{"cells":[{"cell_type":"markdown","source":["# **미세튜닝된 어댑터 업로드한 ID: yelin1001**"],"metadata":{"id":"8WVANRh09Lmi"},"id":"8WVANRh09Lmi"},{"cell_type":"markdown","metadata":{"id":"28e4c4d1-a73f-437b-a1bd-c2cc3874924a"},"source":["# 강의 11주차: llama2-food-order-understanding\n","\n","1. llama-2-7b-chat-hf 를 주문 문장 이해에 미세 튜닝\n","\n","- food-order-understanding-small-3200.json (학습)\n","- food-order-understanding-small-800.json (검증)\n","\n","\n","종속적인 필요 내용\n","- huggingface 계정 설정 및 llama-2 사용 승인\n","- 로깅을 위한 wandb"],"id":"28e4c4d1-a73f-437b-a1bd-c2cc3874924a"},{"cell_type":"markdown","source":["**종속성 설치**"],"metadata":{"id":"bw2BFoFLTKUm"},"id":"bw2BFoFLTKUm"},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":32444,"status":"ok","timestamp":1702211414044,"user":{"displayName":"장예린","userId":"08326973126538993419"},"user_tz":-540},"id":"nDZe_wqKU6J3","colab":{"base_uri":"https://localhost:8080/"},"outputId":"34fee47c-aa48-4056-c525-28e0a66dae0c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n","Collecting peft\n","  Downloading peft-0.7.0-py3-none-any.whl (168 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.3/168.3 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting accelerate\n","  Downloading accelerate-0.25.0-py3-none-any.whl (265 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.7/265.7 kB\u001b[0m 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setproctitle, sentry-sdk, pyarrow-hotfix, humanfriendly, docstring-parser, docker-pycreds, dill, multiprocess, gitdb, coloredlogs, tyro, GitPython, accelerate, wandb, datasets, trl, peft, optimum\n","Successfully installed GitPython-3.1.40 accelerate-0.25.0 bitsandbytes-0.41.3 coloredlogs-15.0.1 datasets-2.15.0 dill-0.3.7 docker-pycreds-0.4.0 docstring-parser-0.15 gitdb-4.0.11 humanfriendly-10.0 multiprocess-0.70.15 optimum-1.15.0 peft-0.7.0 pyarrow-hotfix-0.6 sentencepiece-0.1.99 sentry-sdk-1.38.0 setproctitle-1.3.3 shtab-1.6.5 smmap-5.0.1 trl-0.7.4 tyro-0.6.0 wandb-0.16.1\n"]}],"source":["pip install transformers peft accelerate optimum bitsandbytes trl wandb"],"id":"nDZe_wqKU6J3"},{"cell_type":"markdown","source":["**라이브러리 및 모듈 임포트**\n","\n","> 데이터 처리, 모델 학습, 추론에 사용되는 라이브러리 및 모듈을 포함함."],"metadata":{"id":"4_gMKrh6TjGX"},"id":"4_gMKrh6TjGX"},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":21277,"status":"ok","timestamp":1702211440757,"user":{"displayName":"장예린","userId":"08326973126538993419"},"user_tz":-540},"id":"51eb00d7-2928-41ad-9ae9-7f0da7d64d6d","colab":{"base_uri":"https://localhost:8080/"},"outputId":"17065527-a612-40d3-bf40-6c82f4ef70a0"},"outputs":[{"output_type":"stream","name":"stderr","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"]}],"source":["import os\n","from dataclasses import dataclass, field\n","from typing import Optional\n","import re\n","\n","import torch\n","import tyro\n","from accelerate import Accelerator\n","from datasets import load_dataset, Dataset\n","from peft import AutoPeftModelForCausalLM, LoraConfig\n","from tqdm import tqdm\n","from transformers import (\n","    AutoModelForCausalLM,\n","    AutoTokenizer,\n","    BitsAndBytesConfig,\n","    TrainingArguments,\n",")\n","\n","from trl import SFTTrainer\n","\n","from trl.trainer import ConstantLengthDataset"],"id":"51eb00d7-2928-41ad-9ae9-7f0da7d64d6d"},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":162,"referenced_widgets":["667e9c0c44c741d7ad2e1f72a5d30f15","ab31fc48b8964613bda9c7f061b51e04","b6ee4419b72f46dd8316bf7bef2a42a7","80508da0275e49bea4b7f96695676543","3992b9dd8e84461c888d3f6839867c9f","ca86def675be4e04abaf125233b76893","816d6cc5021740ef834f6b74c25f4864","d0a7ce78248342759e07468b3a14df57","6fe8b52eddac4287b660e3cf11c45f6f","627de2f1b3aa43fea5ccc78c4951b987","06b26023303b42ea8c0aca702918cf38","4aced34149be4f138e6c8b6b7e90b539","ea1f90e0b6904f7892e9281be151adf4","e41548502db44070bddfef4c77e8be08","334d81fa47c14d7a82065877a119c3e6","3a3fe6e7b40a4e9fb7f5e1aa12c85984","dacec847ac774fb6a07ea1769d05e949","7c1a8f5442ad46b981a0046087aba092","86382ea99b7c48d2bcb6b85f7a0a45bf","aec695ff46284892ba684fd77a75053a","e0f6b4d95f224c3191b55945a9f32577","98381d851db64876a6488d4f9fe362ff","b003798463b84924a0fcaec47a5de67d","2d519bb251354953bcfb5983af4805cf","7c05d82795dd41f5b26fe7b919cd7d1d","aec11289153746e2b6ee1a4dada4e49f","dcdb79dff3134227b11f21f1441fa0f4","f1f2934e0e0b440ebc413d65cb2764b0","2cf08cb0891b4ae384fb7cd29ebabd47","2042876c98b941f79586a4b4fef8a5fa","aaa012d62d0347e68f71fc24286e54d0","51993e86e0bd42b7aa868925b3b1f660"]},"executionInfo":{"elapsed":282,"status":"ok","timestamp":1702211541255,"user":{"displayName":"장예린","userId":"08326973126538993419"},"user_tz":-540},"id":"tX7gYxZaVhYL","outputId":"c830cdac-bdbf-4f63-d869-19c7cced2ab0"},"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co./front/assets/huggingface_logo-noborder.sv…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"667e9c0c44c741d7ad2e1f72a5d30f15"}},"metadata":{}}],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"],"id":"tX7gYxZaVhYL"},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":15704,"status":"ok","timestamp":1702211563167,"user":{"displayName":"장예린","userId":"08326973126538993419"},"user_tz":-540},"id":"4DF9D2SXVpHP","colab":{"base_uri":"https://localhost:8080/"},"outputId":"f5a427a2-d777-4061-9897-69eb73a72be6"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /gdrive\n"]}],"source":["from google.colab import drive\n","drive.mount('/gdrive')"],"id":"4DF9D2SXVpHP"},{"cell_type":"markdown","metadata":{"id":"036eece3-5f89-4fec-b0cd-268478b5e83d"},"source":["# 매개 변수 설정\n","\n","\n","> 스크립트에 사용되는 다양한 인자들을 설정함.\n","\n","\n","\n"],"id":"036eece3-5f89-4fec-b0cd-268478b5e83d"},{"cell_type":"code","execution_count":5,"metadata":{"id":"e03d01b5-eaeb-4626-9dc5-47c3691e7fcf","executionInfo":{"status":"ok","timestamp":1702211573318,"user_tz":-540,"elapsed":455,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[],"source":["@dataclass\n","class ScriptArguments:\n","    cache_dir: Optional[str] = field(\n","        default=None, metadata={\"help\": \"the cache dir\"}\n","    )\n","    model_name: Optional[str] = field(\n","        default=\"meta-llama/Llama-2-7b-chat-hf\", metadata={\"help\": \"the model name\"}\n","    )\n","\n","    dataset_name: Optional[str] = field(\n","        default=None,\n","        metadata={\"help\": \"the dataset name\"},\n","    )\n","    seq_length: Optional[int] = field(\n","        default=1024, metadata={\"help\": \"the sequence length\"}\n","    )\n","    num_workers: Optional[int] = field(\n","        default=8, metadata={\"help\": \"the number of workers\"}\n","    )\n","    training_args: TrainingArguments = field(\n","        default_factory=lambda: TrainingArguments(\n","            output_dir=\"./results\",\n","            # max_steps=500,\n","            logging_steps=20,\n","            # save_steps=10,\n","            per_device_train_batch_size=1,\n","            per_device_eval_batch_size=1,\n","            gradient_accumulation_steps=2,\n","            gradient_checkpointing=False,\n","            group_by_length=False,\n","            learning_rate=1e-4,\n","            lr_scheduler_type=\"cosine\",\n","            # warmup_steps=100,\n","            warmup_ratio=0.03,\n","            max_grad_norm=0.3,\n","            weight_decay=0.05,\n","            save_total_limit=20,\n","            save_strategy=\"epoch\",\n","            num_train_epochs=1,\n","            optim=\"paged_adamw_32bit\",\n","            fp16=True,\n","            remove_unused_columns=False,\n","            report_to=\"wandb\",\n","        )\n","    )\n","\n","    packing: Optional[bool] = field(\n","        default=True, metadata={\"help\": \"whether to use packing for SFTTrainer\"}\n","    )\n","\n","    peft_config: LoraConfig = field(\n","        default_factory=lambda: LoraConfig(\n","            #r=8,\n","            r=16,\n","            lora_alpha=16,\n","            #lora_dropout=0.05,\n","            lora_dropout=0.1,\n","            target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"down_proj\", \"up_proj\", \"gate_proj\"],\n","            bias=\"none\",\n","            task_type=\"CAUSAL_LM\",\n","        )\n","    )\n","\n","    merge_with_final_checkpoint: Optional[bool] = field(\n","        default=False, metadata={\"help\": \"Do only merge with final checkpoint\"}\n","    )"],"id":"e03d01b5-eaeb-4626-9dc5-47c3691e7fcf"},{"cell_type":"markdown","metadata":{"id":"b0b34850-006c-4c87-a7d5-27c6c871e7de"},"source":["# 유틸리티"],"id":"b0b34850-006c-4c87-a7d5-27c6c871e7de"},{"cell_type":"code","execution_count":6,"metadata":{"id":"8224d213-8766-4b40-899e-1a1b8d164365","executionInfo":{"status":"ok","timestamp":1702211575987,"user_tz":-540,"elapsed":262,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[],"source":["def chars_token_ratio(dataset, tokenizer, nb_examples=400):\n","    \"\"\"\n","    Estimate the average number of characters per token in the dataset.\n","    \"\"\"\n","    total_characters, total_tokens = 0, 0\n","    for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):\n","        text = prepare_sample_text(example)\n","        total_characters += len(text)\n","        if tokenizer.is_fast:\n","            total_tokens += len(tokenizer(text).tokens())\n","        else:\n","            total_tokens += len(tokenizer.tokenize(text))\n","\n","    return total_characters / total_tokens\n","\n","\n","def print_trainable_parameters(model):\n","    \"\"\"\n","    Prints the number of trainable parameters in the model.\n","    \"\"\"\n","    trainable_params = 0\n","    all_param = 0\n","    for _, param in model.named_parameters():\n","        all_param += param.numel()\n","        if param.requires_grad:\n","            trainable_params += param.numel()\n","    print(\n","        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n","    )"],"id":"8224d213-8766-4b40-899e-1a1b8d164365"},{"cell_type":"markdown","metadata":{"id":"58085944-b951-4c9b-bdeb-0ccc60c2a3b3"},"source":["# 데이터 로딩"],"id":"58085944-b951-4c9b-bdeb-0ccc60c2a3b3"},{"cell_type":"markdown","source":["**텍스트 전처리 함수**\n","\n","> prepare_sample_text 함수는 데이터셋의 각 예제를 전처리하는데 사용됨. 아래 주어진 템플릿으로 텍스트를 생성함."],"metadata":{"id":"ceujqBmdUoJr"},"id":"ceujqBmdUoJr"},{"cell_type":"code","execution_count":null,"metadata":{"id":"b49c3470-480e-4ff2-b2c5-fcf1d3a13fba"},"outputs":[],"source":["def prepare_sample_text(example):\n","    \"\"\"Prepare the text from a sample of the dataset.\"\"\"\n","\n","    prompt_template = \"\"\"###System;{System}\n","    ###User;{User}\n","    ###Midm;{Midm}\"\"\"\n","\n","    default_system_msg = (\n","        \"너는 사용자가 작성한 리뷰의 긍정 또는 부정을 판단해야 한다.\"\n","    )\n","\n","    text = (\n","        prompt_template.format(System=default_system_msg,\n","                               User=example[\"document\"],\n","                               Midm=\"긍정\" if example[\"label\"]==1 else \"부정\"\n","                               )\n","    )\n","\n","    return text"],"id":"b49c3470-480e-4ff2-b2c5-fcf1d3a13fba"},{"cell_type":"markdown","source":["**훈련 데이터셋 생성 함수**\n","\n","\n","> create_train_datasets 함수는 훈련 데이터셋을 생성함. 훈련 데이터 개수를 지정하고 토큰화하여 모델 훈련에 사용될 수 있도록 데이터셋 구성함.\n","\n"],"metadata":{"id":"L6ZySGtGVQuK"},"id":"L6ZySGtGVQuK"},{"cell_type":"code","execution_count":null,"metadata":{"id":"5d9abfb4-339d-414b-855b-ced51631b752"},"outputs":[],"source":["def create_train_datasets(tokenizer, args):\n","    dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir)\n","    train_data = dataset['train'].select(range(150000))\n","\n","    chars_per_token = chars_token_ratio(train_data, tokenizer)\n","    print(f\"The character to token ratio of the dataset is: {chars_per_token:.2f}\")\n","\n","    train_dataset = ConstantLengthDataset(\n","        tokenizer,\n","        train_data,\n","        formatting_func=prepare_sample_text,\n","        infinite=True,\n","        seq_length=args.seq_length,\n","        chars_per_token=chars_per_token,# 부가정보, 컨트롤하는 정보\n","    )\n","\n","    return train_dataset"],"id":"5d9abfb4-339d-414b-855b-ced51631b752"},{"cell_type":"markdown","source":["**테스트 데이터셋 생성**\n","\n","> create_valid_datasets 함수는 테스트 데이터셋을 생성함. 테스트 데이터 개수를 지정하고 토큰화하여 모델 추론에 사용될 수 있도록 데이터셋 구성함.\n","\n"],"metadata":{"id":"Q_aYVx7QWE2f"},"id":"Q_aYVx7QWE2f"},{"cell_type":"code","execution_count":7,"metadata":{"id":"rwC9GQ3HHP_Z","executionInfo":{"status":"ok","timestamp":1702211579847,"user_tz":-540,"elapsed":264,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[],"source":["def create_valid_datasets(tokenizer, args):\n","    dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir) # nsmc 데이터셋을 허깅페이스에서 가져옴\n","    valid_data = dataset['test']\n","    valid_dataset = valid_data.select(range(1000))\n","\n","    return valid_dataset"],"id":"rwC9GQ3HHP_Z"},{"cell_type":"markdown","metadata":{"id":"2e7ef79d-a354-4c80-9435-c130ffed9e32"},"source":["# 미세 튜닝용 모델 로딩"],"id":"2e7ef79d-a354-4c80-9435-c130ffed9e32"},{"cell_type":"code","execution_count":8,"metadata":{"id":"bf4c65e0-5ffa-4a20-9e78-6c1f030572ff","executionInfo":{"status":"ok","timestamp":1702211580886,"user_tz":-540,"elapsed":5,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[],"source":["script_args = ScriptArguments(\n","    num_workers=2,\n","    seq_length=512,\n","    dataset_name='nsmc',\n","    model_name='meta-llama/Llama-2-7b-chat-hf',\n","    )"],"id":"bf4c65e0-5ffa-4a20-9e78-6c1f030572ff"},{"cell_type":"code","execution_count":9,"metadata":{"id":"372c64be-8fc0-4cc8-bda8-92ecf3632cc3","executionInfo":{"status":"ok","timestamp":1702211582001,"user_tz":-540,"elapsed":5,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[],"source":["script_args.training_args.logging_steps = 100\n","script_args.training_args.max_steps = 1600\n","script_args.training_args.output_dir = '/gdrive/MyDrive/nlp/lora-llama-2-7b-nsmc'\n","script_args.training_args.run_name = 'llama-2-7b-nsmc'"],"id":"372c64be-8fc0-4cc8-bda8-92ecf3632cc3"},{"cell_type":"code","execution_count":null,"metadata":{"id":"bac62c01-21ef-491e-a686-cf4988186c58","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702210364974,"user_tz":-540,"elapsed":7,"user":{"displayName":"장예린","userId":"14823696343626614035"}},"outputId":"3210deee-623f-4cc5-81c4-e9b1081a605b"},"outputs":[{"output_type":"stream","name":"stdout","text":["ScriptArguments(cache_dir=None, model_name='meta-llama/Llama-2-7b-chat-hf', dataset_name='nsmc', seq_length=512, num_workers=2, training_args=TrainingArguments(\n","_n_gpu=1,\n","adafactor=False,\n","adam_beta1=0.9,\n","adam_beta2=0.999,\n","adam_epsilon=1e-08,\n","auto_find_batch_size=False,\n","bf16=False,\n","bf16_full_eval=False,\n","data_seed=None,\n","dataloader_drop_last=False,\n","dataloader_num_workers=0,\n","dataloader_pin_memory=True,\n","ddp_backend=None,\n","ddp_broadcast_buffers=None,\n","ddp_bucket_cap_mb=None,\n","ddp_find_unused_parameters=None,\n","ddp_timeout=1800,\n","debug=[],\n","deepspeed=None,\n","disable_tqdm=False,\n","dispatch_batches=None,\n","do_eval=False,\n","do_predict=False,\n","do_train=False,\n","eval_accumulation_steps=None,\n","eval_delay=0,\n","eval_steps=None,\n","evaluation_strategy=no,\n","fp16=True,\n","fp16_backend=auto,\n","fp16_full_eval=False,\n","fp16_opt_level=O1,\n","fsdp=[],\n","fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},\n","fsdp_min_num_params=0,\n","fsdp_transformer_layer_cls_to_wrap=None,\n","full_determinism=False,\n","gradient_accumulation_steps=2,\n","gradient_checkpointing=False,\n","gradient_checkpointing_kwargs=None,\n","greater_is_better=None,\n","group_by_length=False,\n","half_precision_backend=auto,\n","hub_always_push=False,\n","hub_model_id=None,\n","hub_private_repo=False,\n","hub_strategy=every_save,\n","hub_token=<HUB_TOKEN>,\n","ignore_data_skip=False,\n","include_inputs_for_metrics=False,\n","include_tokens_per_second=False,\n","jit_mode_eval=False,\n","label_names=None,\n","label_smoothing_factor=0.0,\n","learning_rate=0.0001,\n","length_column_name=length,\n","load_best_model_at_end=False,\n","local_rank=0,\n","log_level=passive,\n","log_level_replica=warning,\n","log_on_each_node=True,\n","logging_dir=./results/runs/Dec10_12-12-43_fffde4d9d272,\n","logging_first_step=False,\n","logging_nan_inf_filter=True,\n","logging_steps=100,\n","logging_strategy=steps,\n","lr_scheduler_type=cosine,\n","max_grad_norm=0.3,\n","max_steps=1600,\n","metric_for_best_model=None,\n","mp_parameters=,\n","neftune_noise_alpha=None,\n","no_cuda=False,\n","num_train_epochs=1,\n","optim=paged_adamw_32bit,\n","optim_args=None,\n","output_dir=/gdrive/MyDrive/nlp/lora-llama-2-7b-nsmc,\n","overwrite_output_dir=False,\n","past_index=-1,\n","per_device_eval_batch_size=1,\n","per_device_train_batch_size=1,\n","prediction_loss_only=False,\n","push_to_hub=False,\n","push_to_hub_model_id=None,\n","push_to_hub_organization=None,\n","push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n","ray_scope=last,\n","remove_unused_columns=False,\n","report_to=['wandb'],\n","resume_from_checkpoint=None,\n","run_name=llama-2-7b-nsmc,\n","save_on_each_node=False,\n","save_safetensors=True,\n","save_steps=500,\n","save_strategy=epoch,\n","save_total_limit=20,\n","seed=42,\n","skip_memory_metrics=True,\n","split_batches=False,\n","tf32=None,\n","torch_compile=False,\n","torch_compile_backend=None,\n","torch_compile_mode=None,\n","torchdynamo=None,\n","tpu_metrics_debug=False,\n","tpu_num_cores=None,\n","use_cpu=False,\n","use_ipex=False,\n","use_legacy_prediction_loop=False,\n","use_mps_device=False,\n","warmup_ratio=0.03,\n","warmup_steps=0,\n","weight_decay=0.05,\n","), packing=True, peft_config=LoraConfig(peft_type=<PeftType.LORA: 'LORA'>, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type='CAUSAL_LM', inference_mode=False, r=16, target_modules={'v_proj', 'o_proj', 'gate_proj', 'down_proj', 'q_proj', 'up_proj', 'k_proj'}, lora_alpha=16, lora_dropout=0.1, fan_in_fan_out=False, bias='none', modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={}), merge_with_final_checkpoint=False)\n"]}],"source":["print(script_args)"],"id":"bac62c01-21ef-491e-a686-cf4988186c58"},{"cell_type":"markdown","source":["**4비트 양자화**\n","\n","\n","> 데이터를 4비트로 양자화하여 로드함\n","\n"],"metadata":{"id":"bnQg8GPOW9Lm"},"id":"bnQg8GPOW9Lm"},{"cell_type":"code","execution_count":null,"metadata":{"id":"1ff1422e-184d-4438-b033-40ae8bdaa5fd"},"outputs":[],"source":["bnb_config = BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch.bfloat16,\n",")"],"id":"1ff1422e-184d-4438-b033-40ae8bdaa5fd"},{"cell_type":"markdown","source":["**사전 훈련된 모델 로딩**"],"metadata":{"id":"DdtsgFvYXh0L"},"id":"DdtsgFvYXh0L"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":474,"referenced_widgets":["c6c8840bc47d4b31ab226acba47b8a9e","5ac35d39b200478987e81076152e52ec","9140b990f3ca40bb92561c48aac357b1","717bc9b53de3428980dc059b993c214f","ebe913670f634f59a399895e346be7b4","e0cdeffa16c64838a4d8d507cdaa4d72","25f9e15dc6ac47299c76b651f59380c8","ec652cfec6a64266b6339454141ebad6","18931575b84f48a18bb79202469a3b5d","a4a56d1ecc3c4200a59e42a9b3a97efd","bd2f88bcb17046858875562e4f447bf1","cbefc2c441df44eb9321ebf4cbf2e2aa","1b0d99531e704f9893fd0676c555b7d2","200fa25ba1924355a2b6c3a5a9ac7d45","3c6dc0fe54c14c4996582667dbc5b2ae","3b93e289d4f74f2bbe744632f1fc9c67","6ac7974baf694a6493d0819ad4304e34","816a9ec97ed54bb5ae9a98b2e642d7c2","f9446046961d496f8c7450616aab7e18","dfb69b0cedd14488bf4f300eb41dee6f","dc9fb91e6e954ecbb8cffc78940561be","f07897f4dafa4222ae22ca7a973c10a1","fa6c5de8500b44ec9961aad365ff8d25","289e2a32b9ae42c5b811b4034fe1d38a","cabf1356d0244ff09b808e6dee37ea14","afeaf539c9764fdf85ecb71c1553ca6c","bc17c17607284f20b4a3388b99cce889","eb346643690542c5aabeab371c1d61cb","878462bb00154efa9b1a0dfd3baf4ae6","cfb69d4014704b1a916d0cc434135212","49b710c017be433895a1d0d8c71d8adc","dae6aa543b9745b19d4983fddd1b8834","ee6c85e0baae4dea97d26de8e37e767c","e6b5bb7bca7e48a599fe2c64c645c065","a6ece2420245456cb5d961196ca161ee","5ce21776a2724a788d1285b608aba2a2","9ea303616a744ec597088612bcc18cb5","628287504c34499998499eb07f7985b4","f3f7c72db4f9430193f4e86889081423","f6dd43127b334b6ab10a0a6234c6ab03","46749f4056a44a86af61ab9242981666","fe385de2e49546929361d18eb8c34ddd","cf02901687ae4ab3a2731e9541f27e45","acf48c19581949fca30ea47f2bb05799","a13c642d517a4da6ab2eee20a31042ed","ad2be60729ae4fb2802031c8099fb8e2","7cee960286a04d7fa9b422cc7fa15bfd","8d4b034e391b4de8a55bc0038673154b","c2b45a79886c4bd197b73a180a8f9c6a","d407e47d57964a34ad585ab3d8b90f43","5f0504e7abf146f79748674c5914eed0","220e7aac0c3d49f3a7a13cfb5b8efb83","1f0ab297b47946b298a5988c6e4e6f0f","33c022d8cd9b4bdfab491dc14480e89e","41e0c9bf229142e19e9b44371101cb67","57ef26de07ab4a56a2bc3fe367076bf9","cb6855c833b541418bdfbb2e1ecf2037","c324e3dba44c4080a7f72ba20a9cec72","98e10e3e886d42aa90c5aa2e8ff68562","d19c37df09764b21a7d82358853ec263","ba745ba50fc146318ae402edf8f7cab7","cc0509b05ad243c2a387c303b06bcdd4","ea9663cf522c42bb810211eb7ff3a569","e2e560cd6877437eb4019c64d8eac14d","4405017847274c4981c735407429aa4a","ab014933e59d42e88d2978aee1af9fe5","df3642ffe5fb462ea6f3b1e78afe302a","2f654a8328484667bb6a4132ade9d75a","11b2069987a54e088c05f9172a5aa6bf","06532cd4e1644ee98564142ddd9e968b","858f0b4eff354b8083344155e9e8c572","1e0b7501bb9249288fbc8aada802f362","2380d42c7bdc44b5898d9f4eaa036e84","7f4d8e1051474006b6cec07ac977e655","deb1135a3a104b0992dab9293c1c29a6","d7252d6a34c447fa9487d7369d12bc59","f915a3ba21f04b12955b953a88e1945d"]},"executionInfo":{"elapsed":155838,"status":"ok","timestamp":1702203515322,"user":{"displayName":"장예린","userId":"14823696343626614035"},"user_tz":-540},"id":"15c8425e-bb0b-40c5-bfe8-385bac699b9d","outputId":"a5285f64-4773-483b-e968-1c95a3f00b7f"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:472: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c6c8840bc47d4b31ab226acba47b8a9e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cbefc2c441df44eb9321ebf4cbf2e2aa"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fa6c5de8500b44ec9961aad365ff8d25"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e6b5bb7bca7e48a599fe2c64c645c065"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a13c642d517a4da6ab2eee20a31042ed"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"57ef26de07ab4a56a2bc3fe367076bf9"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:374: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/188 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"df3642ffe5fb462ea6f3b1e78afe302a"}},"metadata":{}}],"source":["base_model = AutoModelForCausalLM.from_pretrained(\n","    script_args.model_name,\n","    quantization_config=bnb_config,\n","    device_map=\"auto\",  # {\"\": Accelerator().local_process_index},\n","    trust_remote_code=True,\n","    use_auth_token=True,\n","    cache_dir=script_args.cache_dir,\n",")\n","base_model.config.use_cache = False"],"id":"15c8425e-bb0b-40c5-bfe8-385bac699b9d"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":463,"status":"ok","timestamp":1702203518582,"user":{"displayName":"장예린","userId":"14823696343626614035"},"user_tz":-540},"id":"E9D239NqbDba","outputId":"adff55b6-5c0f-45f6-b67e-090bbdc2d470"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LlamaForCausalLM(\n","  (model): LlamaModel(\n","    (embed_tokens): Embedding(32000, 4096)\n","    (layers): ModuleList(\n","      (0-31): 32 x LlamaDecoderLayer(\n","        (self_attn): LlamaAttention(\n","          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (k_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (v_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (rotary_emb): LlamaRotaryEmbedding()\n","        )\n","        (mlp): LlamaMLP(\n","          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n","          (act_fn): SiLUActivation()\n","        )\n","        (input_layernorm): LlamaRMSNorm()\n","        (post_attention_layernorm): LlamaRMSNorm()\n","      )\n","    )\n","    (norm): LlamaRMSNorm()\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",")"]},"metadata":{},"execution_count":16}],"source":["base_model"],"id":"E9D239NqbDba"},{"cell_type":"code","execution_count":null,"metadata":{"id":"d37b485f-4fd3-404f-ab02-2bf3e93b3fc2"},"outputs":[],"source":["peft_config = script_args.peft_config"],"id":"d37b485f-4fd3-404f-ab02-2bf3e93b3fc2"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1702203521154,"user":{"displayName":"장예린","userId":"14823696343626614035"},"user_tz":-540},"id":"4420fcc4-2bac-413d-b7aa-89455c512419","outputId":"3a8811b0-81e2-4119-a0a8-3fa024436797"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LoraConfig(peft_type=<PeftType.LORA: 'LORA'>, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type='CAUSAL_LM', inference_mode=False, r=16, target_modules={'v_proj', 'down_proj', 'gate_proj', 'up_proj', 'o_proj', 'q_proj', 'k_proj'}, lora_alpha=16, lora_dropout=0.2, fan_in_fan_out=False, bias='none', modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={})"]},"metadata":{},"execution_count":18}],"source":["peft_config"],"id":"4420fcc4-2bac-413d-b7aa-89455c512419"},{"cell_type":"markdown","source":["**토크나이저 초기화 및 토크나이저, 모델 설정**"],"metadata":{"id":"SX7JSIJmYRau"},"id":"SX7JSIJmYRau"},{"cell_type":"code","execution_count":null,"metadata":{"id":"f47f9584-3988-46b8-a062-29dcde75a0e2","colab":{"base_uri":"https://localhost:8080/","height":257,"referenced_widgets":["58dd6d77c87d4daab64183c2727ff4fe","ae95407a63634d77b80feeba5f22aede","486cde6480564faa86d3df4733b10d75","815e7cd79c58498c9b7831e907ca7e42","a4ed213690134f8bae0da45f50dea8a2","0695ca14e74b4ea98657579b5b7ec1a0","d269811b86824ba8a189730ace7b2e0b","ea5f01aecee140d5be0cd18ae155e54a","bc573c3d295f437593fb6d4c1270129d","89b5296da1e14433b59aed2305c33cb5","685c6f3f11774a34a1bad49c1e9cb8cb","c239fb496d61456cb13e8e92eaca3d6c","c16fdde020da4f089b4ede2abd051160","44c0677669f848d8b6666190bd9cdef0","10819dc5a2694b71a9e3d186331c8671","f7bcf5a650bb46edaf0a6dd98550036f","5591183e008a4bd79fb32d14d8946d26","94e09d963d824d88abe69da4bca29475","0f16822d709346ceacd6a4f459b801fc","bdf16da1b94e4da8a78af7cc754e351f","59c4cde96b8f4ed2b472f017ceb2e699","c0fd7c8ba5994686b3a88eb9e82c4a7f","cd8c557f5c9541ad98f7dd4fa21746a7","151c5cc31c9540f89c69e209d7fcd2ba","c4efb1f1bb564e2dab237ca3c6f5101f","ec8930da60a24d9c9b5e13ddc8e21a9b","cd76d9a64a27421c93b40f0e4e1eb7c2","3d37939bd80e48aba4fc41bae2093bb9","f2d6edc946874a8b8747b3ec76715e78","b619d21595214a4787fd77d665df5e22","689ecaa008214f3ea46fbd65bcb3d994","02b5f8d420384a108868953f40637feb","8e2b19bbe8c84ca8ba9caa3b21e8b005","ed6be8806280426491b3b1cd71cedb7b","bc4c8f840aa64d68bbac538b2d0e6c86","57f36cc2701e431d9ffacfe6399d0d6f","fcea8641bf234a38866d9f0c34db9191","ea49921fb4d143d7bced513016f50dce","ad9630542834443a8948ffcc49496a22","6f1317c3935d4d04a1f486d473cde4c6","d6772d04818e46baafa9f7c22a6141cd","fc4e1ef4ed1147db8624537ec53dbf78","992a2a93c0584d45b4b42e680c594db9","caf4a47e6a9d409bbfb8467d8afba399"]},"executionInfo":{"status":"ok","timestamp":1702203523811,"user_tz":-540,"elapsed":1308,"user":{"displayName":"장예린","userId":"14823696343626614035"}},"outputId":"231667f1-2bd5-415e-e982-7c3f8c2c0a33"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"58dd6d77c87d4daab64183c2727ff4fe"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c239fb496d61456cb13e8e92eaca3d6c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cd8c557f5c9541ad98f7dd4fa21746a7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ed6be8806280426491b3b1cd71cedb7b"}},"metadata":{}}],"source":["tokenizer = AutoTokenizer.from_pretrained(\n","    script_args.model_name,\n","    trust_remote_code=True,\n","    cache_dir=script_args.cache_dir,\n",")\n","\n","if getattr(tokenizer, \"pad_token\", None) is None:\n","    tokenizer.pad_token = tokenizer.eos_token\n","tokenizer.padding_side = \"right\"  # Fix weird overflow issue with fp16 training\n","\n","base_model.config.pad_token_id = tokenizer.pad_token_id"],"id":"f47f9584-3988-46b8-a062-29dcde75a0e2"},{"cell_type":"code","execution_count":null,"metadata":{"id":"abd17c83-ab8d-44cb-b69b-fc0936c2cec5"},"outputs":[],"source":["training_args = script_args.training_args"],"id":"abd17c83-ab8d-44cb-b69b-fc0936c2cec5"},{"cell_type":"markdown","source":["**데이터셋 생성**"],"metadata":{"id":"_Z1y6UmwYjrh"},"id":"_Z1y6UmwYjrh"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":538,"referenced_widgets":["76336247e8824927a47ed05daf615683","d9a6b7b9caad445dbdf90689a0cb7f71","02edc505de454f4e85e9ede78893ecb1","f4ff88e401454dfb931f3a457663653a","9bb4bbd9138842a4920e956b86510e67","3ed0e20fc58b4071977cac6e268b6700","4d1744695c204e07888a6de291d8afcd","eee815d398b84b6687de413b6d0e72f3","b38852d53677406bb0cef96b3960ddb7","1159994aedda4c08bdf9207c6570f228","e2c49dd4a9e14334bc859c9ed8381ef9","297e685f986448fba4e64d27ee398fda","e3047d914e86418bb0db6b653b90b2c0","b693956338fb439e8d2ac28ef2e1c050","50fb3bfc63fe4a3a92662d62c3281866","10818c788e534fdbb025f7762f5362b2","10e40129301f4f79bcd3ab8469477a36","bebde8fbb9e44b858064920bb1c832af","10438eb52b4440f3b6001adc927e69e1","caa34004671740328e6c32c77441eba2","3241609dd1ca4041a1409dcf8e3949bc","6ecba01f0e884fbe8e24c083eb8faab7","9358a586fe014259ac3cd1512d545148","2d8be99082e5421b86cdd7358ea000ba","bf1c1ece9d314647b867ecf7f16e719f","23e842caa50244c4979d3c3d59650123","3d30e9e73a064f908c5ab368f7fa2d03","de4a77a3c19044fbb8a52e4138dbcd0c","708a4f038fe94626b6eb2ee59e69ad68","5b153898ada442ecae9612d902210843","68ffcc14460f4e47ae86b5e3a957c0be","f92ada08cd4b45999f6eab1e0e7323fc","02d90a2908744d5592915d86df57fa81","13acac1e74b04b40ac61b2a9a3babcbc","3c432a98afeb4fc7b98b9bf252a7d2a0","0357a655cc70450d820ba8c2718fb24e","81421cd32c7b4cddbb961b33e37e9d18","916a31834acf4793904d9cfdb253302b","9f589a4e514f4010bc4674ced41ad1cf","e0f325552cff4d8f9a59d2a95672a934","6b9b80688a354c03a76306a3a779fed4","6d6ecbeb363545f2821fab40906a41b2","9933063881dc446a976e241660e52277","0d125c349fae4a3ba601ec6c1bf35cac","05478bf5bb4f419f8a6b6d526f9c68fc","e5b3121d4b484c08a2c386008dc7cffc","f376d2e3e941461e8985a839c4918d17","e2899146e2034d128a4cb29db6aa622d","073db9d1aa91450c94f8113fea49d2c7","32ad27d79e7944d3a6b59ff7ade58fc3","2e04543385614d7883ad484a919edacd","d7899c00c01c46b98e1a68ba58469bfd","459e6a07948b47c8bb6c196540d26e4e","24ade9915ab54b7bbc12259348b8ad4c","8e308d594fd5468d834724c35f3e5bf2","64aafcb081e641aa8fb91228aed90447","cc7a876390d44011b4d94722b0a3c377","fef47ee7112548148bda20277f41183d","5e80dc9d848b45169feea1d2f8dddee7","8bc8ee1ca3d5487289fd470bd70bb5e8","7bcde2d87a3f4083898405892128264a","f3b059e59e27457bbefe6b0bef409f5c","97eac8266dfc41aeaab04f264736e51d","e47976760a034162a85422e84b73da68","d0c0064794584bf690fa63f97b1d75f3","dd1ee03146b549969b784cf4c28f756d","ca221680c25742cda9cf6d245ac7d917","1588e190344f4e81a4adf4d4f5610ad0","b7d575b18d3e40359152bf98e0dcc7fd","cb18b4d90b7c40408e59dc42e1febf51","3e67369e3ca741a99a8890ddf086031b","c5c0e7c34212446f96d5bbbf9871afef","6217c7a1004b4fc0892358440c5feb4a","e56a67e354464cd1832658c1f993f829","424a61cf49ef4aba8885a873ef391901","ec4533c5f30c4546b0e441b9cec54004","7cc9e0c07b684cf1bb910af182cb310d","a8ec0c3bb26b4e1fb476f4baa249600b","e76effd426624a6191b00a2cc6cb6111","d122de3bdd4844f6b80ebdff0417e756","ad9c87514e794bc2a5c05888e2a657e7","e970af02c8e14c1b955078ac09af863a","c339f9945fbb4e1aa9294f46baabc7de","f080f6c26aa44d5abf37f74d5b17bd15","66344bd84055415dbfc5b4aae7d5d3d5","bdde48004cc54ebf96197c32c8ad8750","7430d7692b504a30bfff1b78d2df4525","39fabc4024ba4f0a88c7e34990f2cd68","8b3859f97a424b12bdd2e63692008356","803aa298eadd45608a463d6f8c9686a9","97a3f712342a48bda3d1113481b179c3","3adffacc1be74787a8194687724e821f","b22fbd65319f410e8087e2eedce52ec3","ac1087e346204429a528b85f7e2ccaa6","8357e8bef9364706864ec7bd4248c3b0","cdddab8b975844f0a59193b9fdffdf96","c9174fac7f7d46fe8c927374b6aa4851","f02eb3923f654da1a474bef0cfb00ab4","1796e0d526a245d3ba1bd10707cdd7d4"]},"executionInfo":{"elapsed":13912,"status":"ok","timestamp":1702203543874,"user":{"displayName":"장예린","userId":"14823696343626614035"},"user_tz":-540},"id":"62e8139f-5179-4c75-84a7-0c818ab0a35a","outputId":"8c9e80cc-cd90-4718-ac66-5f2831b9c405"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/3.18k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"76336247e8824927a47ed05daf615683"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/1.67k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"297e685f986448fba4e64d27ee398fda"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/3.74k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9358a586fe014259ac3cd1512d545148"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"13acac1e74b04b40ac61b2a9a3babcbc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/6.33M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"05478bf5bb4f419f8a6b6d526f9c68fc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/2.12M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"64aafcb081e641aa8fb91228aed90447"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ca221680c25742cda9cf6d245ac7d917"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/150000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a8ec0c3bb26b4e1fb476f4baa249600b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/50000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8b3859f97a424b12bdd2e63692008356"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["100%|██████████| 400/400 [00:00<00:00, 2204.97it/s]\n","/usr/local/lib/python3.10/dist-packages/trl/trainer/utils.py:548: UserWarning: The passed formatting_func has more than one argument. Usually that function should have a single argument `example` which corresponds to the dictionary returned by each element of the dataset. Make sure you know what you are doing.\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["The character to token ratio of the dataset is: 0.87\n"]}],"source":["train_dataset = create_train_datasets(tokenizer, script_args)\n","valid_dataset = create_valid_datasets(tokenizer, script_args)"],"id":"62e8139f-5179-4c75-84a7-0c818ab0a35a"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":314,"status":"ok","timestamp":1702203546749,"user":{"displayName":"장예린","userId":"14823696343626614035"},"user_tz":-540},"id":"000314e9-f10b-4685-8da6-0511494a9eb4","outputId":"3092fb46-cd56-4c8e-b983-ae2cdd57224d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["150000"]},"metadata":{},"execution_count":22}],"source":["len(train_dataset)"],"id":"000314e9-f10b-4685-8da6-0511494a9eb4"},{"cell_type":"markdown","source":["**SFTTrainer 초기화**\n","\n","\n",">\n","\n"],"metadata":{"id":"8dbpPmzXYzOV"},"id":"8dbpPmzXYzOV"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1121,"status":"ok","timestamp":1702203550833,"user":{"displayName":"장예린","userId":"14823696343626614035"},"user_tz":-540},"id":"4ba80a64-0ec7-4b29-ac95-7b3d34549f17","outputId":"02764c28-04d4-438c-928e-3adaea6f5ae5"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:267: UserWarning: You passed `packing=True` to the SFTTrainer, and you are training your model with `max_steps` strategy. The dataset will be iterated until the `max_steps` are reached.\n","  warnings.warn(\n"]}],"source":["trainer = SFTTrainer(\n","    model=base_model,\n","    formatting_func=prepare_sample_text,\n","    train_dataset=train_dataset,\n","    eval_dataset=valid_dataset,\n","    peft_config=peft_config,\n","    packing=script_args.packing,\n","    max_seq_length=script_args.seq_length,\n","    tokenizer=tokenizer,\n","    args=training_args,\n",")"],"id":"4ba80a64-0ec7-4b29-ac95-7b3d34549f17"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":387,"status":"ok","timestamp":1702203556810,"user":{"displayName":"장예린","userId":"14823696343626614035"},"user_tz":-540},"id":"gw9xbeUgbZEo","outputId":"b46976fd-dd83-4257-9540-b30622eb29ab"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LlamaForCausalLM(\n","  (model): LlamaModel(\n","    (embed_tokens): Embedding(32000, 4096)\n","    (layers): ModuleList(\n","      (0-31): 32 x LlamaDecoderLayer(\n","        (self_attn): LlamaAttention(\n","          (q_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.2, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (k_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.2, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (v_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.2, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (o_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.2, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (rotary_emb): LlamaRotaryEmbedding()\n","        )\n","        (mlp): LlamaMLP(\n","          (gate_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.2, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=11008, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (up_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.2, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=11008, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (down_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=11008, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.2, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=11008, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (act_fn): SiLUActivation()\n","        )\n","        (input_layernorm): LlamaRMSNorm()\n","        (post_attention_layernorm): LlamaRMSNorm()\n","      )\n","    )\n","    (norm): LlamaRMSNorm()\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",")"]},"metadata":{},"execution_count":24}],"source":["base_model"],"id":"gw9xbeUgbZEo"},{"cell_type":"markdown","source":["**base_model 장치 확인**\n","\n","\n","> device 속성은 모델이 CPU에 있는지 GPU에 있는지 알려줌.\n","\n"],"metadata":{"id":"wbWUTqvWZOsR"},"id":"wbWUTqvWZOsR"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1702203559588,"user":{"displayName":"장예린","userId":"14823696343626614035"},"user_tz":-540},"id":"cuc1RNfUK_sP","outputId":"5e91cc85-56f8-4533-ea29-a7525d461480"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["device(type='cuda', index=0)"]},"metadata":{},"execution_count":25}],"source":["base_model.device\n"],"id":"cuc1RNfUK_sP"},{"cell_type":"markdown","source":["**학습 가능한 파라미터 정보**"],"metadata":{"id":"Hga34BUfZgwV"},"id":"Hga34BUfZgwV"},{"cell_type":"code","execution_count":null,"metadata":{"id":"edb204be-ec15-4800-af49-6cfbad2f7f9a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702203561734,"user_tz":-540,"elapsed":370,"user":{"displayName":"장예린","userId":"14823696343626614035"}},"outputId":"9b1960cc-542f-4460-dc63-7127552a79ee"},"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 39976960 || all params: 3540389888 || trainable%: 1.1291682911958425\n"]}],"source":["print_trainable_parameters(base_model)"],"id":"edb204be-ec15-4800-af49-6cfbad2f7f9a"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":866},"id":"tFeEyBJnCi0C","outputId":"04e4ea66-f11d-4545-f87b-afc2ad3a420c","executionInfo":{"status":"ok","timestamp":1702209686325,"user_tz":-540,"elapsed":6123503,"user":{"displayName":"장예린","userId":"14823696343626614035"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","        window._wandbApiKey = new Promise((resolve, reject) => {\n","            function loadScript(url) {\n","            return new Promise(function(resolve, reject) {\n","                let newScript = document.createElement(\"script\");\n","                newScript.onerror = reject;\n","                newScript.onload = resolve;\n","                document.body.appendChild(newScript);\n","                newScript.src = url;\n","            });\n","            }\n","            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n","            const iframe = document.createElement('iframe')\n","            iframe.style.cssText = \"width:0;height:0;border:none\"\n","            document.body.appendChild(iframe)\n","            const handshake = new Postmate({\n","                container: iframe,\n","                url: 'https://wandb.ai/authorize'\n","            });\n","            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n","            handshake.then(function(child) {\n","                child.on('authorize', data => {\n","                    clearTimeout(timeout)\n","                    resolve(data)\n","                });\n","            });\n","            })\n","        });\n","    "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n","\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n","wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"]},{"name":"stdout","output_type":"stream","text":[" ··········\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.16.1"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Run data is saved locally in <code>/content/wandb/run-20231210_101937-9u485tp9</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Syncing run <strong><a href='https://wandb.ai/yerini/huggingface/runs/9u485tp9' target=\"_blank\">llama-2-7b-nsmc</a></strong> to <a href='https://wandb.ai/yerini/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[" View project at <a href='https://wandb.ai/yerini/huggingface' target=\"_blank\">https://wandb.ai/yerini/huggingface</a>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[" View run at <a href='https://wandb.ai/yerini/huggingface/runs/9u485tp9' target=\"_blank\">https://wandb.ai/yerini/huggingface/runs/9u485tp9</a>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1600' max='1600' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1600/1600 1:41:39, 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>100</td>\n","      <td>1.185700</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>0.864200</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>0.819000</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>0.793300</td>\n","    </tr>\n","    <tr>\n","      <td>500</td>\n","      <td>0.794500</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>0.781300</td>\n","    </tr>\n","    <tr>\n","      <td>700</td>\n","      <td>0.812400</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>0.749900</td>\n","    </tr>\n","    <tr>\n","      <td>900</td>\n","      <td>0.784800</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.736300</td>\n","    </tr>\n","    <tr>\n","      <td>1100</td>\n","      <td>0.763900</td>\n","    </tr>\n","    <tr>\n","      <td>1200</td>\n","      <td>0.783400</td>\n","    </tr>\n","    <tr>\n","      <td>1300</td>\n","      <td>0.735200</td>\n","    </tr>\n","    <tr>\n","      <td>1400</td>\n","      <td>0.760800</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>0.778000</td>\n","    </tr>\n","    <tr>\n","      <td>1600</td>\n","      <td>0.757700</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=1600, training_loss=0.8062745618820191, metrics={'train_runtime': 6123.2885, 'train_samples_per_second': 0.523, 'train_steps_per_second': 0.261, 'total_flos': 6.53458201903104e+16, 'train_loss': 0.8062745618820191, 'epoch': 0.02})"]},"metadata":{},"execution_count":27}],"source":["trainer.train()\n"],"id":"tFeEyBJnCi0C"},{"cell_type":"markdown","metadata":{"id":"76sRe172fGlm"},"source":["구글 코랩 T-4 GPU: 1:37:34 예상시간\n","- 총 1,600 스텝 필요\n","- 하지만 이보다 일찍 종료됨 약 900번 미만 스텝에서 종료됨\n","\n","시퀀스 길이 512의 경우\n","- 14.4 G / 15.0 G 사용\n","- 메모리 오버플로우 발생시 512보다 줄일 것"],"id":"76sRe172fGlm"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":326,"status":"ok","timestamp":1702210266465,"user":{"displayName":"장예린","userId":"14823696343626614035"},"user_tz":-540},"id":"3Y4FQSyRghQt","outputId":"20a0d171-932c-452f-cb3b-9fb09d1ef0cb"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/gdrive/MyDrive/nlp/lora-llama-2-7b-nsmc'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":28}],"source":["script_args.training_args.output_dir"],"id":"3Y4FQSyRghQt"},{"cell_type":"code","execution_count":null,"metadata":{"id":"49f05450-da2a-4edd-9db2-63836a0ec73a"},"outputs":[],"source":["trainer.save_model(script_args.training_args.output_dir)"],"id":"49f05450-da2a-4edd-9db2-63836a0ec73a"},{"cell_type":"markdown","metadata":{"id":"652f307e-e1d7-43ae-b083-dba2d94c2296"},"source":["# 추론 테스트"],"id":"652f307e-e1d7-43ae-b083-dba2d94c2296"},{"cell_type":"code","execution_count":null,"metadata":{"id":"ea8a1fea-7499-4386-9dea-0509110f61af"},"outputs":[],"source":["from transformers import pipeline, TextStreamer"],"id":"ea8a1fea-7499-4386-9dea-0509110f61af"},{"cell_type":"code","execution_count":null,"metadata":{"id":"52626888-1f6e-46b6-a8dd-836622149ff5"},"outputs":[],"source":["instruction_prompt_template = \"\"\"###System;다음은 네이버 영화 리뷰들을 모아놓은 문장이다. 이를 분석하여 사용자가 작성한 영화 리뷰의 감정을 긍정 또는 부정으로 예측하라.\n","\n","### 리뷰 내용: {0} ### 분석 결과:\n","\"\"\"\n","\n","prompt_template = \"\"\"###System;{System}\n","###User;{User}\n","###Midm;\"\"\"\n","\n","default_system_msg = (\n","        \"너는 먼저 사용자가 작성한 영화 리뷰의 감정을 분석하는 에이전트이다. 이로부터 긍정 또는 부정을 판단해야 한다.\"\n",")"],"id":"52626888-1f6e-46b6-a8dd-836622149ff5"},{"cell_type":"code","execution_count":null,"metadata":{"id":"1919cf1f-482e-4185-9d06-e3cea1918416"},"outputs":[],"source":["def wrapper_generate(model, input_prompt):\n","    data = tokenizer(input_prompt, return_tensors=\"pt\")\n","    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n","    input_ids = data.input_ids[..., :-1]\n","    with torch.no_grad():\n","        pred = model.generate(\n","            input_ids=input_ids.cuda(),\n","            #input_ids = input_ids.to('cuda'),\n","            streamer=streamer,\n","            use_cache=True,\n","            max_new_tokens=float('inf'),\n","            temperature=0.5\n","        )\n","    decoded_text = tokenizer.batch_decode(pred, skip_special_tokens=True)\n","    return (decoded_text[0][len(input_prompt):])"],"id":"1919cf1f-482e-4185-9d06-e3cea1918416"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1537373,"status":"ok","timestamp":1701343970452,"user":{"displayName":"장예린","userId":"14823696343626614035"},"user_tz":-540},"id":"Lxptc3T1kDSO","outputId":"1604b12c-871e-4d41-f739-b7e54ad03091"},"outputs":[{"name":"stdout","output_type":"stream","text":[";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍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= {i: wrapper_generate(model=base_model, input_prompt=prompt_template.format(System=default_system_msg, User=example[\"document\"]))for i, example in enumerate(valid_dataset)}"],"id":"Lxptc3T1kDSO"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":398,"status":"ok","timestamp":1701265256607,"user":{"displayName":"장예린","userId":"08326973126538993419"},"user_tz":-540},"id":"fefd04ba-2ed8-4f84-bdd0-86d52b3f39f6","outputId":"642ae37a-4c0c-4385-f75c-20a8f0957242"},"outputs":[{"name":"stdout","output_type":"stream","text":["부정\n"]}],"source":["print(eval_dic[8])"],"id":"fefd04ba-2ed8-4f84-bdd0-86d52b3f39f6"},{"cell_type":"markdown","metadata":{"id":"3f471e3a-723b-4df5-aa72-46f571f6bab6"},"source":["# 미세튜닝된 모델 로딩 후 테스트"],"id":"3f471e3a-723b-4df5-aa72-46f571f6bab6"},{"cell_type":"code","source":["from transformers import pipeline, TextStreamer"],"metadata":{"id":"U5AcpecYRRlC","executionInfo":{"status":"ok","timestamp":1702211592479,"user_tz":-540,"elapsed":1132,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"id":"U5AcpecYRRlC","execution_count":10,"outputs":[]},{"cell_type":"markdown","source":["**프롬프트 정의**\n","\n","\n","> 사용자 영화 리뷰의 예측을 생성하기 위한 프롬프트 정의함.\n","\n"],"metadata":{"id":"qVr_AMtJa-AF"},"id":"qVr_AMtJa-AF"},{"cell_type":"code","source":["instruction_prompt_template = \"\"\"###System;다음은 네이버 영화 리뷰들을 모아놓은 문장이다. 이를 분석하여 사용자가 작성한 영화 리뷰의 감정을 긍정 또는 부정으로 예측하라.\n","\n","### 리뷰 내용: {0} ### 분석 결과:\n","\"\"\"\n","\n","prompt_template = \"\"\"###System;{System}\n","###User;{User}\n","###Midm;\"\"\"\n","\n","default_system_msg = (\n","        \"너는 사용자가 작성한 리뷰의 긍정 또는 부정을 판단해야 한다.\"\n",")"],"metadata":{"id":"nzxPwBS5RY1P","executionInfo":{"status":"ok","timestamp":1702211926639,"user_tz":-540,"elapsed":295,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"id":"nzxPwBS5RY1P","execution_count":11,"outputs":[]},{"cell_type":"markdown","source":["**wrapper_generate 함수**\n","\n","\n","> 사용자 영화 리뷰에 대한 입력 프롬프트 생성.\n","\n"],"metadata":{"id":"zkWiQd1NaFZz"},"id":"zkWiQd1NaFZz"},{"cell_type":"code","source":["def wrapper_generate(model, input_prompt):\n","    data = tokenizer(input_prompt, return_tensors=\"pt\")\n","    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n","    input_ids = data.input_ids[..., :-1]\n","    with torch.no_grad():\n","        pred = model.generate(\n","            input_ids=input_ids.cuda(),\n","            streamer=streamer,\n","            use_cache=True,\n","            max_new_tokens=float('inf'),\n","            temperature=0.5\n","        )\n","    decoded_text = tokenizer.batch_decode(pred, skip_special_tokens=True)\n","    return (decoded_text[0][len(input_prompt):])"],"metadata":{"id":"nPNJNH5TRcYQ","executionInfo":{"status":"ok","timestamp":1702211928380,"user_tz":-540,"elapsed":275,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"id":"nPNJNH5TRcYQ","execution_count":12,"outputs":[]},{"cell_type":"markdown","source":["**양자화 설정**\n","\n","\n","> 4비트로 양자화함.\n","\n"],"metadata":{"id":"XwPG_xZ3aXaL"},"id":"XwPG_xZ3aXaL"},{"cell_type":"code","execution_count":13,"metadata":{"id":"a43bdd07-7555-42b2-9888-a614afec892f","executionInfo":{"status":"ok","timestamp":1702211929706,"user_tz":-540,"elapsed":3,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[],"source":["bnb_config = BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch.bfloat16,\n",")"],"id":"a43bdd07-7555-42b2-9888-a614afec892f"},{"cell_type":"markdown","source":["**모델 불러오기**\n","\n","> 미세튜닝된 모델을 4비트로 양자화하여 저장된 모델을 가져옴.\n","\n"],"metadata":{"id":"m2z3YCa1aoP4"},"id":"m2z3YCa1aoP4"},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":409,"referenced_widgets":["2ca2aaecc8a840efaefc75732edf3150","b60614fe19f74a4c9d7abb2c7ee97c60","d76c9327a76d4493b3c08b5cd4979989","90f85d3fec14447b804eaee7d811484b","93e4e15bff034bdf8df6fdc9bc59fd6c","84adda03f9f64c7e8e231ca7d5ac8fcb","9738c4b8391d4b2ca924e7103b7c73d4","4c26d3a15fb2461aabe7c479be670986","8839e1c73a784a07ab8fa33bbd897954","dc3c665e1ed640e983b4f3b7b58116ca","117ced4159a649dc985348f31e885205","335d07c3d6a2438182810737c931335e","b141ac3b5c7d4e799766746ad37fb915","1addb8ce56704a1fa0c506d2572d7383","63bc7dda6fe6447c99d179e38ef5b921","33fbcbe6f6dd42d0a29f3e2d78e86196","5399a2f7adc1403ca53ea4e9d2e36138","d345e8b24fa94a199dff0bf155ef49c3","8506eee76253473a9e2e01478086678c","144f86491529409ba69a134ba7c1ff2a","e0a713ace1154e28903e40e08cf12d6b","54aa6d61cff147b4adb1cc995c73c817","8beb1f4112db47c5be9c125ec3c82b5d","b96b38b7dcc246e4a20a996b23dc8fa3","8b6ec68913f242b3b47a353ea7383052","f5c597be9e234b60825476b532144f45","ec892629b3c848349ae98ca6fcb00602","a0081fd895fb49f9a9ebf663f81180ed","050e43be284142dcacecb3be4faa36a7","46e7e3f527964b5f87d2a4f23e0f0326","295e7e1ba7824552924b3535ffa0136d","c201a0f6e66942da9c977e4349f947c6","7e6e490a92eb4d0ab368a4f667640939","215394b9a6a04653ab03a35d0135fe5d","0da18f01a0b1451a9d0719acfe64fbfe","b8708ff795f8411d9ebaefd4762fb10b","f37abd59649542c982fd9db4fee74fc4","05c7d7ed12e14bc4a4615f1122070b6c","61a2cbfc425a4493886f32c7f0b6565f","bc85c375435c48048270fb12899f609d","3d074657a7804e79a03d485dc3ffab27","6dd22ca989c24429b42d737019be2630","19a9e7016218438785d84e622a715d42","20540ca4e72f4314b03cb8395d289180","0bfa51ca810b4caa9aa7046ba74866dc","a3d010d441c74537934bd2d4d13a8204","1d3387e9e3644e4499d71336d38c5978","15631bb8aa2f4a6f86f712e08f4c27f5","b0de8cbef5434403a67947bff1f018c7","2916ff3c45524c208e142f8b570c8569","3f012298d2fc4a38b73cf0ec84e21a69","b79cd1783d8649a094db48678b6ef89d","88f1fbd3f6a345b8897c8d41eea55099","065d3296645b48b98baf4fb2063594c6","764eec174756419bbcf4b32734e0418e","9095199ed82f4d73acadb43366781167","95461d42509743d8b6790c99bb9f1e7d","6ecc5f2ba6c841a7970a55ccf2a93d24","17b99454204344688f41f98b87580b22","97cbf25ce3e04f32a3b1e431622d946c","43931f64cb304416bde19775db56d2e3","7addecb1b5624048bef936ed1872fb69","9de7301c3c0f46908606cb6f2f769bf5","4b46186d47ef428fa52ab14e5ca27f7d","2b9a14bbf98c4254976fcce5ee212b78","e0e15b3c2c0a40d98d10f2f0618b4f2d","4d88b9b66b424d6fa7315e3b3144a6fc","5450c2bc9f9c49f09cf734cd52bdd857","915d86f6b11c474fab6fc09d871445de","a3f8fc1a8ba14f1091b3044e570aab67","374bcae18dcf42ea9fe1b99dc4ebf46d","fd7c85a5399e40ba9a3dec8aa7d5c1cc","00464cd6e834424b8e1f26b3c5c71692","f4af0a41493f4803a733eff2c188c369","239672d958de4b579e9eec9a9065324a","e8dc05eca5ce451791dc8932d828282e","3a8bf67931774be2b5d9c723cddff124"]},"id":"39db2ee4-23c8-471f-89b2-bca34964bf81","outputId":"573909e3-e6c7-4192-898f-b57c11bd8eee","executionInfo":{"status":"ok","timestamp":1702212090601,"user_tz":-540,"elapsed":159918,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json: 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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"215394b9a6a04653ab03a35d0135fe5d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0bfa51ca810b4caa9aa7046ba74866dc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9095199ed82f4d73acadb43366781167"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/188 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4d88b9b66b424d6fa7315e3b3144a6fc"}},"metadata":{}}],"source":["trained_model = AutoPeftModelForCausalLM.from_pretrained(\n","    script_args.training_args.output_dir,\n","    quantization_config=bnb_config,\n","    device_map=\"auto\",\n","    cache_dir=script_args.cache_dir,\n",")"],"id":"39db2ee4-23c8-471f-89b2-bca34964bf81"},{"cell_type":"markdown","source":["**토크나이저 초기화**\n","\n","\n","> 지정된 모델에서 미리 훈련된 토크나이저를 로드함.\n","\n"],"metadata":{"id":"aZSH97Zmb3fK"},"id":"aZSH97Zmb3fK"},{"cell_type":"code","execution_count":15,"metadata":{"id":"b0b75ca4-730d-4bde-88bb-a86462a76d52","executionInfo":{"status":"ok","timestamp":1702212097846,"user_tz":-540,"elapsed":820,"user":{"displayName":"장예린","userId":"08326973126538993419"}},"colab":{"base_uri":"https://localhost:8080/","height":257,"referenced_widgets":["b8c0ed67113a4e80b84f6666f23099d4","797f0e1579f141918ed3dc829fbec677","3ef5a717d2b74c1ba283978ffaa35b62","204be735363f457c911389c8f1e6e39f","3b6eca7db05f47328b931f76079c34ef","6e301e443e4e480fa3b0c373abf2406e","1910cf65cb9748319d41c3d36037baf3","69423b28f6624a3bac61e7065caf78f2","bce7e88560f348b1b529011b1ef14216","d93ecffef8a544fab1de42d7f1e90f72","35f8c79833224eadaece352d3c1d9a38","f906532a16604e149b9cff76645ac75c","89641c5edc694ff98b6caf4d8dbe64ea","13c081422d564164b9bca0a53417ca4b","da2b3cfb50d74fab87c45adb6076dfec","da20532333994c8fac877fda7baacff7","30224f652f114c97a54289e5448d7662","b4bb9505d8e24b68b51e5c7ca7041e9e","3e18648fd7e242c2b876d46f4663bdf3","8543a8a6a57845af9f749be05ecd6cee","e212e3a0f59b41619beedc73dfa1d17b","b6c0dcca8af74eb4923dbeec5f5613b2","426a2f997e534a07bb608defdffd0e8b","156d9fb26dd24245bcfd7089ba149233","87ca02b2ae0742c8b4172ad140a6df0c","20a880bb787c4e259d3d21c8aab73b29","d8920e0146b24363b8dc6b5b33e41d47","617029a0c31244af9beb8d1b9396c609","0efa1acc3ae8423cbf4cfe56ac83ee2c","964e11f14ce44e04acc576886fece5db","141c770dd3f84613bad7cf862b5a3f2b","ebdc1d5bd7c1485284f1e53558275000","e97222f0b0cb45abaf74a20599fc9529","3ce1074ff3d9410eabba06c696cb6d28","eed104cb411c4a7eb728ff2803dc61c2","8f8194511023476cbbc136bb4b9495a1","b5018df79ad5412da2b9dc0edac4e7a5","b53f3be24fc645b0932ab40052f4ae8a","f30bb1ed570a4306aa3c1bc3ba8a0e9f","265f9ed416e84b91999936113ce1bd65","cd74d92f057f4050b8c2a74051d9ab5e","fd23284c49f747058df8fb74b24e9ea4","b42a508a99cc42de9264bb804d3e5aa7","4a514c42f06b4d3bbd61b3240efb8d04"]},"outputId":"29b3dd18-2101-4500-f002-f9dfd1045942"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 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AutoTokenizer.from_pretrained(\n","    script_args.model_name,\n","    trust_remote_code=True,\n","    cache_dir=script_args.cache_dir,\n",")\n","\n","if getattr(tokenizer, \"pad_token\", None) is None:\n","    tokenizer.pad_token = tokenizer.eos_token\n","tokenizer.padding_side = \"right\"  # Fix weird overflow issue with fp16 training\n","trained_model.config.pad_token_id = tokenizer.pad_token_id"],"id":"b0b75ca4-730d-4bde-88bb-a86462a76d52"},{"cell_type":"markdown","source":["**데이터셋 생성**\n","\n","\n","> 추론에 사용할 테스트 데이터 생성함.\n","\n"],"metadata":{"id":"qHcNDlVObcoa"},"id":"qHcNDlVObcoa"},{"cell_type":"code","execution_count":16,"metadata":{"id":"b5O6GD2BVr67","executionInfo":{"status":"ok","timestamp":1702212112233,"user_tz":-540,"elapsed":12498,"user":{"displayName":"장예린","userId":"08326973126538993419"}},"colab":{"base_uri":"https://localhost:8080/","height":473,"referenced_widgets":["eb8f992993b3446384204c1ec2293c4c","4659ae82bbc541b1b1fe5ac86f1f54cc","7d28bad462de45168d1b874a2fb1958f","2f7d9c7da2294f9e906897c51608abda","5b656c19d9a244d5a59e1089fc02b144","bb74b110db0946c18219b2184a9baf53","165644b063484559ac81b8f2c1864a8e","754ad1de97ab4d5d919cf21e164ab382","8acdc2206c96453aba78b0135eac77ff","57025c73a9994b93957ff9778417c619","d82207b7a792480a9740c5e187986c8e","dd4b9577f61f4d4a991e6f02934df083","452100f3be3f419ca63abf6759db2c79","f386c0cf38ae427e9481f0ea37405510","7067fe15e777404fab0659ae7ccafd78","a0e8d6377fc34ff7af7130ce8b5c6be2","10b16c8ff26142e197b8c7f286609742","186cb679924948f380b52a299df7a22f","3c2897cde14d4e06b3bf1b2fdaa11dd6","d09c9658cb904ab1805dfd2170e0ee3e","9a016a78ab404eb18cfb2a8a6b0385d1","a55d396641cf4e598b6d2f0f925ffd55","0ceaa8bf55364aad8ed743bea2594cef","2b4e3402ac3644fcbb3869a47e0492f2","691bddec0297480b8c212e0429395ef1","a54bffeac1ba4552bab9ef218c8b2a90","d5a95d52f9aa47679d447f5fe682d005","9a5029e9f9d24c39b8cc2e3c1c19646c","f97fc42540954b0e9e23a4425e31aba3","cde7293837084071bdeb5a65d4fbbf4a","ebe18a0a5b9f4fd1a687f09908aa9916","8cbf9570d35a497893402b8031d92b07","5a5d2ed3a3674c1abfd3774ab40426c0","320a7c0d91a145689768cf778fe7f85d","66af33db1fce49a5a4e16a6ac8308d78","cad7292862494c809df97923318681d2","f0276c0e06dd445a831082a4e9cfe937","e67fb23389e8422aba30a45db7b83065","282e47cdf9df431b8f75f1134da60ce8","da97fd9a85c3496fb281804ba7687d88","d57caacf16074527a032781e638bd590","0dd55cda962c4f03abfbe7b22628c5c3","a7e432cf046742ec95648d0ffb78c6d0","0713b13258074ad7ae31704a59fafc3c","8086210309074fc8ab0e6bc1f7c7466f","e634b02e7f014f09861038ca5f579489","9964f232b28f466a815488c82e5f9613","7137852e0a674a63b0cd1409d4d010b8","8d9a9ddaf76c497f86148660ffc357b9","a99c6a9013ba408b99f72e24deb255ee","ba9d74c019124f5e9bbe82ba41fb6af0","f7ebbe2932ac48ff8b84beb9f86c821b","1457193b34d04a2e8e29b7a4b3fc0b8e","a70e9bf4846b47b18b55a90ec67c5752","3eba7b852cf5477197745d708190bb70","7d366b10250744e3b6cfcc81f1794d07","ecf3fbedc71e4f59acbef86b44c3bf6e","bd2a3440af7d4e3a98f95b4b6603dc6f","6c5260e59cbb45ff974bc55f758c4f6b","33f4ba89668b41eca016b44d3bb5890e","839298e02e104f46b98fd1c85d55d85c","5196626f8597425c93e5fefb1c604305","161f6c502e4c4183998bcbc512be8aca","2923ebcff45e4201814a79b4b7849fd8","3472c498957148c18a244c0650e274f8","387b2e145301412caaea4b24979f72d6","62b9de5fdef24e45ae322e7a64bc4285","05309d077097486790404de34c335499","cbc7bf63d8b24b0faa5f0d2e232d4e85","62a335c9cc2241b1a0d0a1ba43ad80a2","ab7c6074f0814935b1f780cca1201e2d","8c10255ff49d4c819149da6a28bad88b","59f99687781a4c1ba0fed6561298ed75","9d50c47299af494c8852c01a796106b1","779f50edb94943ea8a7e4f3c4770bf65","d94f916a26bc4ffcb8a4e1539b75a0d8","f1a5b613630a41fd9d375713a20bd53c","464f4a1d4185407f80501d7512abbe9f","c5ef39639cfb48269c800b5b9fe2597b","50463372fcaf45e9a6bfb8082fc433c5","e8a4c9cf83e84bdbbb2c6bbe7186fa9a","1a8f69ebe0364a528bb697b82b8162c6","f787a3c4eec041e7bb22b8f197e2a6c1","c14ea0005dbd4a42b6f50b4ca75df58f","06ccbb8862514b6ea4596b9f69be9fc9","2312df9829af4c32bfd9e045f3337ff3","9859665df2634a429c31f52548ccb559","121ddc81bc7944c294a064a90e26b88b","4c63f5b266db4b85aaeadf11fc8025a9","6d2f903d329d4040aff1261199bc827b","a3df83b95bbe4611a83ac14270148372","8c176e5a1ece4460b7453fef8724f8d3","a8a5fdac98c848749765c1520c871829","23752b695ed24fdc8c874f6f706e4903","8dd8b001efa440a9b0376bf17c9ae160","38bcd7f771244a5bb84c4c30275b1a27","f4d538fa2bba41c49d24f7fb7391a3a1","ed7ed85a4c194bcda7967cd07f0c2df1","a279b8ed424244b18a999f8e47dba9ef"]},"outputId":"b18b5551-e5ba-4666-92d1-844449f0729a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/3.18k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eb8f992993b3446384204c1ec2293c4c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/1.67k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dd4b9577f61f4d4a991e6f02934df083"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/3.74k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0ceaa8bf55364aad8ed743bea2594cef"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"320a7c0d91a145689768cf778fe7f85d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/6.33M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8086210309074fc8ab0e6bc1f7c7466f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/2.12M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7d366b10250744e3b6cfcc81f1794d07"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"62b9de5fdef24e45ae322e7a64bc4285"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/150000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"464f4a1d4185407f80501d7512abbe9f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/50000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4c63f5b266db4b85aaeadf11fc8025a9"}},"metadata":{}}],"source":["valid_dataset = create_valid_datasets(tokenizer, script_args)"],"id":"b5O6GD2BVr67"},{"cell_type":"markdown","metadata":{"id":"X1tRCa4EiYXp"},"source":["추론 과정에서는 GPU 메모리를 약 5.5 GB 활용"],"id":"X1tRCa4EiYXp"},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e374555b-9f8a-4617-8ea7-c1e6ee1b2999","outputId":"c8af7904-1d81-4519-d03c-ec94a2c43813","executionInfo":{"status":"ok","timestamp":1702213801231,"user_tz":-540,"elapsed":1689016,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"stream","name":"stdout","text":[";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",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= {i: wrapper_generate(model=trained_model, input_prompt=prompt_template.format(System=default_system_msg, User=example[\"document\"]))for i, example in enumerate(valid_dataset)}"],"id":"e374555b-9f8a-4617-8ea7-c1e6ee1b2999"},{"cell_type":"markdown","source":["**모델 평가 및 결과**\n","\n","\n","> 정확도 및 혼돈 행렬로 모델을 평가함.\n","\n"],"metadata":{"id":"PtEfo3dHg6b2"},"id":"PtEfo3dHg6b2"},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":522},"executionInfo":{"elapsed":747,"status":"ok","timestamp":1702213956467,"user":{"displayName":"장예린","userId":"08326973126538993419"},"user_tz":-540},"id":"zGWyzs1tbSJH","outputId":"b34fdb2c-c827-4982-aa80-b081e3de4fc8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.869\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 400x200 with 2 Axes>"],"image/png":"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0.8762475049900199\n","Recall: 0.8641732283464567\n","F1 Score: 0.8701684836471754 \n","\n","Classification Report:\n","               precision    recall  f1-score   support\n","\n","    Negative       0.86      0.87      0.87       492\n","    Positive       0.88      0.86      0.87       508\n","\n","    accuracy                           0.87      1000\n","   macro avg       0.87      0.87      0.87      1000\n","weighted avg       0.87      0.87      0.87      1000\n","\n"]}],"source":["import seaborn as sns\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score, classification_report\n","\n","true_labels = [example[\"label\"] for example in valid_dataset]\n","\n","predicted_labels = [1 if \"긍정\" in eval_dic[i] else 0 for i in range(len(valid_dataset))]\n","\n","conf_matrix = confusion_matrix(true_labels, predicted_labels)\n","\n","accuracy = accuracy_score(true_labels, predicted_labels)\n","\n","TN = conf_matrix[0, 0] if len(conf_matrix) > 1 else 0\n","TP = conf_matrix[1, 1] if len(conf_matrix) > 1 else 0\n","FP = conf_matrix[0, 1] if len(conf_matrix) > 1 else 0\n","FN = conf_matrix[1, 0] if len(conf_matrix) > 1 else 0\n","\n","# 강의자료 표처럼 만들기 위해 TP, TN 위치 바꿈\n","conf_matrix[0, 0] = TP\n","conf_matrix[1, 1] = TN\n","\n","# PP, PN\n","data = {\n","    'True Positive (TP)': [TP, FN],\n","    'True Negative (TN)': [FP, TN],\n","}\n","\n","df_2x2 = pd.DataFrame(data, index=['Predicted Positive (PP)', 'Predicted Negative (PN)'])\n","\n","print(\"Accuracy:\", accuracy)\n","#print(df_2x2)\n","\n","# Create a heatmap of the confusion matrix\n","plt.figure(figsize=(4,2))\n","sns.set(font_scale=1.4)  # 폰트 크기 조절\n","\n","# 히트맵 생성\n","sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", linewidths=.5, annot_kws={\"size\": 12})\n","plt.xticks(ticks=[0.5, 1.5], labels=['TP', 'TN'])\n","plt.yticks(ticks=[0.5, 1.5], labels=['PP', 'PN'])\n","plt.title('Confusion Matrix')\n","plt.show()\n","\n","# 정밀도, 재현율, F1 점수 계산\n","precision = precision_score(true_labels, predicted_labels)\n","recall = recall_score(true_labels, predicted_labels)\n","f1 = f1_score(true_labels, predicted_labels)\n","\n","print(\"Precision:\", precision)\n","print(\"Recall:\", recall)\n","print(\"F1 Score:\", f1, \"\\n\")\n","\n","# 분류 리포트 출력\n","class_report = classification_report(true_labels, predicted_labels, target_names=['Negative', 'Positive'])\n","print(\"Classification Report:\\n\", class_report)"],"id":"zGWyzs1tbSJH"},{"cell_type":"markdown","source":["# 테스트 데이터에 대한 분류 결과 리포트\n","\n","**모델 평가 결과**\n","\n","> 정확도 : 0.869\n","\n","> 정밀도 : 0.876\n","\n","> 재현율 : 0.864\n","\n","> F1 점수 : 0.870\n","\n","> Confusion Matrix\n","\n",">  |  | TP | TN |\n","|----------|----------|----------|\n","| PP | 439 | 62 |\n","| PN | 69 | 430 |\n","\n",">\n","\n","**결과 분석**\n","\n","> 정확도는 0.869로 모델이 1000개 테스트 데이터 중 869개에 대해 정확한 예측을 수행했다.\n","\n","> TP : 439, TN : 430, FP : 62, FN : 69의 결과가 나왔다. FP와 FN의 값을 봤을 때, 미세튜닝된 모델은 비슷하게 잘못 예측했다.\n","\n"],"metadata":{"id":"uKo5-WjkhVwV"},"id":"uKo5-WjkhVwV"}],"metadata":{"accelerator":"GPU","colab":{"provenance":[],"collapsed_sections":["652f307e-e1d7-43ae-b083-dba2d94c2296"]},"kernelspec":{"display_name":"Python 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