91jeong_krx_qwen2_1.5b.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyPWZ8Jb4Kxe+LPy00eQFSll"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","source":["!pip install transformers datasets trl bitsandbytes peft\n","!pip install datasets\n","!pip install -U accelerate"],"metadata":{"collapsed":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"-84y3O9audxh","executionInfo":{"status":"ok","timestamp":1730211323839,"user_tz":-540,"elapsed":24581,"user":{"displayName":"‍구원정[ 학부재학 / 수학과 ]","userId":"15682121601729926510"}},"outputId":"62401782-8e8b-4215-efdd-41331def3f6d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n","Collecting datasets\n","  Downloading datasets-3.0.2-py3-none-any.whl.metadata (20 kB)\n","Collecting 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torchvision\n","torchvision.disable_beta_transforms_warning()\n","from datasets import load_dataset, concatenate_datasets\n","from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, BitsAndBytesConfig\n","from trl import SFTTrainer\n","from peft import LoraConfig, get_peft_model"]},{"cell_type":"markdown","source":["# W&B 비활성화"],"metadata":{"id":"3QxeXUHqyZSL"}},{"cell_type":"code","source":["import os\n","os.environ[\"WANDB_MODE\"] = \"disabled\""],"metadata":{"id":"G5KNSMgQyZJs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["torch.cuda.empty_cache()\n","torch.cuda.memory_summary(device=None, abbreviated=False)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":227},"collapsed":true,"id":"dvrZxfhRoSX_","executionInfo":{"status":"ok","timestamp":1730211361971,"user_tz":-540,"elapsed":375,"user":{"displayName":"‍구원정[ 학부재학 / 수학과 ]","userId":"15682121601729926510"}},"outputId":"27bcfaf8-f420-451f-824c-d6e329218838"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'|===========================================================================|\\n|                  PyTorch CUDA memory summary, device ID 0                 |\\n|---------------------------------------------------------------------------|\\n|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\\n|===========================================================================|\\n|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\\n|---------------------------------------------------------------------------|\\n| Allocated memory      |      0 B   |      0 B   |      0 B   |      0 B   |\\n|       from large pool |      0 B   |      0 B   |      0 B   |      0 B   |\\n|       from small pool |      0 B   |      0 B   |      0 B   |      0 B   |\\n|---------------------------------------------------------------------------|\\n| Active memory         |      0 B   |      0 B   |      0 B   |      0 B   |\\n|       from large pool |      0 B   |      0 B   |      0 B   |      0 B   |\\n|       from small pool |      0 B   |      0 B   |      0 B   |      0 B   |\\n|---------------------------------------------------------------------------|\\n| Requested memory      |      0 B   |      0 B   |      0 B   |      0 B   |\\n|       from large pool |      0 B   |      0 B   |      0 B   |      0 B   |\\n|       from small pool |      0 B   |      0 B   |      0 B   |      0 B   |\\n|---------------------------------------------------------------------------|\\n| GPU reserved memory   |      0 B   |      0 B   |      0 B   |      0 B   |\\n|       from large pool |      0 B   |      0 B   |      0 B   |      0 B   |\\n|       from small pool |      0 B   |      0 B   |      0 B   |      0 B   |\\n|---------------------------------------------------------------------------|\\n| Non-releasable memory |      0 B   |      0 B   |      0 B   |      0 B   |\\n|       from large pool |      0 B   |      0 B   |      0 B   |      0 B   |\\n|       from small pool |      0 B   |      0 B   |      0 B   |      0 B   |\\n|---------------------------------------------------------------------------|\\n| Allocations           |       0    |       0    |       0    |       0    |\\n|       from large pool |       0    |       0    |       0    |       0    |\\n|       from small pool |       0    |       0    |       0    |       0    |\\n|---------------------------------------------------------------------------|\\n| Active allocs         |       0    |       0    |       0    |       0    |\\n|       from large pool |       0    |       0    |       0    |       0    |\\n|       from small pool |       0    |       0    |       0    |       0    |\\n|---------------------------------------------------------------------------|\\n| GPU reserved segments |       0    |       0    |       0    |       0    |\\n|       from large pool |       0    |       0    |       0    |       0    |\\n|       from small pool |       0    |       0    |       0    |       0    |\\n|---------------------------------------------------------------------------|\\n| Non-releasable allocs |       0    |       0    |       0    |       0    |\\n|       from large pool |       0    |       0    |       0    |       0    |\\n|       from small pool |       0    |       0    |       0    |       0    |\\n|---------------------------------------------------------------------------|\\n| Oversize allocations  |       0    |       0    |       0    |       0    |\\n|---------------------------------------------------------------------------|\\n| Oversize GPU segments |       0    |       0    |       0    |       0    |\\n|===========================================================================|\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","source":["# 환경 변수 로드 및 Google Colab 환경 설정"],"metadata":{"id":"hvemiqXbtPVo"}},{"cell_type":"code","source":["if os.path.exists('C:/Users/yd170/OneDrive/바탕 화면/Coding/KRX.env'):\n","    load_dotenv('C:/Users/yd170/OneDrive/바탕 화면/Coding/KRX.env')\n","    hf_token = os.getenv(\"HF_TOKEN\")\n","else:\n","    hf_token = \"YOUR_HF_TOKEN\"\n","\n","model_name = \"Qwen/Qwen2-1.5B\"\n","max_seq_length = 2048"],"metadata":{"id":"2nEoHW7itR3t"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# 양자화 설정 (4비트 양자화 사용)"],"metadata":{"id":"XPOupITbtTe1"}},{"cell_type":"code","source":["bnb_config = BitsAndBytesConfig(\n","    load_in_4bit=True,  # 4비트 양자화 적용\n","    bnb_4bit_use_double_quant=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch.float16\n",")"],"metadata":{"id":"LxQopNuFtTOy"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# 모델 및 토크나이저 로드 (GPU 사용하도록 설정)"],"metadata":{"id":"dcD5t1OstWbx"}},{"cell_type":"code","source":["tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)\n","model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config, device_map=\"auto\")"],"metadata":{"collapsed":true,"id":"uXH2KLTytX0A"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# LoRA 설정 추가"],"metadata":{"id":"230hvGJmtZU6"}},{"cell_type":"code","source":["lora_config = LoraConfig(\n","    r=16,\n","    lora_alpha=32,\n","    target_modules=[\"q_proj\", \"v_proj\"],\n","    lora_dropout=0.05,\n","    bias=\"none\"\n",")\n","model = get_peft_model(model, lora_config)"],"metadata":{"id":"RM4Xvc74tZD8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# 두 개의 데이터셋 로드 및 병합"],"metadata":{"id":"Y-jCFuCdtb7i"}},{"cell_type":"code","source":["first_dataset = load_dataset(\"amphora/krx-sample-instructions\", split=\"train\")\n","second_dataset = load_dataset(\"Cartinoe5930/web_text_synthetic_dataset_50k\", split=\"train\")\n","\n","# 데이터셋 병합\n","dataset = concatenate_datasets([first_dataset, second_dataset])\n","\n","# 프롬프트 포맷 설정\n","prompt_format = \"\"\"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","### Instruction:\n","{}\n","\n","### Response:\n","{}\"\"\"\n","\n","EOS_TOKEN = tokenizer.eos_token\n","\n","def formatting_prompts_func(examples):\n","    instructions = []\n","    outputs = []\n","\n","    # 데이터셋 내 필드 확인 후 각 필드에 따라 처리\n","    if \"prompt\" in examples and \"response\" in examples:\n","        instructions = examples[\"prompt\"]\n","        outputs = examples[\"response\"]\n","    elif \"question\" in examples and \"response\" in examples:\n","        instructions = examples[\"question\"]\n","        outputs = examples[\"response\"]\n","    else:\n","        raise KeyError(\"The dataset fields do not match the expected format.\")\n","\n","    texts = [prompt_format.format(instr, output) + EOS_TOKEN for instr, output in zip(instructions, outputs)]\n","    return {\"formatted_text\": texts}\n","\n","# 데이터셋 가공\n","dataset = dataset.map(formatting_prompts_func, batched=True)"],"metadata":{"collapsed":true,"id":"FNBsMfZWtdYm"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# 모델 학습 설정"],"metadata":{"id":"yhoxZxuBtmuu"}},{"cell_type":"code","source":["training_args = TrainingArguments(\n","    output_dir=\"./output\",\n","    per_device_train_batch_size=1,  d\n","    gradient_accumulation_steps=8,\n","    max_steps=100,\n","    logging_steps=10,\n","    learning_rate=2e-5,\n","    seed=42,\n","    save_steps=100,\n","    fp16=True, # 혼합 정밀도 사용으로 메모리 최적화\n","    report_to=\"none\",\n",")"],"metadata":{"id":"ijdYp1Wxtmgi"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# GPU 메모리 관리 최적화 환경 변수 설정"],"metadata":{"id":"O7iV-HEyxagR"}},{"cell_type":"code","source":["os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\""],"metadata":{"id":"WqNvG7vwxboU"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# SFTTrainer 초기화"],"metadata":{"id":"bPlQN84Nto4j"}},{"cell_type":"code","source":["trainer = SFTTrainer(\n","    model=model,\n","    tokenizer=tokenizer,\n","    train_dataset=dataset,  # 병합된 데이터셋 사용\n","    dataset_text_field=\"formatted_text\",\n","    max_seq_length=1024,\n","    args=training_args,\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"O4MNZHjNtp11","executionInfo":{"status":"ok","timestamp":1730212244752,"user_tz":-540,"elapsed":765,"user":{"displayName":"‍구원정[ 학부재학 / 수학과 ]","userId":"15682121601729926510"}},"outputId":"0e779eee-4195-4ef5-93bc-ce3096a1e2a9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in '__init__': dataset_text_field, max_seq_length. Will not be supported from version '1.0.0'.\n","\n","Deprecated positional argument(s) used in SFTTrainer, please use the SFTConfig to set these arguments instead.\n","  warnings.warn(message, FutureWarning)\n","/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:283: UserWarning: You passed a `max_seq_length` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:321: UserWarning: You passed a `dataset_text_field` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n","  warnings.warn(\n","max_steps is given, it will override any value given in num_train_epochs\n"]}]},{"cell_type":"markdown","source":["# 학습"],"metadata":{"id":"qbLwnuqYtqYh"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t586zh7owDHf","executionInfo":{"status":"ok","timestamp":1730211648880,"user_tz":-540,"elapsed":18263,"user":{"displayName":"‍구원정[ 학부재학 / 수학과 ]","userId":"15682121601729926510"}},"outputId":"0409cadd-f76f-41aa-a2ff-a738e6d575f5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["print(\"모델 학습 시작...\")\n","trainer.train()\n","print(\"모델 학습 완료.\")\n","\n","# 학습된 모델 저장\n","print(\"모델 저장 중...\")\n","model.save_pretrained(\"/content/drive/My Drive/KRX_Qwen2_1_5B\")\n","print(\"모델 저장 완료.\")\n","\n","print(\"토크나이저 저장 중...\")\n","tokenizer.save_pretrained(\"/content/drive/My Drive/KRX_Qwen2_1_5B\")\n","print(\"토크나이저 저장 완료.\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":493},"collapsed":true,"id":"42X2FaIPtsdj","executionInfo":{"status":"ok","timestamp":1730212538132,"user_tz":-540,"elapsed":287807,"user":{"displayName":"‍구원정[ 학부재학 / 수학과 ]","userId":"15682121601729926510"}},"outputId":"8e271ef3-1f6b-4754-841d-fe7797d070fb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["모델 학습 시작...\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [100/100 04:37, Epoch 0/1]\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>10</td>\n","      <td>1.821300</td>\n","    </tr>\n","    <tr>\n","      <td>20</td>\n","      <td>1.804400</td>\n","    </tr>\n","    <tr>\n","      <td>30</td>\n","      <td>1.777700</td>\n","    </tr>\n","    <tr>\n","      <td>40</td>\n","      <td>1.827900</td>\n","    </tr>\n","    <tr>\n","      <td>50</td>\n","      <td>1.770500</td>\n","    </tr>\n","    <tr>\n","      <td>60</td>\n","      <td>1.734600</td>\n","    </tr>\n","    <tr>\n","      <td>70</td>\n","      <td>1.803100</td>\n","    </tr>\n","    <tr>\n","      <td>80</td>\n","      <td>1.693200</td>\n","    </tr>\n","    <tr>\n","      <td>90</td>\n","      <td>1.727900</td>\n","    </tr>\n","    <tr>\n","      <td>100</td>\n","      <td>1.709200</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["모델 학습 완료.\n","모델 저장 중...\n","모델 저장 완료.\n","토크나이저 저장 중...\n","토크나이저 저장 완료.\n"]}]},{"cell_type":"markdown","source":["# 학습된 모델 로드"],"metadata":{"id":"TiLfumsA1TVr"}},{"cell_type":"code","source":["from transformers import AutoTokenizer, AutoModelForCausalLM\n","from google.colab import drive\n","\n","# Google Drive 마운트\n","drive.mount('/content/drive')\n","\n","# 학습된 모델 로드 (Google Drive에서 저장된 경로 지정)\n","model_name = \"/content/drive/My Drive/KRX_Qwen2_1_5B\"\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","model = AutoModelForCausalLM.from_pretrained(model_name)\n","\n","print(\"모델과 토크나이저가 성공적으로 로드되었습니다.\")"],"metadata":{"collapsed":true,"id":"onqwou0O1VU8","executionInfo":{"status":"ok","timestamp":1730212718308,"user_tz":-540,"elapsed":41590,"user":{"displayName":"‍구원정[ 학부재학 / 수학과 ]","userId":"15682121601729926510"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"07c75506-35c0-4647-8a83-d78bf82f4a39"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]},{"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":"stream","name":"stdout","text":["모델과 토크나이저가 성공적으로 로드되었습니다.\n"]}]},{"cell_type":"markdown","source":["# 추론을 위한 프롬프트 설정"],"metadata":{"id":"J3YP2p4b1YMF"}},{"cell_type":"code","source":["prompt_format = \"\"\"The following is a detailed financial question or instruction, and the corresponding answer is expected to be precise and informative. Use relevant financial terms and provide a comprehensive explanation.\n","\n","### Instruction:\n","{}\n","\n","### Response:\"\"\""],"metadata":{"id":"bEuDvZ9N1Z-l"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# 예제 프롬프트"],"metadata":{"id":"WAcjaaz91av1"}},{"cell_type":"code","source":["instruction = \"선물옵션에 대해 설명해줘.\"\n","prompt = prompt_format.format(instruction)\n","inputs = tokenizer(prompt, return_tensors=\"pt\")"],"metadata":{"id":"5rCUMNMT1cKw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# 텍스트 생성"],"metadata":{"id":"OT6wiZby1dvy"}},{"cell_type":"code","source":["outputs = model.generate(\n","    **inputs,\n","    max_new_tokens=256,\n","    temperature=0.7,         # 다양성 조절\n","    top_k=50,                # 최상위 K개의 단어만 고려\n","    repetition_penalty=1.2,  # 반복을 줄이기 위한 패널티\n","    use_cache=True\n",")"],"metadata":{"id":"ZQEsBUq21eyc","executionInfo":{"status":"ok","timestamp":1730212993303,"user_tz":-540,"elapsed":24179,"user":{"displayName":"‍구원정[ 학부재학 / 수학과 ]","userId":"15682121601729926510"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"08c63ca0-9b2f-4437-9a80-5fc0d814a7a0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.7` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n","  warnings.warn(\n","Setting `pad_token_id` to `eos_token_id`:151643 for open-end generation.\n"]}]},{"cell_type":"markdown","source":["# 결과 출력"],"metadata":{"id":"RG3dENu01gNC"}},{"cell_type":"code","source":["response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n","\n","# 입력된 프롬프트 이후의 응답 부분만 출력\n","print(response[len(prompt):].strip())"],"metadata":{"id":"IgPVmSqQ1hlC","executionInfo":{"status":"ok","timestamp":1730213030713,"user_tz":-540,"elapsed":385,"user":{"displayName":"‍구원정[ 학부재학 / 수학과 ]","userId":"15682121601729926510"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"1f5ee5aa-8144-4b70-a47d-953e81782543"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Orange price at the port of origin?\n","\n","Yes, that's correct! The term \"orange\" in this context refers to the orange fruit itself rather than its value as an investment asset.\n"]}]}]}