td>\n","    </tr>\n","    <tr>\n","      <td>4650</td>\n","      <td>0.877500</td>\n","    </tr>\n","    <tr>\n","      <td>4675</td>\n","      <td>1.277000</td>\n","    </tr>\n","    <tr>\n","      <td>4700</td>\n","      <td>0.938400</td>\n","    </tr>\n","    <tr>\n","      <td>4725</td>\n","      <td>1.390100</td>\n","    </tr>\n","    <tr>\n","      <td>4750</td>\n","      <td>0.895300</td>\n","    </tr>\n","    <tr>\n","      <td>4775</td>\n","      <td>1.449400</td>\n","    </tr>\n","    <tr>\n","      <td>4800</td>\n","      <td>0.936600</td>\n","    </tr>\n","    <tr>\n","      <td>4825</td>\n","      <td>1.407100</td>\n","    </tr>\n","    <tr>\n","      <td>4850</td>\n","      <td>1.189400</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=4850, training_loss=2.0706684887286313, metrics={'train_runtime': 2004.9585, 'train_samples_per_second': 9.671, 'train_steps_per_second': 2.419, 'total_flos': 1.6260667467005952e+16, 'train_loss': 2.0706684887286313, 'epoch': 10.0})"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["logging.set_verbosity(logging.CRITICAL)\n","\n","prompt = \"삼성전자의 5월 17일 증권 현황은 어떤가요?\"\n","pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n","result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n","print(result[0]['generated_text'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g7u85tHzMrEl","executionInfo":{"status":"ok","timestamp":1724538777661,"user_tz":240,"elapsed":13995,"user":{"displayName":"박병건","userId":"04072544495695427838"}},"outputId":"d8a44859-7987-4f10-9039-f54a43a1e58d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<s>[INST] 삼성전자의 5월 17일 증권 현황은 어떤가요? [/INST] [/s] [/a] [/t] [/d] [/p] [/m] [/x] [/u] [/f] [/v] [/r] [/i] [/c] [/e] [/a] [/t] [/u] [/f] [/v] [/r] [/i] [/c] [/e] [/a] [/t] [/u] [/f] [/v] [/r] [/i] [/c] [/e] [/a] [/t] [/u] [/f] [/v] [/r] [/i] [/c] [/e] [/a] [/t] [/u] [/f] [/v] [/r] [/i] [/c] [/e] [/a] [/t] [/u] [/f] [/v] [/r] [/i] [/c] [/\n"]}]},{"cell_type":"code","source":["logging.set_verbosity(logging.CRITICAL)\n","\n","prompt = \"Just answer this question: Tell me about the role of Maui Emergency Management Agency (MEMA) in the 2023 wildfires.\"\n","pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n","result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n","print(result[0]['generated_text'])"],"metadata":{"id":"Ck5BHGG9Pwbv","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1724538795126,"user_tz":240,"elapsed":12654,"user":{"displayName":"박병건","userId":"04072544495695427838"}},"outputId":"838a87e0-77d3-440a-92a0-aee159e56bb8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<s>[INST] Just answer this question: Tell me about the role of Maui Emergency Management Agency (MEMA) in the 2023 wildfires. [/INST]A Maui resident shared that MEMA played a key role in coordinating government response to the 2023 Lahaina fires. They worked with other agencies to establish a joint information services (JIS) to share information and coordinate responses. They helped establish a joint emergency response system to coordinate responses to the fires. They also helped establish a joint system for sharing information on the fires, including sharing data on the number of cases and the types of cases. The resident emphasized that MEMA played a key role in coordinating the response to the fires, including establishing a joint system for sharing information on the fires and the types of cases. The resident noted that MEMA helped establish a joint system for sharing information on the fires on social media, including sharing data on the number of cases and the types of cases. The resident emphasized that MEMA played\n"]}]},{"cell_type":"code","source":["# ### ENTER YOUR QUESTION BELOW\n","\n","# question = \"Just answer this question: Tell me about the role of Maui Emergency Management Agency (MEMA) in the 2023 wildfires??\"\n","\n","# # Format the question\n","# eval_prompt = f\"{question}\\n\\n\"\n","\n","# promptTokenized = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n","\n","# modelFinetuned.eval()\n","# with torch.no_grad():\n","#     print(tokenizer.decode(modelFinetuned.generate(**promptTokenized, max_new_tokens = 1024)[0], skip_special_tokens=True))\n","# torch.cuda.empty_cache()"],"metadata":{"id":"Gzdp_d0nUSUm"},"execution_count":null,"outputs":[]}]}