3_AutoTrain_LLM_colab_231122.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1QXqPVg2P7Im9A7vJsBQpzBqsRsYbzXPD?usp=sharing)"
      ],
      "metadata": {
        "id": "-g5npBe68eSB"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uvh6pvko5Oas"
      },
      "source": [
        "# Autotrain LLAMA2 fine-tuning\n",
        "\n",
        "231122\n",
        "\n",
        "- [ref](https://youtu.be/GjZ1a0OJqGk?feature=shared)\n",
        "\n",
        "- [autotrain](https://github.com/huggingface/autotrain-advanced)을 사용하는 경우 [link](https://github.com/huggingface/autotrain-advanced/blob/main/src/autotrain/trainers/clm/__main__.py) 코드 변형해서 쓰면 된다\n",
        "\n",
        "Params: Namespace(version=False, train=True, deploy=False, inference=False, data_path='royboy0416/ko-alpaca', train_split='train', valid_split=None, text_column='text', model='abhishek/llama-2-7b-hf-small-shards', learning_rate=0.0002, num_train_epochs=2, train_batch_size=2, warmup_ratio=0.1, gradient_accumulation_steps=4, optimizer='adamw_torch', scheduler='linear', weight_decay=0.01, max_grad_norm=1.0, seed=42, add_eos_token=False, block_size=1024, use_peft=True, lora_r=16, lora_alpha=32, lora_dropout=0.05, logging_steps=-1, project_name='my_autotrain_llm', evaluation_strategy='epoch', save_total_limit=1, save_strategy='epoch', auto_find_batch_size=False, fp16=True, push_to_hub=True, use_int8=False, model_max_length=1024, repo_id='keapsteady/autotrain_test', use_int4=True, trainer='default', target_modules=None, merge_adapter=False, token='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', backend='default', username=None, use_flash_attention_2=False, func=<function run_llm_command_factory at 0x7fc860cef760>)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 에러없는 과거버전 autotrain으로 설치\n",
        "!git clone https://github.com/airobotlab/KoChatGPT.git\n",
        "%cd ./KoChatGPT\n",
        "!unzip autotrain-advanced_231012 -d ./autotrain-advanced_231012\n",
        "%cd ./autotrain-advanced_231012\n",
        "!pip install -e ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qBN-x20G30aY",
        "outputId": "4ebeaf9d-cd34-48b3-c092-82906e9f8b90"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'KoChatGPT'...\n",
            "remote: Enumerating objects: 269, done.\u001b[K\n",
            "remote: Counting objects: 100% (100/100), done.\u001b[K\n",
            "remote: Compressing objects: 100% (47/47), done.\u001b[K\n",
            "remote: Total 269 (delta 67), reused 53 (delta 53), pack-reused 169\u001b[K\n",
            "Receiving objects: 100% (269/269), 48.80 MiB | 26.67 MiB/s, done.\n",
            "Resolving deltas: 100% (103/103), done.\n",
            "/content/KoChatGPT\n",
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            "  Downloading typing_extensions-4.8.0-py3-none-any.whl (31 kB)\n",
            "Collecting httpcore (from httpx->gradio==3.41.0->autotrain-advanced==0.6.36.dev0)\n",
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            "\u001b[?25hRequirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx->gradio==3.41.0->autotrain-advanced==0.6.36.dev0) (1.3.0)\n",
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            "Building wheels for collected packages: ipadic, sacremoses, ffmpy\n",
            "  Building wheel for ipadic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ipadic: filename=ipadic-1.0.0-py3-none-any.whl size=13556704 sha256=b9cd6c96c78a3fb00a90f4de02a58209600d9771d22b57482f7ec885d32d011e\n",
            "  Stored in directory: /root/.cache/pip/wheels/5b/ea/e3/2f6e0860a327daba3b030853fce4483ed37468bbf1101c59c3\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895241 sha256=cebb483771793f3d8559bdf3c5b94751bdb128c1fe7f19096057645508bacdf3\n",
            "  Stored in directory: /root/.cache/pip/wheels/00/24/97/a2ea5324f36bc626e1ea0267f33db6aa80d157ee977e9e42fb\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=35663e2e231c2a2561e95e340a3e729cceef4fbb4266fa13dfae02b5e6624b6b\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
            "Successfully built ipadic sacremoses ffmpy\n",
            "Installing collected packages: types-python-dateutil, sentencepiece, pydub, ipadic, fuzzywuzzy, ffmpy, bitsandbytes, werkzeug, websockets, typing-extensions, tqdm, shtab, semantic-version, rapidfuzz, python-multipart, pynvml, pyarrow-hotfix, protobuf, Pillow, packaging, orjson, Mako, loguru, joblib, h11, einops, docstring-parser, dill, colorlog, cmaes, aiofiles, xgboost, uvicorn, tiktoken, starlette, scikit-learn, sacremoses, responses, pydantic, multiprocess, jiwer, httpcore, arrow, tyro, invisible-watermark, httpx, fastapi, diffusers, codecarbon, alembic, accelerate, optuna, gradio-client, datasets, trl, peft, gradio, evaluate, autotrain-advanced\n",
            "  Attempting uninstall: werkzeug\n",
            "    Found existing installation: Werkzeug 3.0.1\n",
            "    Uninstalling Werkzeug-3.0.1:\n",
            "      Successfully uninstalled Werkzeug-3.0.1\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: tqdm\n",
            "    Found existing installation: tqdm 4.66.1\n",
            "    Uninstalling tqdm-4.66.1:\n",
            "      Successfully uninstalled tqdm-4.66.1\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.20.3\n",
            "    Uninstalling protobuf-3.20.3:\n",
            "      Successfully uninstalled protobuf-3.20.3\n",
            "  Attempting uninstall: Pillow\n",
            "    Found existing installation: Pillow 9.4.0\n",
            "    Uninstalling Pillow-9.4.0:\n",
            "      Successfully uninstalled Pillow-9.4.0\n",
            "  Attempting uninstall: packaging\n",
            "    Found existing installation: packaging 23.2\n",
            "    Uninstalling packaging-23.2:\n",
            "      Successfully uninstalled packaging-23.2\n",
            "  Attempting uninstall: joblib\n",
            "    Found existing installation: joblib 1.3.2\n",
            "    Uninstalling joblib-1.3.2:\n",
            "      Successfully uninstalled joblib-1.3.2\n",
            "  Attempting uninstall: xgboost\n",
            "    Found existing installation: xgboost 2.0.2\n",
            "    Uninstalling xgboost-2.0.2:\n",
            "      Successfully uninstalled xgboost-2.0.2\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.2.2\n",
            "    Uninstalling scikit-learn-1.2.2:\n",
            "      Successfully uninstalled scikit-learn-1.2.2\n",
            "  Attempting uninstall: pydantic\n",
            "    Found existing installation: pydantic 1.10.13\n",
            "    Uninstalling pydantic-1.10.13:\n",
            "      Successfully uninstalled pydantic-1.10.13\n",
            "  Running setup.py develop for autotrain-advanced\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",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\n",
            "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 4.23.4 which is incompatible.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Mako-1.3.0 Pillow-10.0.0 accelerate-0.24.1 aiofiles-23.2.1 alembic-1.12.1 arrow-1.3.0 autotrain-advanced-0.6.36.dev0 bitsandbytes-0.41.2.post2 cmaes-0.10.0 codecarbon-2.2.3 colorlog-6.7.0 datasets-2.14.7 diffusers-0.23.1 dill-0.3.7 docstring-parser-0.15 einops-0.6.1 evaluate-0.3.0 fastapi-0.104.1 ffmpy-0.3.1 fuzzywuzzy-0.18.0 gradio-3.41.0 gradio-client-0.5.0 h11-0.14.0 httpcore-1.0.2 httpx-0.25.1 invisible-watermark-0.2.0 ipadic-1.0.0 jiwer-3.0.2 joblib-1.3.1 loguru-0.7.0 multiprocess-0.70.15 optuna-3.3.0 orjson-3.9.10 packaging-23.1 peft-0.6.2 protobuf-4.23.4 pyarrow-hotfix-0.5 pydantic-1.10.11 pydub-0.25.1 pynvml-11.5.0 python-multipart-0.0.6 rapidfuzz-2.13.7 responses-0.18.0 sacremoses-0.0.53 scikit-learn-1.3.0 semantic-version-2.10.0 sentencepiece-0.1.99 shtab-1.6.4 starlette-0.27.0 tiktoken-0.5.1 tqdm-4.65.0 trl-0.7.4 types-python-dateutil-2.8.19.14 typing-extensions-4.8.0 tyro-0.5.17 uvicorn-0.24.0.post1 websockets-11.0.3 werkzeug-2.3.6 xgboost-1.7.6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "EEQPUrmu3LUh"
      },
      "outputs": [],
      "source": [
        "# # 작은모델로 autotrain 체크하는 코드\n",
        "# autotrain llm --train --model \"abhishek/llama-2-7b-hf-small-shards\" --project_name \"my_autotrain_llm\" --data_path \"aboonaji/alpaca_micro_demo\" --text_column \"text\" --learning_rate 0.0002 --train_batch_size 2 --num_train_epochs 2 --block_size 1024 --warmup_ratio 0.1 --lora_r 16 --lora_alpha 32 --lora_dropout 0.05 --weight_decay 0.01 --gradient_accumulation_steps 4  --fp16 --use_peft --use_int4 --push-to-hub --token \"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\" --repo-id \"keepsteady/autotrain_test\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FOGbcWeQ3LUh",
        "outputId": "46367621-e5f4-4196-b42e-fc5b33710ab5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
            "### Instruction: Identify the odd one out.\n",
            "### Input: Twitter, Instagram, Telegram\n",
            "### Response: Telegram\n"
          ]
        }
      ],
      "source": [
        "# data\n",
        "description = 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.'\n",
        "지시 = 'Identify the odd one out.'\n",
        "입력 = 'Twitter, Instagram, Telegram'\n",
        "출력 = 'Telegram'\n",
        "\n",
        "text = f'{description}\\n### Instruction: {지시}\\n### Input: {입력}\\n### Response: {출력}'\n",
        "print(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mEBsenlr3LUi",
        "outputId": "52401bcb-f9dd-4134-8d3b-355b2705ac8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'text': 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n### Instruction: Identify the odd one out.\\n### Input: Twitter, Instagram, Telegram\\n### Response: Telegram'},\n",
              " {'text': 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n### Instruction: Identify the odd one out.\\n### Input: Twitter, Instagram, Telegram\\n### Response: Telegram'},\n",
              " {'text': 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n### Instruction: Identify the odd one out.\\n### Input: Twitter, Instagram, Telegram\\n### Response: Telegram'}]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "custom_data = [\n",
        "    {\"text\": text},\n",
        "    {\"text\": text},\n",
        "    {\"text\": text}\n",
        "]\n",
        "\n",
        "custom_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PE06uCs33LUi",
        "outputId": "99c9e5b7-eefb-4a97-853d-d061a53438fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
            "### Instruction: Identify the odd one out.\n",
            "### Input: Twitter, Instagram, Telegram\n",
            "### Response: Telegram\n"
          ]
        }
      ],
      "source": [
        "# data\n",
        "description = 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.'\n",
        "\n",
        "\n",
        "지시 = 'Identify the odd one out.'\n",
        "입력 = 'Twitter, Instagram, Telegram'\n",
        "출력 = 'Telegram'\n",
        "\n",
        "text = f'{description}\\n### Instruction: {지시}\\n### Input: {입력}\\n### Response: {출력}'\n",
        "print(text)\n",
        "\n",
        "\n",
        "\n",
        "custom_data = [\n",
        "    {\"text\": \"### Human: Can you write a short introduction about the relevance of the term \\\"monopsony\\\" in economics? Please use examples related to\"},\n",
        "    {\"text\": \"### Human: Can you write a short introduction about the relevance of the term \\\"monopsony\\\" in economics? Please use examples related to\"},\n",
        "    {\"text\": \"### Human: Can you write a short introduction about the relevance of the term \\\"monopsony\\\" in economics? Please use examples related to\"}\n",
        "]\n",
        "\n",
        "# 위에서 설정한 학습 데이터를 파일로 저장합니다.\n",
        "import json\n",
        "with open(\"custom_data.jsonl\", \"w\", encoding='utf-8') as json_file:\n",
        "    json.dump(custom_data, json_file, indent=4, ensure_ascii=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rSeDGNNJ3LUi",
        "outputId": "30a753ef-e8f0-43b9-f5e9-f708a8bd054e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "autotrain llm --train --project_name test                     --model TinyPixel/Llama-2-7B-bf16-sharded                     --data_path timdettmers/openassistant-guanaco                     --text_column \"text\" \\                    \n",
            "                    --use_peft                     --use_int4                     --learning_rate 2e-4                     --train_batch_size 2                     --num_train_epochs 2                     --trainer sft                     --model_max_length 2048                     --push_to_hub                     --repo_id keepsteady/test                     --block_size 2048\n"
          ]
        }
      ],
      "source": [
        "# single line command!\n",
        "# \"quantumaikr/KoreanLM-llama-2-7B-finetuned\"  # 한국어\n",
        "# \"TinyPixel/Llama-2-7B-bf16-sharded\"  # 영어\n",
        "\n",
        "project_name='test'\n",
        "model = 'TinyPixel/Llama-2-7B-bf16-sharded'\n",
        "data_path = 'timdettmers/openassistant-guanaco'\n",
        "# data_path = 'tatsu-lab/alpaca'\n",
        "\n",
        "train_batch_size = 2\n",
        "num_train_epochs = 2\n",
        "model_max_length = 2048\n",
        "repo_id = 'keepsteady/test'\n",
        "\n",
        "cmd = f'''autotrain llm --train --project_name {project_name} \\\n",
        "                    --model {model} \\\n",
        "                    --data_path {data_path} \\\n",
        "                    --text_column \\\"text\\\" \\\n",
        "                    --use_peft \\\n",
        "                    --use_int4 \\\n",
        "                    --learning_rate 2e-4 \\\n",
        "                    --train_batch_size {train_batch_size} \\\n",
        "                    --num_train_epochs {num_train_epochs} \\\n",
        "                    --trainer sft \\\n",
        "                    --model_max_length {model_max_length} \\\n",
        "                    --push_to_hub \\\n",
        "                    --repo_id {repo_id} \\\n",
        "                    --block_size 2048'''\n",
        "\n",
        "print(cmd)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7dPE3tY3LUj",
        "outputId": "a8fc1a82-dc06-42d9-88ab-a3cad122cb60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.\n",
            "  warnings.warn(\n",
            "> \u001b[1mINFO    Running LLM\u001b[0m\n",
            "> \u001b[1mINFO    Params: Namespace(version=False, train=True, deploy=False, inference=False, data_path='timdettmers/openassistant-guanaco', train_split='train', valid_split=None, text_column='text', model='TinyPixel/Llama-2-7B-bf16-sharded', learning_rate=0.0002, num_train_epochs=2, train_batch_size=2, warmup_ratio=0.1, gradient_accumulation_steps=1, optimizer='adamw_torch', scheduler='linear', weight_decay=0.0, max_grad_norm=1.0, seed=42, add_eos_token=False, block_size=2048, use_peft=True, lora_r=16, lora_alpha=32, lora_dropout=0.05, logging_steps=-1, project_name='test', evaluation_strategy='epoch', save_total_limit=1, save_strategy='epoch', auto_find_batch_size=False, fp16=False, push_to_hub=True, use_int8=False, model_max_length=2048, repo_id='keepsteady/test', use_int4=True, trainer='sft', target_modules=None, merge_adapter=False, token=None, backend='default', username=None, use_flash_attention_2=False, func=<function run_llm_command_factory at 0x781d97ec5090>)\u001b[0m\n",
            "Downloading readme: 100% 395/395 [00:00<00:00, 1.65MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/repocard.py:105: UserWarning: Repo card metadata block was not found. Setting CardData to empty.\n",
            "  warnings.warn(\"Repo card metadata block was not found. Setting CardData to empty.\")\n",
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            "Extracting data files: 100% 2/2 [00:00<00:00, 2209.85it/s]\n",
            "Generating train split: 9846 examples [00:00, 142187.63 examples/s]\n",
            "Generating test split: 518 examples [00:00, 114506.67 examples/s]\n",
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            "Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/autotrain\", line 33, in <module>\n",
            "    sys.exit(load_entry_point('autotrain-advanced', 'console_scripts', 'autotrain')())\n",
            "  File \"/content/KoChatGPT/autotrain-advanced_231012/src/autotrain/cli/autotrain.py\", line 46, in main\n",
            "    command.run()\n",
            "  File \"/content/KoChatGPT/autotrain-advanced_231012/src/autotrain/cli/run_llm.py\", line 495, in run\n",
            "    train_llm(params)\n",
            "  File \"/content/KoChatGPT/autotrain-advanced_231012/src/autotrain/utils.py\", line 280, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/content/KoChatGPT/autotrain-advanced_231012/src/autotrain/trainers/clm/__main__.py\", line 124, in train\n",
            "    model = AutoModelForCausalLM.from_pretrained(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py\", line 566, in from_pretrained\n",
            "    return model_class.from_pretrained(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\", line 3128, in from_pretrained\n",
            "    resolved_archive_file, sharded_metadata = get_checkpoint_shard_files(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\", line 1052, in get_checkpoint_shard_files\n",
            "    cached_filename = cached_file(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\", line 430, in cached_file\n",
            "    resolved_file = hf_hub_download(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\n",
            "    return fn(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\", line 1461, in hf_hub_download\n",
            "    http_get(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\", line 544, in http_get\n",
            "    temp_file.write(chunk)\n",
            "  File \"/usr/lib/python3.10/tempfile.py\", line 622, in func_wrapper\n",
            "    return func(*args, **kwargs)\n",
            "KeyboardInterrupt\n"
          ]
        }
      ],
      "source": [
        "!autotrain llm --train --project_name test                     --model TinyPixel/Llama-2-7B-bf16-sharded                     --data_path timdettmers/openassistant-guanaco                     --text_column \"text\" \\--use_peft                     --use_int4                     --learning_rate 2e-4                     --train_batch_size 2                     --num_train_epochs 2                     --trainer sft                     --model_max_length 2048                     --push_to_hub                     --repo_id keepsteady/test                     --block_size 2048"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "w9zh7Nlu3LUj"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "m-_5jFtq3LUj"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "id": "JvMRbVLEJlZT"
      },
      "outputs": [],
      "source": [
        "#@title 🤗 AutoTrain LLM\n",
        "#@markdown In order to use this colab\n",
        "#@markdown - upload train.csv to a folder named `data/`\n",
        "#@markdown - train.csv must contain a `text` column\n",
        "#@markdown - choose a project name if you wish\n",
        "#@markdown - change model if you wish, you can use most of the text-generation models from Hugging Face Hub\n",
        "#@markdown - add huggingface information (token and repo_id) if you wish to push trained model to huggingface hub\n",
        "#@markdown - update hyperparameters if you wish\n",
        "#@markdown - click `Runtime > Run all` or run each cell individually\n",
        "\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K7yIeAgVz5tP"
      },
      "source": [
        "project_name: checkpoint등 파일들의 저장 경로\n",
        "model: model 경로 (LLAMA2 2.7B 사용)\n",
        "data_path: 데이터 경로(ko-alpaca 데이터셋 사용)\n",
        "text_column: 데이터 중 사용할 칼럼 지정 ('text' 칼럼 사용)\n",
        "use_peft: parameter efficient fine tuning\n",
        "use_int4: integer 형태로\n",
        "learning_rate: 학습률 지정\n",
        "train_batch_size: 학습 배치사이즈\n",
        " A100의 경우 16, T4의 경우 2 추천\n",
        "num_train_epoch\n",
        "trainer: sft(supervised fine tuning)으로 지정\n",
        "model_max_length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "tKyrYR2Bz4Ll"
      },
      "outputs": [],
      "source": [
        "#@title 🤗 AutoTrain LLM 입력 파라미터 설명\n",
        "#@markdown 학습하기 위해 아래 파라미터 입력 필요\n",
        "#@markdown - project_name: checkpoint등 파일들이 저장될 경로\n",
        "#@markdown - model: model 경로 (LLAMA2 2.7B 사용)\n",
        "#@markdown - data_path: 데이터 경로(ko-alpaca 데이터셋 사용)\n",
        "#@markdown - text_column: 데이터 중 사용할 칼럼 지정 ('text' 칼럼 사용)\n",
        "#@markdown - use_peft: parameter efficient fine tuning\n",
        "#@markdown - merge_adapter: PEFT로 학습된 부분을 실제 모델과 병합할 것인가?? 큰 모델로 만들지, 작은 부분만 저장할지 선택, True이면 전체 모델이 저장\n",
        "#@markdown - use_int4: integer 형태로\n",
        "#@markdown - learning_rate: 학습률 지정\n",
        "#@markdown - train_batch_size: 학습 배치사이즈, A100의 경우 16, T4의 경우 2 추천\n",
        "#@markdown - num_train_epoch: 몇번 반복학습 할지\n",
        "#@markdown - trainer: sft(supervised fine tuning)으로 지정\n",
        "#@markdown - model_max_length: 최대 생성 길이"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "grNf-TUR5eQc"
      },
      "source": [
        "## Finetune config\n",
        "\n",
        "- 초기 모델: https://huggingface.co/TinyPixel/Llama-2-7B-bf16-sharded\n",
        "- 데이터셋(한국어): https://huggingface.co/datasets/royboy0416/ko-alpaca\n",
        "- 데이터셋(영어): https://huggingface.co/datasets/timdettmers/openassistant-guanaco"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "BI-UMjFT5des",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255,
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        },
        "outputId": "6424a128-8968-4d17-9817-ff82df06243f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b7ba8cdf3b1647cf85e2300b442cb053"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/106k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
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              "version_minor": 0,
              "model_id": "76666ddb18db42cf815e528f3c91a6e9"
            }
          },
          "metadata": {}
        },
        {
          "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": "3eed97eb886940b2bbaf5f7d0346be16"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['instruction', 'input', 'output', 'text'],\n",
              "        num_rows: 120\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# 작은 모델로 체크\n",
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"aboonaji/alpaca_micro_demo\")\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "A2-_lkBS1WKA",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@markdown ---\n",
        "#@markdown #### Project Config\n",
        "#@markdown Note: if you are using a restricted/private model, you need to enter your Hugging Face token in the next step.\n",
        "project_name = 'my_autotrain_llm' # @param {type:\"string\"}\n",
        "model_name = 'abhishek/llama-2-7b-hf-small-shards' # @param {type:\"string\"}\n",
        "# data_path = 'royboy0416/ko-alpaca' # @param {type:\"string\"}\n",
        "data_path = 'aboonaji/alpaca_micro_demo' # @param {type:\"string\"}\n",
        "text_column = 'text' # @param {type:\"string\"}\n",
        "# royboy0416/ko-alpaca, timdettmers/openassistant-guanaco\n",
        "\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown #### Push to Hub?\n",
        "#@markdown Use these only if you want to push your trained model to a private repo in your Hugging Face Account\n",
        "#@markdown If you dont use these, the model will be saved in Google Colab and you are required to download it manually.\n",
        "#@markdown Please enter your Hugging Face write token. The trained model will be saved to your Hugging Face account.\n",
        "#@markdown You can find your token here: https://huggingface.co/settings/tokens\n",
        "push_to_hub = False # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "hf_token = \"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\" #@param {type:\"string\"}\n",
        "repo_id = \"keepsteady/autotrain_test\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown #### Hyperparameters\n",
        "#@markdown ###### A100: 16, T4: 2\n",
        "num_train_epochs = 1 #@param {type:\"number\"}\n",
        "learning_rate = 2e-4 # @param {type:\"number\"}\n",
        "train_batch_size = 2 # @param {type:\"slider\", min:1, max:32, step:1}\n",
        "block_size = 1024 # @param {type:\"number\"}\n",
        "trainer = \"sft\" # @param [\"default\", \"sft\"] {type:\"raw\"}\n",
        "warmup_ratio = 0.1 # @param {type:\"number\"}\n",
        "weight_decay = 0.01 # @param {type:\"number\"}\n",
        "gradient_accumulation_step = 4 # @param {type:\"number\"}\n",
        "use_fp16 = True # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "use_peft = True # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "use_merge_adapter = True # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "use_int4 = True # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "lora_r = 16 #@param {type:\"number\"}\n",
        "lora_alpha = 32 #@param {type:\"number\"}\n",
        "lora_dropout = 0.05 #@param {type:\"number\"}\n",
        "\n",
        "os.environ[\"PROJECT_NAME\"] = project_name\n",
        "os.environ[\"MODEL_NAME\"] = model_name\n",
        "os.environ[\"PUSH_TO_HUB\"] = str(push_to_hub)\n",
        "os.environ[\"HF_TOKEN\"] = hf_token\n",
        "os.environ[\"REPO_ID\"] = repo_id\n",
        "os.environ[\"LEARNING_RATE\"] = str(learning_rate)\n",
        "os.environ[\"NUM_EPOCHS\"] = str(num_train_epochs)\n",
        "os.environ[\"BATCH_SIZE\"] = str(train_batch_size)\n",
        "os.environ[\"BLOCK_SIZE\"] = str(block_size)\n",
        "os.environ[\"WARMUP_RATIO\"] = str(warmup_ratio)\n",
        "os.environ[\"WEIGHT_DECAY\"] = str(weight_decay)\n",
        "os.environ[\"GRADIENT_ACCUMULATION\"] = str(gradient_accumulation_step)\n",
        "os.environ[\"USE_FP16\"] = str(use_fp16)\n",
        "os.environ[\"USE_PEFT\"] = str(use_peft)\n",
        "os.environ[\"USE_INT4\"] = str(use_int4)\n",
        "os.environ[\"LORA_R\"] = str(lora_r)\n",
        "os.environ[\"LORA_ALPHA\"] = str(lora_alpha)\n",
        "os.environ[\"LORA_DROPOUT\"] = str(lora_dropout)\n",
        "\n",
        "\n",
        "\n",
        "txt_fp16 = ' --fp16' if use_fp16 else ''\n",
        "txt_use_peft = ' --use_peft' if use_peft else ''\n",
        "txt_use_int4 = ' --use_int4' if use_int4 else ''\n",
        "txt_use_merge_adapter = ' --merge_adapter' if use_merge_adapter else ''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OMNkaLoiw88H",
        "outputId": "95150b7e-d5cd-48e7-8f98-d0d9dae961be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "!autotrain llm --train --model \"abhishek/llama-2-7b-hf-small-shards\" --project_name \"my_autotrain_llm\" --data_path \"aboonaji/alpaca_micro_demo\" --text_column \"text\" --learning_rate 0.0002 --train_batch_size 2 --num_train_epochs 1 --block_size 1024 --warmup_ratio 0.1 --lora_r 16 --lora_alpha 32 --lora_dropout 0.05 --weight_decay 0.01 --gradient_accumulation_steps 4 --trainer sft  --fp16 --use_peft --use_int4 --merge_adapter --push-to-hub --token \"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\" --repo-id \"keepsteady/autotrain_test\"\n"
          ]
        }
      ],
      "source": [
        "# Train!\n",
        "cmd = f'!autotrain llm \\\n",
        "--train \\\n",
        "--model \"{model_name}\" \\\n",
        "--project_name \"{project_name}\" \\\n",
        "--data_path \"{data_path}\" \\\n",
        "--text_column \"{text_column}\" \\\n",
        "--learning_rate {learning_rate} \\\n",
        "--train_batch_size {train_batch_size} \\\n",
        "--num_train_epochs {num_train_epochs} \\\n",
        "--block_size {block_size} \\\n",
        "--warmup_ratio {warmup_ratio} \\\n",
        "--lora_r {lora_r} \\\n",
        "--lora_alpha {lora_alpha} \\\n",
        "--lora_dropout {lora_dropout} \\\n",
        "--weight_decay {weight_decay} \\\n",
        "--gradient_accumulation_steps {gradient_accumulation_step} \\\n",
        "--trainer {trainer} \\\n",
        "{txt_fp16}{txt_use_peft}{txt_use_int4}{txt_use_merge_adapter} \\\n",
        "--push-to-hub --token \"{hf_token}\" --repo-id \"{repo_id}\"'\n",
        "\n",
        "print(cmd)\n",
        "!$command"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X3XZKnC03LUk"
      },
      "outputs": [],
      "source": [
        "!autotrain llm --train --model \"abhishek/llama-2-7b-hf-small-shards\" --project_name \"my_autotrain_llm\" --data_path \"aboonaji/alpaca_micro_demo\" --text_column \"text\" --learning_rate 0.0002 --train_batch_size 2 --num_train_epochs 1 --block_size 1024 --warmup_ratio 0.1 --lora_r 16 --lora_alpha 32 --lora_dropout 0.05 --weight_decay 0.01 --gradient_accumulation_steps 4 --trainer sft  --fp16 --use_peft --use_int4 --merge_adapter --push-to-hub --token \"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\" --repo-id \"keepsteady/autotrain_test\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "IFZ2C_wH3LUk"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DmyHQcBA3LUk"
      },
      "source": [
        "# 학습한 Fine-tunning 모델 사용하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "9NMGE3zQ3LUk"
      },
      "outputs": [],
      "source": [
        "# # 설치\n",
        "# !pip install -q transformers peft bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "NCl5NJIk3LUk"
      },
      "outputs": [],
      "source": [
        "repo_id = 'keepsteady/autotrain_test'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "M36hOrF53LUk"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "TYg0jn5S3LUl",
        "outputId": "7c29d5eb-c7be-4063-b1b5-b0aa51a64875"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHFValidationError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/config.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, subfolder, **kwargs)\u001b[0m\n\u001b[1;32m    104\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m                 config_file = hf_hub_download(\n\u001b[0m\u001b[1;32m    106\u001b[0m                     \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCONFIG_NAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubfolder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubfolder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhf_hub_download_kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    109\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0marg_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"repo_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"from_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"to_id\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m                 \u001b[0mvalidate_repo_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36mvalidate_repo_id\u001b[0;34m(repo_id)\u001b[0m\n\u001b[1;32m    157\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mrepo_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m         raise HFValidationError(\n\u001b[0m\u001b[1;32m    159\u001b[0m             \u001b[0;34m\"Repo id must be in the form 'repo_name' or 'namespace/repo_name':\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHFValidationError\u001b[0m: Repo id must be in the form 'repo_name' or 'namespace/repo_name': './my_autotrain_llm/checkpoint-5'. Use `repo_type` argument if needed.",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-426928062077>\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPeftConfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeft_model_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/config.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, subfolder, **kwargs)\u001b[0m\n\u001b[1;32m    107\u001b[0m                 )\n\u001b[1;32m    108\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Can't find '{CONFIG_NAME}' at '{pretrained_model_name_or_path}'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0mloaded_attributes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_json_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Can't find 'adapter_config.json' at './my_autotrain_llm/checkpoint-5'"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "from peft import PeftModel, PeftConfig\n",
        "\n",
        "model_id = \"TinyPixel/Llama-2-7B-bf16-sharded\"\n",
        "# # 구글드라이브에서 가져오기\n",
        "# peft_model_id = \"/content/drive/MyDrive/my_autotrain_llm/checkpoint-1222\"  # checkpoint 변경\n",
        "# # huggingface에서 가져오기\n",
        "# peft_model_id = 'keepsteady/autotrain_test'  # 학습 저장된 폴더경로\n",
        "# local에 저장된 폴더에서 모델 가져오기\n",
        "peft_model_id = './my_autotrain_llm/checkpoint-5'\n",
        "\n",
        "\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "\n",
        "\n",
        "# Huggingface에 저장된 README.md 에서 Training porcedure 값을 가져옴\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_8bit=False,\n",
        "    load_in_4bit=True,\n",
        "    llm_int8_threshold=6.0,\n",
        "    llm_int8_skip_modules=None,\n",
        "    llm_int8_enable_fp32_cpu_offload=False,\n",
        "    llm_int8_has_fp16_weight=False,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_use_double_quant=False,\n",
        "    bnb_4bit_compute_dtype=\"float16\",\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\":0})\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "\n",
        "model.eval()\n",
        "print('load model complete')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AYIvNutt3LUl"
      },
      "outputs": [],
      "source": [
        "# data\n",
        "description = 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.'\n",
        "\n",
        "\n",
        "지시 = 'Identify the odd one out.'\n",
        "입력 = 'Twitter, Instagram, Telegram'\n",
        "출력 = 'Telegram'\n",
        "\n",
        "text = f'{description}\\n### Instruction: {지시}\\n### Input: {입력}\\n### Response: {출력}'\n",
        "print(text)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BvBe3Xxp3LUl"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline\n",
        "import torch\n",
        "\n",
        "llama_pipeline = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v-OW4JMC3LUl"
      },
      "outputs": [],
      "source": [
        "\n",
        "def gen(Instruction=None, Input=None):\n",
        "\n",
        "    # 포맷 변경\n",
        "    description = 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.'\n",
        "    if Input:\n",
        "        prompt = f'{description}\\n### Instruction: {Instruction}\\n### Input: {Input}\\n### Response: '\n",
        "    else:\n",
        "        prompt = f'{description}\\n### Instruction: {Instruction}\\n### Response: '\n",
        "\n",
        "    generated_txt = model.generate(\n",
        "        **tokenizer(\n",
        "            prompt,  # 프롬프트 입력\n",
        "            return_tensors='pt',\n",
        "            return_token_type_ids=False\n",
        "        ).to('cuda'),\n",
        "        max_new_tokens=128,\n",
        "        early_stopping=True,\n",
        "        do_sample=True,\n",
        "    )\n",
        "    answer = tokenizer.decode(generated_txt[0]).replace(prompt, \"\").lstrip('<s> ').rstrip('</s>').strip().split(\"\\n### \")[0].strip()\n",
        "    print(answer)\n",
        "\n",
        "    return answer\n",
        "\n",
        "\n",
        "output = gen(Instruction=\"생성형 AI로 업무를 자동화 할 수 있을까요?\", Input=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GDphEk2t3LUl"
      },
      "outputs": [],
      "source": [
        "output = gen(Instruction=\"AI를 실제 업무에 적용하려면 어떤 프로세스가 필요해요?\", Input=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "jTNWdzAE3LUl"
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      "source": []
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        "id": "lDNsGd8C3LUx"
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      "source": []
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