{"cells":[{"cell_type":"markdown","source":["# **미세튜닝된 어댑터 업로드한 ID: yelin1001**"],"metadata":{"id":"3vV0Eyh39Q5U"},"id":"3vV0Eyh39Q5U"},{"cell_type":"markdown","metadata":{"id":"28e4c4d1-a73f-437b-a1bd-c2cc3874924a"},"source":["# 강의 11주차: midm-food-order-understanding\n","\n","1. KT-AI/midm-bitext-S-7B-inst-v1 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\n","\n","\n","history\n","\n","v1.2\n","- KT-AI/midm-bitext-S-7B-inst-v1 에 safetensors 포맷이 올라왔기에, 해당 리포에서 받도록 설정 변경\n","- 전체 과정 재검증"],"id":"28e4c4d1-a73f-437b-a1bd-c2cc3874924a"},{"cell_type":"markdown","source":["**종속성 설치**"],"metadata":{"id":"QGv6ld9q1XHv"},"id":"QGv6ld9q1XHv"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nDZe_wqKU6J3","outputId":"ba12f714-4a8e-4ede-d2ef-30ae5684aca4","executionInfo":{"status":"ok","timestamp":1702034765617,"user_tz":-540,"elapsed":28982,"user":{"displayName":"장예린","userId":"10756148590164405752"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages 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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 einops-0.7.0 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 einops"],"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":"mxXOIhar1c_2"},"id":"mxXOIhar1c_2"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"51eb00d7-2928-41ad-9ae9-7f0da7d64d6d","outputId":"cdd1ead9-5511-4ac9-d491-81745915c552","executionInfo":{"status":"ok","timestamp":1702034791587,"user_tz":-540,"elapsed":21068,"user":{"displayName":"장예린","userId":"10756148590164405752"}}},"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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":162,"referenced_widgets":["c9ce3698b3394296b6798cbbf8ddc724","232b14f9c2c24d288da416db01805a63","ac386ff03b65403baec9d51fd4974751","f8ea2806c9f04fba9405ef67d88b5e1d","e4cab837d16a4c469c34b5d3a10ad66a","65e9cdbbcd07433e9e16ded92090932f","7165fc33c43e4a7e8f9b543ac75c7ab2","6845b38b57a342798208d1ec003d4fae","aa6ac3b0818e436b80e3d2e1de20aa4c","4409ed630ac54adda1447851dc83e406","a064832a02fd4619b15f7dc33e393a2a","737d67665a4d460fa66a5476450b7b26","a32f94b7399046c3bc372cb47789c0c7","e6c60a658df047c68655aa19679f953e","385aaaa585ca416b8d47209926d90fff","458a7a274b4048fbabf247e7bcf0311d","fc1720a332f8456fafa28201ef15c7b4","5f50951016d0433cb713a635919701b2","2dde2a96536941ce9be7b515449007d4","9323cb30144a4456958e5c6e4ee1ad32","8af34a10528d42dbac7bc7d8cd8b7b38","56767534afe44dc8805f3691f25c7d6a","c5d8b5766bb74bd288bad8519659bba8","1e4a1cb769e84cd9922e792ec64e894b","6449f00cea404eac9d47ef9c66bcf947","d65da8e94de941b98f5b924a75b5cfbe","117c506e02ec4955ad5ed3f1bff101e1","6010cf9a274f48f2b7ea6be4a39f1036","d4919d97690540f9b0435401b1991399","e853c95344f845f398c01ffd4398f078","ea29c770c607482d8e4307ca108e0c94","b602bf9ab61a4b608cc281f5ba085e55"]},"id":"tX7gYxZaVhYL","outputId":"e19a087a-9f52-4715-f429-d9b6016f5f0f","executionInfo":{"status":"ok","timestamp":1702034804376,"user_tz":-540,"elapsed":367,"user":{"displayName":"장예린","userId":"10756148590164405752"}}},"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":"c9ce3698b3394296b6798cbbf8ddc724"}},"metadata":{}}],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"],"id":"tX7gYxZaVhYL"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4DF9D2SXVpHP","outputId":"ca67e46a-6ac7-4da9-e4cc-ffd2136f8ec6","executionInfo":{"status":"ok","timestamp":1702034829715,"user_tz":-540,"elapsed":19181,"user":{"displayName":"장예린","userId":"10756148590164405752"}}},"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","> 스크립트에 사용되는 다양한 인자들을 설정함."],"id":"036eece3-5f89-4fec-b0cd-268478b5e83d"},{"cell_type":"code","execution_count":null,"metadata":{"id":"e03d01b5-eaeb-4626-9dc5-47c3691e7fcf"},"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","            lora_alpha=16,\n","            lora_dropout=0.05,\n","            target_modules=[\"c_attn\", \"c_proj\", \"c_fc\"],\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":null,"metadata":{"id":"8224d213-8766-4b40-899e-1a1b8d164365"},"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":"3_8RynWf1nn6"},"id":"3_8RynWf1nn6"},{"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."],"metadata":{"id":"pPPapcN01rYb"},"id":"pPPapcN01rYb"},{"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(5000))\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."],"metadata":{"id":"b3bJ6RxJ1xfZ"},"id":"b3bJ6RxJ1xfZ"},{"cell_type":"code","source":["def create_valid_datasets(tokenizer, args):\n","    dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir)\n","    valid_data = dataset['test']\n","    valid_dataset = valid_data.select(range(1000))\n","\n","    return valid_dataset"],"metadata":{"id":"cofSBNtjZpGp"},"id":"cofSBNtjZpGp","execution_count":null,"outputs":[]},{"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":null,"metadata":{"id":"bf4c65e0-5ffa-4a20-9e78-6c1f030572ff"},"outputs":[],"source":["script_args = ScriptArguments(\n","    num_workers=2,\n","    seq_length=384,\n","    dataset_name='nsmc',\n","    model_name='KT-AI/midm-bitext-S-7B-inst-v1',\n","    # model_name='jangmin/midm-7b-safetensors-only',\n","    )"],"id":"bf4c65e0-5ffa-4a20-9e78-6c1f030572ff"},{"cell_type":"code","execution_count":null,"metadata":{"id":"372c64be-8fc0-4cc8-bda8-92ecf3632cc3"},"outputs":[],"source":["script_args.training_args.logging_steps = 50\n","script_args.training_args.max_steps = 800\n","script_args.training_args.output_dir = '/gdrive/MyDrive/nlp/lora-midm-7b-nsmc-v1.2'\n","script_args.training_args.run_name = 'midm-7b-nsmc-v1.2'"],"id":"372c64be-8fc0-4cc8-bda8-92ecf3632cc3"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bac62c01-21ef-491e-a686-cf4988186c58","outputId":"bea4e041-3755-4a0c-efcf-b8b56a6de5b1","executionInfo":{"status":"ok","timestamp":1701177022120,"user_tz":-540,"elapsed":5,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["ScriptArguments(cache_dir=None, model_name='KT-AI/midm-bitext-S-7B-inst-v1', dataset_name='nsmc', seq_length=384, 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/Nov28_13-10-17_31a37b1fc47f,\n","logging_first_step=False,\n","logging_nan_inf_filter=True,\n","logging_steps=50,\n","logging_strategy=steps,\n","lr_scheduler_type=cosine,\n","max_grad_norm=0.3,\n","max_steps=800,\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-midm-7b-nsmc-v1.2,\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=midm-7b-nsmc-v1.2,\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=8, target_modules={'c_fc', 'c_proj', 'c_attn'}, lora_alpha=16, lora_dropout=0.05, 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={}), 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"],"metadata":{"id":"iU4k9KYi13pz"},"id":"iU4k9KYi13pz"},{"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":"ceO2qzi72Adz"},"id":"ceO2qzi72Adz"},{"cell_type":"markdown","metadata":{"id":"elg7gcB-5zb7"},"source":["원본인 'KT-AI/midm-bitext-S-7B-inst-v1' 는 *.bin 형태로 모델을 제공한다.\n","- 코랩에서 CPU 메모리 부족 발생\n","\n","해결책\n","- safetensors로 변환한 모델을 업로드 하고 이를 사용하기로 한다."],"id":"elg7gcB-5zb7"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":142,"referenced_widgets":["88acc2080c7243d3a2e18b36ae3d8277","971b95d99dfa4f0a90b8ed1045b67b04","67c7846ae04249aaaa05d57aa473a498","5967a4b70baf4a099bc42caba006cbe5","8dac1f4ca630434e8ad93290a4194f1b","c71b361a8491421c94f9f28cd7f67713","40917badb9624b8dbed59504cbad6223","98c8468d08f844d6be9896fec3e31348","84d4d2c2c30649f39914fdbb2a52539f","23ad1550288d4e15ab8b78c436686d22","dd2f1488a7e046718640ad15987a6e7d"]},"id":"15c8425e-bb0b-40c5-bfe8-385bac699b9d","outputId":"f7d33334-3bce-4ac7-ec00-b7a8fcf6d747","executionInfo":{"status":"ok","timestamp":1701177156681,"user_tz":-540,"elapsed":97693,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"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":["Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"88acc2080c7243d3a2e18b36ae3d8277"}},"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"]}],"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/"},"id":"E9D239NqbDba","outputId":"e782faac-fdfa-4acc-c789-f9122651055e","executionInfo":{"status":"ok","timestamp":1701176648442,"user_tz":-540,"elapsed":5,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["MidmLMHeadModel(\n","  (transformer): MidmModel(\n","    (wte): Embedding(72192, 4096)\n","    (rotary_pos_emb): RotaryEmbedding()\n","    (drop): Dropout(p=0.0, inplace=False)\n","    (h): ModuleList(\n","      (0-31): 32 x MidmBlock(\n","        (ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n","        (attn): MidmAttention(\n","          (c_attn): Linear4bit(in_features=4096, out_features=12288, bias=False)\n","          (c_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (attn_dropout): Dropout(p=0.0, inplace=False)\n","          (resid_dropout): Dropout(p=0.0, inplace=False)\n","        )\n","        (ln_2): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n","        (mlp): MidmMLP(\n","          (c_fc): Linear4bit(in_features=4096, out_features=21760, bias=False)\n","          (c_proj): Linear4bit(in_features=10880, out_features=4096, bias=False)\n","          (dropout): Dropout(p=0.0, inplace=False)\n","        )\n","      )\n","    )\n","    (ln_f): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=72192, bias=False)\n",")"]},"metadata":{},"execution_count":17}],"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/"},"id":"4420fcc4-2bac-413d-b7aa-89455c512419","outputId":"e8d669da-b8e9-454f-f5fa-251e01c65b05","executionInfo":{"status":"ok","timestamp":1701177036784,"user_tz":-540,"elapsed":5,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"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=8, target_modules={'c_fc', 'c_proj', 'c_attn'}, lora_alpha=16, lora_dropout=0.05, 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={})"]},"metadata":{},"execution_count":16}],"source":["peft_config"],"id":"4420fcc4-2bac-413d-b7aa-89455c512419"},{"cell_type":"markdown","source":["**토크나이저 초기화 및 토크나이저, 모델 설정**"],"metadata":{"id":"erNS2deR2D20"},"id":"erNS2deR2D20"},{"cell_type":"code","execution_count":null,"metadata":{"id":"f47f9584-3988-46b8-a062-29dcde75a0e2"},"outputs":[],"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","tokenizer.add_special_tokens(dict(bos_token='<s>'))\n","\n","base_model.config.pad_token_id = tokenizer.pad_token_id\n","base_model.config.bos_token_id = tokenizer.bos_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":"rpWQQMIn2LmA"},"id":"rpWQQMIn2LmA"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"62e8139f-5179-4c75-84a7-0c818ab0a35a","outputId":"d4b5c999-0c70-4dca-fe6c-e2eec6194390","executionInfo":{"status":"ok","timestamp":1701177171278,"user_tz":-540,"elapsed":1797,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 400/400 [00:00<00:00, 4449.10it/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: 1.58\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/"},"id":"000314e9-f10b-4685-8da6-0511494a9eb4","outputId":"a07645b5-e0ce-42a1-979a-309bbee68a3f","executionInfo":{"status":"ok","timestamp":1701176676372,"user_tz":-540,"elapsed":401,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["5000"]},"metadata":{},"execution_count":23}],"source":["len(train_dataset)"],"id":"000314e9-f10b-4685-8da6-0511494a9eb4"},{"cell_type":"markdown","source":["**SFTTrainer 초기화**"],"metadata":{"id":"p9uTf2Fy2PND"},"id":"p9uTf2Fy2PND"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4ba80a64-0ec7-4b29-ac95-7b3d34549f17","outputId":"8b56170d-dea0-4fb7-bc0e-22d3abef88e1","executionInfo":{"status":"ok","timestamp":1701177175437,"user_tz":-540,"elapsed":1048,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"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=None,\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/"},"id":"6qPxfovivMuH","outputId":"7353e349-6152-4925-bcb6-17e5aea7718e","executionInfo":{"status":"ok","timestamp":1701176683744,"user_tz":-540,"elapsed":432,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["MidmLMHeadModel(\n","  (transformer): MidmModel(\n","    (wte): Embedding(72192, 4096)\n","    (rotary_pos_emb): RotaryEmbedding()\n","    (drop): Dropout(p=0.0, inplace=False)\n","    (h): ModuleList(\n","      (0-31): 32 x MidmBlock(\n","        (ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n","        (attn): MidmAttention(\n","          (c_attn): Linear4bit(\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.05, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=8, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=8, out_features=12288, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","            (base_layer): Linear4bit(in_features=4096, out_features=12288, bias=False)\n","          )\n","          (c_proj): Linear4bit(\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.05, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=8, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=8, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          )\n","          (attn_dropout): Dropout(p=0.0, inplace=False)\n","          (resid_dropout): Dropout(p=0.0, inplace=False)\n","        )\n","        (ln_2): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n","        (mlp): MidmMLP(\n","          (c_fc): Linear4bit(\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.05, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=8, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=8, out_features=21760, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","            (base_layer): Linear4bit(in_features=4096, out_features=21760, bias=False)\n","          )\n","          (c_proj): Linear4bit(\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.05, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=10880, out_features=8, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=8, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","            (base_layer): Linear4bit(in_features=10880, out_features=4096, bias=False)\n","          )\n","          (dropout): Dropout(p=0.0, inplace=False)\n","        )\n","      )\n","    )\n","    (ln_f): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=72192, bias=False)\n",")"]},"metadata":{},"execution_count":25}],"source":["base_model"],"id":"6qPxfovivMuH"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gw9xbeUgbZEo","outputId":"d216fac2-9aa2-4fd4-b024-65c8fa2492fd","executionInfo":{"status":"ok","timestamp":1701176686208,"user_tz":-540,"elapsed":7,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["PeftModelForCausalLM(\n","  (base_model): LoraModel(\n","    (model): MidmLMHeadModel(\n","      (transformer): MidmModel(\n","        (wte): Embedding(72192, 4096)\n","        (rotary_pos_emb): RotaryEmbedding()\n","        (drop): Dropout(p=0.0, inplace=False)\n","        (h): ModuleList(\n","          (0-31): 32 x MidmBlock(\n","            (ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n","            (attn): MidmAttention(\n","              (c_attn): Linear4bit(\n","                (lora_dropout): ModuleDict(\n","                  (default): Dropout(p=0.05, inplace=False)\n","                )\n","                (lora_A): ModuleDict(\n","                  (default): Linear(in_features=4096, out_features=8, bias=False)\n","                )\n","                (lora_B): ModuleDict(\n","                  (default): Linear(in_features=8, out_features=12288, bias=False)\n","                )\n","                (lora_embedding_A): ParameterDict()\n","                (lora_embedding_B): ParameterDict()\n","                (base_layer): Linear4bit(in_features=4096, out_features=12288, bias=False)\n","              )\n","              (c_proj): Linear4bit(\n","                (lora_dropout): ModuleDict(\n","                  (default): Dropout(p=0.05, inplace=False)\n","                )\n","                (lora_A): ModuleDict(\n","                  (default): Linear(in_features=4096, out_features=8, bias=False)\n","                )\n","                (lora_B): ModuleDict(\n","                  (default): Linear(in_features=8, out_features=4096, bias=False)\n","                )\n","                (lora_embedding_A): ParameterDict()\n","                (lora_embedding_B): ParameterDict()\n","                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","              )\n","              (attn_dropout): Dropout(p=0.0, inplace=False)\n","              (resid_dropout): Dropout(p=0.0, inplace=False)\n","            )\n","            (ln_2): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n","            (mlp): MidmMLP(\n","              (c_fc): Linear4bit(\n","                (lora_dropout): ModuleDict(\n","                  (default): Dropout(p=0.05, inplace=False)\n","                )\n","                (lora_A): ModuleDict(\n","                  (default): Linear(in_features=4096, out_features=8, bias=False)\n","                )\n","                (lora_B): ModuleDict(\n","                  (default): Linear(in_features=8, out_features=21760, bias=False)\n","                )\n","                (lora_embedding_A): ParameterDict()\n","                (lora_embedding_B): ParameterDict()\n","                (base_layer): Linear4bit(in_features=4096, out_features=21760, bias=False)\n","              )\n","              (c_proj): Linear4bit(\n","                (lora_dropout): ModuleDict(\n","                  (default): Dropout(p=0.05, inplace=False)\n","                )\n","                (lora_A): ModuleDict(\n","                  (default): Linear(in_features=10880, out_features=8, bias=False)\n","                )\n","                (lora_B): ModuleDict(\n","                  (default): Linear(in_features=8, out_features=4096, bias=False)\n","                )\n","                (lora_embedding_A): ParameterDict()\n","                (lora_embedding_B): ParameterDict()\n","                (base_layer): Linear4bit(in_features=10880, out_features=4096, bias=False)\n","              )\n","              (dropout): Dropout(p=0.0, inplace=False)\n","            )\n","          )\n","        )\n","        (ln_f): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n","      )\n","      (lm_head): Linear(in_features=4096, out_features=72192, bias=False)\n","    )\n","  )\n",")"]},"metadata":{},"execution_count":26}],"source":["trainer.model"],"id":"gw9xbeUgbZEo"},{"cell_type":"markdown","source":["**학습 가능한 파라미터 정보**"],"metadata":{"id":"4CWLQz-M2XFH"},"id":"4CWLQz-M2XFH"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"edb204be-ec15-4800-af49-6cfbad2f7f9a","outputId":"ee19a6c0-b645-405a-89fa-a679fea209db","executionInfo":{"status":"ok","timestamp":1701177183122,"user_tz":-540,"elapsed":422,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 16744448 || all params: 3821510656 || trainable%: 0.4381630592527648\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/"},"id":"sVA-VzeTigHs","outputId":"732a5289-2d93-4b29-d4d4-cb541b8a10dd","executionInfo":{"status":"ok","timestamp":1701177184722,"user_tz":-540,"elapsed":4,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["7795015808"]},"metadata":{},"execution_count":26}],"source":["base_model.get_memory_footprint()"],"id":"sVA-VzeTigHs"},{"cell_type":"code","source":["trainer.model.print_trainable_parameters()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CTZmx_faQ-Xj","outputId":"b97ac162-dce9-40b4-8c9c-08b9d62c7da0","executionInfo":{"status":"ok","timestamp":1701177185739,"user_tz":-540,"elapsed":6,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"id":"CTZmx_faQ-Xj","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 16,744,448 || all params: 7,034,347,520 || trainable%: 0.23803839591934178\n"]}]},{"cell_type":"markdown","metadata":{"id":"76sRe172fGlm"},"source":["midm 모델을 주문 문장 이해에 적용시 특징\n","- 모델 로딩 과정에서 CPU도 5.1기가, 디스크 42.4기가, GPU 메모리: 7,4 기가\n","\n","구글 코랩 T-4 GPU: 300스텝 (13:47초 예상)\n","\n","시퀀스 길이 384의 경우\n","- 14.7 G / 15.0 G 사용\n","- 메모리 오버플로우 발생시 이보다 줄일 것"],"id":"76sRe172fGlm"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":828},"id":"14019fa9-0c6f-4729-ac99-0d407af375b8","outputId":"d140316e-cd7a-4296-d3b3-5fc60f079bc2","executionInfo":{"status":"ok","timestamp":1701173388756,"user_tz":-540,"elapsed":2298174,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"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 0.16.0"]},"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-20231128_113144-sxco8kez</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/sxco8kez' target=\"_blank\">midm-7b-nsmc-v1.2</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/sxco8kez' target=\"_blank\">https://wandb.ai/yerini/huggingface/runs/sxco8kez</a>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/trl/trainer/utils.py:570: UserWarning: The dataset reached end and the iterator is reset to the start.\n","  warnings.warn(\"The dataset reached end and the iterator is reset to the start.\")\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='800' max='800' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [800/800 37:52, 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>50</td>\n","      <td>1.698200</td>\n","    </tr>\n","    <tr>\n","      <td>100</td>\n","      <td>1.034100</td>\n","    </tr>\n","    <tr>\n","      <td>150</td>\n","      <td>1.001700</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>1.013100</td>\n","    </tr>\n","    <tr>\n","      <td>250</td>\n","      <td>0.948000</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>1.010000</td>\n","    </tr>\n","    <tr>\n","      <td>350</td>\n","      <td>0.972000</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>1.057900</td>\n","    </tr>\n","    <tr>\n","      <td>450</td>\n","      <td>1.020900</td>\n","    </tr>\n","    <tr>\n","      <td>500</td>\n","      <td>0.986600</td>\n","    </tr>\n","    <tr>\n","      <td>550</td>\n","      <td>0.998400</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>1.005900</td>\n","    </tr>\n","    <tr>\n","      <td>650</td>\n","      <td>0.916100</td>\n","    </tr>\n","    <tr>\n","      <td>700</td>\n","      <td>0.919100</td>\n","    </tr>\n","    <tr>\n","      <td>750</td>\n","      <td>0.908800</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>0.944000</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=800, training_loss=1.027174186706543, metrics={'train_runtime': 2297.0794, 'train_samples_per_second': 0.697, 'train_steps_per_second': 0.348, 'total_flos': 2.48413559980032e+16, 'train_loss': 1.027174186706543, 'epoch': 0.32})"]},"metadata":{},"execution_count":28}],"source":["trainer.train()"],"id":"14019fa9-0c6f-4729-ac99-0d407af375b8"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"3Y4FQSyRghQt","outputId":"15c3e43a-e930-429e-f1d7-f9f05bb10730","executionInfo":{"status":"ok","timestamp":1701173403220,"user_tz":-540,"elapsed":450,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/gdrive/MyDrive/nlp/lora-midm-7b-nsmc-v1.2'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":29}],"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 주어진 영화 리뷰에 대한 긍정 또는 부정을 추출해야 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, do_stream=False):\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 if do_stream else None,\n","            use_cache=True,\n","            max_new_tokens=float('inf'),\n","            do_sample=False\n","        )\n","    decoded_text = tokenizer.batch_decode(pred, skip_special_tokens=True)\n","    decoded_text = decoded_text[0].replace(\"<[!newline]>\", \"\\n\")\n","    return (decoded_text[len(input_prompt):])"],"id":"1919cf1f-482e-4185-9d06-e3cea1918416"},{"cell_type":"code","source":["eval_dic = {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)}"],"metadata":{"id":"ab-3Q0ZnZHeg"},"id":"ab-3Q0ZnZHeg","execution_count":null,"outputs":[]},{"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":"XMU2ydLG2k3Z"},"id":"XMU2ydLG2k3Z","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**프롬프트 정의**\n","\n","\n","> 사용자 영화 리뷰의 예측을 생성하기 위한 프롬프트 정의함."],"metadata":{"id":"e-SkE9mf2sWx"},"id":"e-SkE9mf2sWx"},{"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":"XUvbhk6t2pXs"},"id":"XUvbhk6t2pXs","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**wrapper_generate 함수**\n","\n","\n","> 사용자 영화 리뷰에 대한 입력 프롬프트 생성."],"metadata":{"id":"p4J4p1b62yAM"},"id":"p4J4p1b62yAM"},{"cell_type":"code","source":["def wrapper_generate(model, input_prompt, do_stream=False):\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 if do_stream else None,\n","            use_cache=True,\n","            max_new_tokens=float('inf'),\n","            do_sample=False\n","        )\n","    decoded_text = tokenizer.batch_decode(pred, skip_special_tokens=True)\n","    decoded_text = decoded_text[0].replace(\"<[!newline]>\", \"\\n\")\n","    return (decoded_text[len(input_prompt):])"],"metadata":{"id":"iov1rlPJ21K-"},"id":"iov1rlPJ21K-","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**양자화 설정**\n","\n","\n","> 4비트로 양자화함.\n"],"metadata":{"id":"Vs2CLd2D24ha"},"id":"Vs2CLd2D24ha"},{"cell_type":"code","execution_count":null,"metadata":{"id":"a43bdd07-7555-42b2-9888-a614afec892f"},"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."],"metadata":{"id":"q6n3Tr4L27kR"},"id":"q6n3Tr4L27kR"},{"cell_type":"code","execution_count":null,"metadata":{"id":"39db2ee4-23c8-471f-89b2-bca34964bf81","colab":{"base_uri":"https://localhost:8080/","height":539,"referenced_widgets":["c2fd65a3f6d941e7b2ee311abd843de1","4d27ca442602458183365f64069d8060","ec252d6aba434310963f90ce0ed38d31","ad24ffb33db14ead93d17d7604118c09","9ed48cf506e04feab20197b3ce72de2e","b7088a7d4ff44d99952c4f93ea6703d6","3a644ec4a7e24f3695007a4bc0517b1a","cfad46ba0e3348a999b704bb884e53e0","412183eef1d44071bf5a5c711ec4b0c3","23e478d6e4b04b408b3c09530acad811","97ae019cd979462b9849183b154363ce","f89669afd8344e78af7cb65c2193388d","d1652fd6364a4ae4977f3d8fa4c30bf4","17a1eb7be7f9438fbc0a442e75867a00","0ed614bcbb984736b4d76489b242dabd","9b473abde4fe4e048260a4d6cc261fcc","16bec1ee9a714a58a4f8cc2c73df3054","5c6a90593db44479a00a17d08f2f372f","83f14d6621c74bffaa7636a1bc6fca52","efc891bb308b4954ad7d2a159d8d432e","fea05935ee3c48d9bd8e323e3727c444","218c579528a54c32a3a11360e241c2b7","4e0e55a1fc304d419cd039309a4ad772","854102a7dcd5407ca9594d8a29b85034","340e4ae8dd484e43a368ede33bd6018f","9fdb56aa70e34571954452534169da92","9d74e809eeb14cb2b49b93e7f1cba670","17df0fe7f3824fd8ac5bee2564a147ea","7da31d43b1bb450187abdf03bc84169e","69f1fcc5c6034926a18b13e0fa7ff851","7dc6c47f66ff4d029ee5497c6549bfb8","56032c34d6914226bdcb2e7640da4381","737ce0b9d9a84b15aad21bf13ebd1136","96c28d9784844a38bd43f17fcf341526","d54f28a82077491d85890c3133f8bb17","5a8f94f9001a4bb0b7666381ec44787f","e16f931caca74f708aed43c6a3dc10cc","49bd70185c124c68bb4ef315560ddaec","0562521df5e0458cb5d48ce7d241933f","6bf0eb9b098e4f18807904fbfbd4b20b","1a9be19bc6794718b8bae064f98f5ff8","f913fa5302b04842958e3ee710879d6e","9cabaac5c8484bc4863e19da135763b6","0d1a0c77a7d544f4b5e3142ce8d48081","7789f290df334e30a1ad829853e31e15","75b9922909dd4103b188778aff457311","083013e09b9f49579358736c66e9d032","88a9d66d6c3e4fe38fe0c2a26e6402da","b6d3b77208104d50936526089a204548","b55a75c9d89b45c5825256f5296eeebb","605580e4e53145c9b4a6061857ce6185","938745b20e1a49c194327e18e39e2916","30d86f839ddd4f418b58473d66e0f2b7","53c838b5c07940f2be0dd6a6ab02b3cc","f46eaf640feb4cfe8d3e8adaead28209","f12a7f89f6d2435f9dfdf05fb7c5daaa","32ecc05b86984b8997ad43e27dd63512","36bae218caf4460796435b10a1aaa6b3","29b37cf59b564ee6bc1ff6948b33ee24","55dcc8edeb474e2ba7793509dff51879","2ff04b51e24747018b6903beb2948a65","e7392eade55846f6b3a603c38bc85dab","b9b96ac6dc29485cb04f1b1bbcd8cb7e","b6d262184a1541b296f94a7d9eab9128","918cf5037bed4fc6b86cec27bbbd8df0","e92c4ec7873d4b31b696c6f5a098dbcc","c88098d28e11421f87463e83e81d3e17","441ccd3214244c44b6d239e3c164a49d","d848184f52cc4f27939b02683ebebfef","316c740ea79e4b4bac22aeaefabea537","1278f290c4c1441c9903b229d5f74a33","5b21b6a5a4f74b119ce3aa4b47518c04","696f283b47854e0b8b3b650d48fc4c7c","7b7ba03b3a0243a29e09127af03d1a9d","a8d344bd1c87403899d15d45c803dab7","74c15cae3a6d4dc0af178f7ea775759f","0b8dabc9628043f2b9f459ed8b17d148","bab910c60b044b9e972fe84b3860189f","65ad4b9159c642a4b0b7c13d4999f229","e29409a04a204ea995aaefb5079d460f","4fbdec2cd7e94b1fa4811dc89b8f0620","5c96f6fb78be4c7c991e52373853338f","76fa12d3ba99455181b2df000d34173f","9c7846adcf934a8aa89ccb8fc7f9a55c","45af7599bc5143a4b8d2046340389a63","b83005e296fd48628f479e049d19dea9","3e41896793364e539163b8fb229e1313","d2f1f59d26724bd48e91ce0ab339627b","204a944a12884c9d8eff0f0891fe5c23","aed8d7b38e724749803a816e7ec8d0c4","fdc5ad3fdbd74ecea5851e807ccd5665","062027b01a76428ba755e1aca4d477e1","bf39c3fdcab54c3fb99234568daf7166","45b03f0ec28f4b0e8a0dcc1005df2953","85fafa11386a468fb016374080a023c7","c9cb72bc82a54193bc360b5b869a3534","960264073f5b4338b7f8c7f21356816a","84b012ffcd644e02bd262196a8b979ad","7f6964afc9c34b3caf901d2b8bb977fa","915fd3620fe84b8cb8cef82704adf868","dba36473c9fe4c208fd31bd00d7268fd","11bcd762a6b44b02be9157945ea1e7f1","2a229f6c77fb49ed8a2c4cb8287301a2","3f5d388ceb5c4df2bea0e241292cde7d","7092f348997e4d85a6741a08a47eabfc","a66a74a482d74f80ad15f1d70304d450","10c08106c76c4ac68728f2e3f0359aa0","e2d94819ed0f4d3b84ad70f959cd0502","c3163dfcf853426bae55293c9f600fcf","fb805a205ad8403dbf74fe56d2b935b6"]},"executionInfo":{"status":"ok","timestamp":1702035314460,"user_tz":-540,"elapsed":200285,"user":{"displayName":"장예린","userId":"10756148590164405752"}},"outputId":"0a76048a-2303-4827-f841-55c47eb3b103"},"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.14k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c2fd65a3f6d941e7b2ee311abd843de1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["configuration_midm.py:   0%|          | 0.00/831 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f89669afd8344e78af7cb65c2193388d"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["A new version of the following files was downloaded from https://huggingface.co/KT-AI/midm-bitext-S-7B-inst-v1:\n","- configuration_midm.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"]},{"output_type":"display_data","data":{"text/plain":["modeling_midm.py:   0%|          | 0.00/61.1k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4e0e55a1fc304d419cd039309a4ad772"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["rotary_position_embedding.py:   0%|          | 0.00/4.06k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"96c28d9784844a38bd43f17fcf341526"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["A new version of the following files was downloaded from https://huggingface.co/KT-AI/midm-bitext-S-7B-inst-v1:\n","- rotary_position_embedding.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n","A new version of the following files was downloaded from https://huggingface.co/KT-AI/midm-bitext-S-7B-inst-v1:\n","- modeling_midm.py\n","- rotary_position_embedding.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json:   0%|          | 0.00/25.4k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7789f290df334e30a1ad829853e31e15"}},"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":"f12a7f89f6d2435f9dfdf05fb7c5daaa"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00002.safetensors:   0%|          | 0.00/9.97G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c88098d28e11421f87463e83e81d3e17"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00002.safetensors:   0%|          | 0.00/6.22G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bab910c60b044b9e972fe84b3860189f"}},"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":"204a944a12884c9d8eff0f0891fe5c23"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/132 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"915fd3620fe84b8cb8cef82704adf868"}},"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","    trust_remote_code=True,\n",")"],"id":"39db2ee4-23c8-471f-89b2-bca34964bf81"},{"cell_type":"markdown","source":["**토크나이저 초기화**\n","\n","\n","> 지정된 모델에서 미리 훈련된 토크나이저를 로드함."],"metadata":{"id":"blIuzrDC2-Mr"},"id":"blIuzrDC2-Mr"},{"cell_type":"code","execution_count":null,"metadata":{"id":"b0b75ca4-730d-4bde-88bb-a86462a76d52","colab":{"base_uri":"https://localhost:8080/","height":187,"referenced_widgets":["81a15629dac64a6faa9bcfb5410f3588","e807dda9a7de4d4b86e5c47d1fd951a1","55baa7e325984defbd9a5d53fdb25bda","5264a42c14894af29c70ca4a00562cda","34010f6a2efa4d65b8961817fba44656","1a0306813d514b35afcde99fc7deaee6","6089cbb639724d4898f252199f7933d6","da0e74aa4f3a4905a9f4c6d63a8a489d","38a23ddce9c941a4aa8907125f304ca4","9f26a083a2e7465d94bb0f358001933c","81df7de0293e4ee3953133d479133945","4bfa73ff754548518b55f1e461290b11","2bcabfdad3f34f758de776f8c3d2814d","4a1f25c9fc07474994ec722f58f1bd6a","fe9fcfca5b3a46ccba2bca9620137322","f3c3a96d493a4d70a22ab7d40424d321","286caa64806d4116873d9ca69191e3d2","4942e858401d4e128de58ba58b77f0bd","3ef40a7706fa4cabb1e39bbdcc86d4cf","86ec597fdbb848d3aa9ed7c41faa3523","a2d671fa9905436c91fe32d3c3d59922","0b2ea01d783648f78b57e7677e46dfc4","abd68d68d5c84ecb8ce8daefd486300e","b7d1367a61404abf8a13a17556a72fe5","151d4d95f3954950baa69567abd1a0dc","078fffe629014e678fe30df577468de3","656c87b1edb94a40982da06783c46f61","c07657a02dce4c198c36e1d57211ea9c","e1ce68c3ab6b40b19c3fc9bd3ba236b7","4c5eb15964554c50ad99ff9ad7b83262","7480a95b4d414955a458aa84a461622f","4581e8a6eb4249b780e9773968f4a3d8","a3e657a9b87d4e06a2f8f73625efb049"]},"executionInfo":{"status":"ok","timestamp":1702035338823,"user_tz":-540,"elapsed":1990,"user":{"displayName":"장예린","userId":"10756148590164405752"}},"outputId":"e404a961-4323-4ee7-d8c4-1fe2c98051df"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"81a15629dac64a6faa9bcfb5410f3588"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["midm_bitext_tokenization.py:   0%|          | 0.00/12.8k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4bfa73ff754548518b55f1e461290b11"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["A new version of the following files was downloaded from https://huggingface.co/KT-AI/midm-bitext-S-7B-inst-v1:\n","- midm_bitext_tokenization.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"]},{"output_type":"display_data","data":{"text/plain":["midm_bitext_tokenizer.model:   0%|          | 0.00/1.46M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"abd68d68d5c84ecb8ce8daefd486300e"}},"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","tokenizer.add_special_tokens(dict(bos_token='<s>'))\n","\n","trained_model.config.pad_token_id = tokenizer.pad_token_id\n","trained_model.config.bos_token_id = tokenizer.bos_token_id"],"id":"b0b75ca4-730d-4bde-88bb-a86462a76d52"},{"cell_type":"markdown","source":["**데이터셋 생성**\n","\n","\n","> 추론에 사용할 테스트 데이터 생성함.\n"],"metadata":{"id":"Wi_2M_tE3A58"},"id":"Wi_2M_tE3A58"},{"cell_type":"code","source":["valid_dataset = create_valid_datasets(tokenizer, script_args)"],"metadata":{"id":"a_uxsZ4U3EHP","colab":{"base_uri":"https://localhost:8080/","height":305,"referenced_widgets":["2eaf7829ffc140a1b6d3208809466334","f4bf440fc087498e9732285b4cda952e","0794089ac98d4b71b9f4ab72a722b551","96f0f0e093024a028e68cfe0e7d12bfc","fa577ddf15034af8a3b18bc6e2c9ac2d","a110536755b1423c93f71859f593ca3e","d3a728e157aa48d8a8587525f1ceda3d","3e45464565d643c6b32b8102f1c7f209","f79554438f914040b9ec0be3cb7535a6","1c8cc175720a489d97839be5a87f4c16","914ac7f77beb4448b7f68814a5280514","e4343745ea8e47dc9bd457e59c03efa6","493074e2841c4cdeb05b2a978d1c27aa","3c3dc3032edd40b2b993dc3b9823302a","6842f78ad395443fb8e5d4bd7dcf90b2","a2516650762b4f8e900373721236a9b7","bbee9f26f99e43ec97a0e4a93657b8f0","68e04dea8b2d4c88b48f2acb90b639a4","eabc245366b54f5a896e54e8aa00706b","ad20749f4eb54ea897796946b8b26b70","bf57773cdc124e1ea9d0dc5555c2a7bf","dfd4783b4aae4eccaa58f5f638563c91","5de1cc1c663242ca905071a18fc132f6","bbab88a07d324036a35fd28f0601aa72","6b1dcf85dc954777bfb1a074cfcfb74e","1d6890f9b08c40aea9ac9e7f188a0d70","3510db1d4bac45b595b00eea59a561c7","e32eaad3f9a94797ba356760980ccf0f","daa09d2068dc449eb6f7dc5edb6d6d5e","169da7ad74bf4cafbb6e14077883035e","6ce52c88931b4dd2885712da6c795f0b","52bf3328cb2f4f938914037bfb2bc016","9cbf69c354214248a482de8ba3009761","84af4a82759c458ea43b18aa2e3fa78d","05b9b25d31ae40d88e3c3910cd6d05e8","d886251450e142299460d00b6a83478d","7f08f4e2f7794353a706c40a2d6fe16b","a593214ec31441dbbc33840b9818fc6e","54fdc654bb894cf7aa22fee3dc1db9e4","b6b7176d0c9741c389a4fc127ea3935e","a43bdfcfac814d17bd9fb02576761930","ebfcf61e0010469d8c2e9445944c25e5","c3806079349646d3b1260c14b42c24fc","dd91e810468a4ef09cc81746d3b3f002","84c548f8cace4bbda2a2dab1f78c3e29","294323d127564772a6654e8c21008ede","8e7d0b4ccee74857ae10136970199e72","9e554e0ba8a84a3baa50079f4b755cca","7cc4edc38c4c4a8c9236b5a880ba434d","a9e88671e5bb4809b36571ae4998c11a","092ea00b9f37476ea910287ca9fd594b","c52af5c452bf4dd4a1e5aaac5df2692b","41c17bf9195540fc8c78ea45429c9cd9","88fcfdcdd3c2433e8efee0a7c52df212","efc33584391e4ec99eacdff33e67bdc4","ec009d083c414688a22d8245eba8299b","5b56720078114f0b9a138d9f58c8e694","e0a18abaf0de475ebdda661a416a5296","81ef9f81ee7d4b9da630928f58fb3552","03740b16841242faaadf636e86fec671","eb160d88e7584c12b447d690613881cd","7348a1af040b4809b622997df68bcb5e","d00a686c6bf24a818247bb5e80f9094a","53c8bfb3ea384bfca527ba0ca9ee7a00","79fa46263201423a824d9eac5dea9517","85e8cf64b3c24ca3932d76fad2df34cf","4bbefa59b4964625bf344febe6cd498e","dafb892db7b949579011b3e4a9a8eada","e4a6f2ee44314785b071f33e8bca7346","cdc16a985df94125b233ec4f5935697f","75c19385910346fe956f391ffd281e7c","023fbf8f348645d9bafc51d7c8e057e8","f659cb7caff047d69802b09385c461de","bab379e08b3c44279e75c2686762e562","0d00703b1efb480dadb681bc119082c6","d591a8a4fa3f4dd1bb491d73fb326035","a0a13112dcab4ca6baf044e4df6ab098","8c4aae732a1146b680f3b2a41d167a08","61033c650eac4ba3941efb4e60b9e860","0e3e6dd4655d4eafbaa40307fc389168","b4d596da34d6463a92c8312e908ea6bc","5a00b40b55d14bf1b5b50879f218a5cc","256d0f9209f84f54961ddd871d0533b1","9ea42fea8c974593a4088863dc5f0b7a","064ec33c77cb499da2b5c95532cc1998","38f480b370744b11b941d645e6b8397a","1a9e4bba634f4f2b8c0e0fbe1f474c64","a03145c757ef4010ba961153df8ba0a1","27a4ae327f794d1ca4c76b2da68136e8","9d36060760c04bf9ac29aac2d4dad7a1","5876b33eba414869a43069e21de4d221","86f39bcada2c4fa091e350c0ccc368c7","b955712e65424f8dbd7f52764a434af8","a953b9b5ddac44c9b1490dc7b8de8862","83177f7df428445fb22a087f6122016d","e1866f1e20c94bd4beb6917dc4d25495","0ba40c87ac5c4406aa386d7c24216fef","9c5235da4ef748789d631e908b65181b","2d7f004eff654b3ca89fea1f2c2aa574"]},"executionInfo":{"status":"ok","timestamp":1702035353099,"user_tz":-540,"elapsed":12783,"user":{"displayName":"장예린","userId":"10756148590164405752"}},"outputId":"02b1ed9f-c670-4732-b2dd-f4a7f9e93652"},"id":"a_uxsZ4U3EHP","execution_count":null,"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":"2eaf7829ffc140a1b6d3208809466334"}},"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":"e4343745ea8e47dc9bd457e59c03efa6"}},"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":"5de1cc1c663242ca905071a18fc132f6"}},"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":"84af4a82759c458ea43b18aa2e3fa78d"}},"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":"84c548f8cace4bbda2a2dab1f78c3e29"}},"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":"ec009d083c414688a22d8245eba8299b"}},"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":"4bbefa59b4964625bf344febe6cd498e"}},"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":"8c4aae732a1146b680f3b2a41d167a08"}},"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":"27a4ae327f794d1ca4c76b2da68136e8"}},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"X1tRCa4EiYXp"},"source":["추론 과정에서는 GPU 메모리를 약 5.5 GB 활용"],"id":"X1tRCa4EiYXp"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e374555b-9f8a-4617-8ea7-c1e6ee1b2999","executionInfo":{"status":"ok","timestamp":1702037381306,"user_tz":-540,"elapsed":2026493,"user":{"displayName":"장예린","userId":"10756148590164405752"}},"outputId":"99642683-e38d-4f19-d277-464b755858aa"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1473: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n","  warnings.warn(\n"]}],"source":["eval_dic = {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."],"metadata":{"id":"O50xByhv3RoQ"},"id":"O50xByhv3RoQ"},{"cell_type":"code","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)"],"metadata":{"id":"TL7zOjZD3T14","colab":{"base_uri":"https://localhost:8080/","height":522},"executionInfo":{"status":"ok","timestamp":1702037385497,"user_tz":-540,"elapsed":1004,"user":{"displayName":"장예린","userId":"10756148590164405752"}},"outputId":"104717f4-2d79-48f3-a62c-b34e358cd527"},"id":"TL7zOjZD3T14","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.91\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 400x200 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Precision: 0.9409282700421941\n","Recall: 0.8779527559055118\n","F1 Score: 0.9083503054989817 \n","\n","Classification Report:\n","               precision    recall  f1-score   support\n","\n","    Negative       0.88      0.94      0.91       492\n","    Positive       0.94      0.88      0.91       508\n","\n","    accuracy                           0.91      1000\n","   macro avg       0.91      0.91      0.91      1000\n","weighted avg       0.91      0.91      0.91      1000\n","\n"]}]},{"cell_type":"markdown","source":["# 테스트 데이터에 대한 분류 결과 리포트\n","\n","**모델 평가 결과**\n","\n","> 정확도 : 0.910\n","\n","> 정밀도 : 0.941\n","\n","> 재현율 : 0.878\n","\n","> F1 점수 : 0.908\n","\n","> Confusion Matrix\n","\n",">  |  | TP | TN |\n","|----------|----------|----------|\n","| PP | 446 | 28 |\n","| PN | 62 | 464 |\n","\n",">\n","\n","**결과 분석**\n","\n","> 정확도는 0.910으로 모델이 1000개 테스트 데이터 중 910개에 대해 정확한 예측을 수행했다.\n","\n","> TP : 446, TN : 464, FP : 28, FN : 62의 결과가 나왔다. 미세튜닝된 모델은 주로 긍정적인 리뷰를 부정적으로 잘못 예측하는 경향이 있다.\n"],"metadata":{"id":"xtT3fzVS3WqY"},"id":"xtT3fzVS3WqY"}],"metadata":{"accelerator":"GPU","colab":{"provenance":[],"collapsed_sections":["652f307e-e1d7-43ae-b083-dba2d94c2296"]},"kernelspec":{"display_name":"Python 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