{"cells":[{"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"],"id":"28e4c4d1-a73f-437b-a1bd-c2cc3874924a"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":28937,"status":"ok","timestamp":1702304112153,"user":{"displayName":"강소연","userId":"16433369612605492183"},"user_tz":-540},"id":"nDZe_wqKU6J3","outputId":"45e12280-6f1d-4fd9-9961-4cea5ec99fa2"},"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[31m3.8 MB/s\u001b[0m eta 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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":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16540,"status":"ok","timestamp":1702304128691,"user":{"displayName":"강소연","userId":"16433369612605492183"},"user_tz":-540},"id":"51eb00d7-2928-41ad-9ae9-7f0da7d64d6d","outputId":"1bb8ec1d-fcce-42fe-c50d-9a9eb4eab964"},"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":145,"referenced_widgets":["2b28c142d3504b168c8ca93856e5f400","f8b42df8b3ec48c5adad8e45bd3cc0e5","6fcba02908d049c7b6d18a08039b354f","76082eb9f69c41f0a57504b47d706e70","57303db2d6194d6a8fec0e96bd7c130c","20531fc5c93344cf930bab835ab50e96","f960a2a70e2d48049eae8364cc9abedb","a819e4fcfb634c029ef7c003fdc135ff","10d04d442c1f4a6c9ba259afddd18d38","9523fe6a14974dfda3eae377a38347bf","82e9a4454761432f986881b658ed1611","662a04a5e49141448bc2740b16b1a1f3","bb476aa148fd41f0b4d9db3a6c3868ab","b261703915894d2185bfad000fdc749f","966845f63156483ba43dce0c76c7d755","6ea387fff5e545a0b5993d120bf19afc","56389cfeabdb4469839d273dc8385be5","df584a10f11a4d12988de9e1a4b41bab","452d8f4301134e49bb76dc4de68c6a20","f43ec23c00884a78964f259f2a759dfa","b1a6a6a0f2be42c089140e9ded9a1402","dee8e2d3da654053bad95371cd7da293","2a49ee7ef8e84e708101806c8ab5bcdc","f233f20aa0e84286a595583db146c997","9c7ecf7f9c91431787ac58d9daacc5a5","4677dcb3c9244357a72f2c6f114a4739","c44beec239d34c2a9b5e7c8447480c51","4032b77e3ec4459baff16a6f0625a9e6","c853335fe4c24286975d25bcdf062aac","bb353e431fc74ee5bfbb1fb6883c19da","b02c90c1b1274747ac1ce1dfa91e17fb","f933303dd9fa4db3a6d35445ca4408bc"]},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1702304128691,"user":{"displayName":"강소연","userId":"16433369612605492183"},"user_tz":-540},"id":"tX7gYxZaVhYL","outputId":"87758fcb-9e73-4754-cd66-27ad14b5657f"},"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":"2b28c142d3504b168c8ca93856e5f400"}},"metadata":{}}],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"],"id":"tX7gYxZaVhYL"},{"cell_type":"markdown","metadata":{"id":"FuKA5uZihmdh"},"source":["드라이브 마운트 후 파일 업로드\n","- food-order-understanding-small-3200.json\n","- food-order-understanding-small-800.json"],"id":"FuKA5uZihmdh"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":80962,"status":"ok","timestamp":1702304217450,"user":{"displayName":"강소연","userId":"16433369612605492183"},"user_tz":-540},"id":"4DF9D2SXVpHP","outputId":"86606876-82a0-40a1-8150-e4d5530ee909"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"id":"4DF9D2SXVpHP"},{"cell_type":"code","execution_count":null,"metadata":{"id":"VFgITUI8WjKe"},"outputs":[],"source":["# /content/drive/MyDrive/자연어처리/food-order-understanding-small-3200.json\n","# /content/drive/MyDrive/자연어처리/food-order-understanding-small-800.json"],"id":"VFgITUI8WjKe"},{"cell_type":"markdown","metadata":{"id":"036eece3-5f89-4fec-b0cd-268478b5e83d"},"source":["# 매개 변수 설정"],"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":"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, User=example[\"document\"],Midm=\"긍정\" if example[\"label\"]==1 else \"부정\")\n","    )\n","\n","    return text"],"id":"b49c3470-480e-4ff2-b2c5-fcf1d3a13fba"},{"cell_type":"code","execution_count":null,"metadata":{"id":"5d9abfb4-339d-414b-855b-ced51631b752"},"outputs":[],"source":["def create_datasets(tokenizer, args):\n","    train_data = Dataset.from_json(args.dataset_name)\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,\n","    )\n","    return train_dataset"],"id":"5d9abfb4-339d-414b-855b-ced51631b752"},{"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='/content/drive/MyDrive/자연어처리/food-order-understanding-small-3200.json',\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 = 300\n","script_args.training_args.output_dir = '/content/drive/MyDrive/자연어처리/lecture-llama-2-7B-food-order-understanding.ipynb'\n","script_args.training_args.run_name = 'midm-7b-food-order-understanding'"],"id":"372c64be-8fc0-4cc8-bda8-92ecf3632cc3"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":513,"status":"ok","timestamp":1702304338092,"user":{"displayName":"강소연","userId":"16433369612605492183"},"user_tz":-540},"id":"bac62c01-21ef-491e-a686-cf4988186c58","outputId":"ad1e60a5-1aba-4d39-d75c-d3fef78189e2"},"outputs":[{"output_type":"stream","name":"stdout","text":["ScriptArguments(cache_dir=None, model_name='jangmin/midm-7b-safetensors-only', dataset_name='/content/drive/MyDrive/자연어처리/food-order-understanding-small-3200.json', 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/Dec11_14-18-50_abd13899f3a8,\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=300,\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=/content/drive/MyDrive/자연어처리/lecture-llama-2-7B-food-order-understanding.ipynb,\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-food-order-understanding,\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_attn', 'c_fc', 'c_proj'}, 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={}, 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":"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","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":707,"referenced_widgets":["b86c488e50cd413f977a6273f13958e3","8c0f218b30ed44b3b8a9f6ad3b8cc24c","b7ca0d92c25f44cbb34ab5a64ee64108","01d3736a46d44f9ca4962f9ef38dc005","e491d74e53e24338bff9fd937e3be07d","1df7fe0376534fd99a26be4d9e165e5a","5dfd0cc7d0c34857938271a47b89d92a","40982efcaecc4d51af644b8cf3615090","d522b4d54d9248999c64fd6b411b4238","53b84ac487af4f96a93904bbbdf0d0c6","336bc37d080040b4b3b246275b66daab","0df52ed1fecd448bab85b6a743097e9a","ff116a57209f4f739d072539e9477e06","42d465cfcf874a05976eae53c73de3bf","98de521b09024fe987f14d1ea9ffb6b3","1b469c6999a047b985c54769795f774f","6ffa31a92ce449f0b0d50caa76e54bed","78cf9753fba74ce1bcb93911045771f2","462878e4564645728d1b0cd6d6e727fd","0776893276bc496f92743bb4e4db9492","04e4c498614447739b1ea710b047f185","258b205add0045c490c8663f6b04bb8a","c5688a5cb2de446dbdc785eecfd709dc","88602dd276c343828682d388d4b6ee4d","0230392da3894bbaa8df7cf3a4a08d92","cf62ad8a05444da1ae8d653cbfb90c96","338987e502a84ba2b5d0f96b027cbf69","98096f1072be4654999550cdad4de39b","47499e08a0ca4d41a22e2ca3d775b992","a8df2ee4df5e4ead836c03a643f1acc6","26ad63b138fb4458bb3e4cb9ed0fe46d","e743e1e011f54d4ea9270e4eabfefdc8","13c588fd8db44a88a3d303966a016d56","442cf46f98704e96922c7fa1b9aeea58","9c9b836ef63e4dafbda877bf513baf17","db9057aa042b4eeca809d475f86d1c57","4d93f5fa6f404cfcaa42152b00a96e1b","d9c949084378445fad0b0f81d566b068","bc34dd20b11841c8a8562edf56e3619d","eb7a46b3dd0b46f4b739ee8016a55ae3","ce473359958e4826af823b5f2e4f7ed9","335b1645c3ed442b816853ba2157e5b7","a258caaa38a04346a1afafe1d49db2ca","ea52b8b3e71540ff8e09ccfbd033cf19","7de5b673614e427791a37155025b1b19","5b6eeb7fc6744d34a3c50bad1b3d5985","6e996c30259d4771ac1dcca82f5c1e09","a80d6f78255a4c769d46159af2228a79","a9a4e60382d54ea8b58aab18823f94d1","4f40e3c515844a369767c655bf03c6ec","997ab43a082a4cb0abb26d0bce3abdf9","e893efc451f4484fb220a0f9195f1ff9","ae40098c103e4bccad74b91c1c8d28b1","bd51dcf967124e359ac19433282396d5","5b153a17abaa4ce2bde50e6d97c0a205","ed5f8502bdd0402d87119a5924c9c363","46d144e55f8348d6a0c034f7522b322a","80554e0b99744551b73ea406bac9b493","0268ba91c9da43f9aabdfe92fbc5ee4c","7159df7162e24d24a04e088a4d6ac74c","a5f4595a8e1642188d86c82c53173908","00597c5128a142188667b74607318cfb","086273b951584d9497d07e5a1ff13091","c2956a32b6de4a3c901d5bb75822e50b","4c5197a893ba40cfb549bff654008fbe","2b4a467be93d4905afe9aa96310958e2","98c03a3b5f5047b98a93949cd2aa56ba","8520fb3f9cd647a68e4a1506041d652e","1fc20abb079e4cb0b88c39e14883c6f5","7e8fb4d666f7442fb1f6a940da3274fb","2397d2d9be0b4a748b93df04cd1e2695","9adcd318fa1942dd8c0f84a0a80e95a0","a29f2daa71944227b4b03e4f5d9f7108","aed4dabb23fc41d2bc3a890df9b7f7a0","9fd62c66ac6d4330985c08adc9964cbb","82f728a9e9a641cc8f85818df31be5a2","9a747910505f4cf3996e1ab1337b558b","a504c45398c54d6288a0714be20b109b","83c5499dd57d4a9fb0e5d66b4a60a303","5f65d5df1bdd45c78405909a46712355","74f98d83424b440fa159b4af23551f88","c00e50e3993946e48cf39ca77dcf607f","6688f110530b4a46ba764afdcf0c1f04","a320a3fa72e5400b8f04b4d0c3f72b39","d8d24c96d6164258a778ec90f99e918a","51f2b08dd62d44868e1b5ded6f06d4d3","2d6b139e609243dea4ab8b460302435d","6fd713c14e134607b72fa8f8cd39ea2e","0f62761fa07f4d0f92938663dcc24d90","1f1ea203063745a1945b8c578345c390","2ee6779a27ec4fbba5f60ef5cc687c05","711cd1b7d01c46e5b22becb3728edd8f","030d34d21c034827a28236749e890bb7","2402d0239904488a978003dda880983b","e4ead0248d9d4a318e52205544a0e1b2","179667cec48044c9821274906b067f57","5b29a490d4c84e0b83f520c0deed6285","49e0fa02fec24f95a4e9a39670715cbe","b1e8141b8ba4434bb496236073671a54","8dc738c044584454bd2e5f56069dd2cb","0e11247d55484a5d8047512d9591f820","5ca1eb3d14ff4533bb7c0058629f9afd","b3fb99d40d2f42f8ad02a76c33dac303","8b0e74ba54234983bf0a3da8685347ff","3e2c82e998aa428cb7cfa417d78765f3","5a2c9b4ec450430bbdb4f6cb064658a0","3536130143a34e55a3aaa30cdb1898de","3a8dedee99604a9f9c7c4c9f80a45b79","3f8501cc9a4f48f7af414741c5c2ece7","cf85f57a34d04285bedb90001b4e2647","8ecbf65ad80041278c8018f18b3714c4","2888186320dd40f1806a3eaa1e6648a8","c5b5e9d9edac4a62a71474e53aa4ddb9","e4531ad202af410da99e41f820faf4ad","978c72f56782459caabaa1cf3156e92c","e8c907346e644528b60dc7f3049fac87","33887042c79c4947b91494455ce91a6f","021df05e2cca4fbfa4778622569a8fb4","e989d563f3064808a40905f2009ecd62","d75fa21342224689921c3f995000ec3d","a61d9d42e9d949f3b72ae99034bf2681","15b26d5f206046238031a9c817c25353","4a1b12ebe3b64c8da95d735ce4a6f768","3ee38fa2754f43db9e6d2d8902d317f7","b2891745bb0946f889998f4f57743143","81f0d51f4fe74591a597fe14848509cf","5f4e0a6947ea41fc910283b3e9dc9fc5","c3ad4a6248ce4151b931112c96fdad4a","f8dc28d37b624be395313ba2f46c4e99","e7761f61da7a498abad76af5f4cd07c3","db6b1c622856459c8015a0358db1b998","23882e2c0ec64b09ab5415d762ff5ad8","092880df40e34a12b78f39e70a4a2d57","d97349a53d2c4ba78eba85e11b9ef5d8","150a43f98bf44548b0a90bfbea3f1431","1e8805c61928417682dd1c6c3886d421","482c2a817496429f9370e20f37bca917","a81b20c4c62f4a4091e2fa2b1d545e45","5184e6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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%|          | 0.00/1.26k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b86c488e50cd413f977a6273f13958e3"}},"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":"0df52ed1fecd448bab85b6a743097e9a"}},"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":"c5688a5cb2de446dbdc785eecfd709dc"}},"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":"442cf46f98704e96922c7fa1b9aeea58"}},"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%|          | 0.00/24.1k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7de5b673614e427791a37155025b1b19"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading shards:   0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ed5f8502bdd0402d87119a5924c9c363"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00005.safetensors:   0%|          | 0.00/3.94G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"98c03a3b5f5047b98a93949cd2aa56ba"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00005.safetensors:   0%|          | 0.00/3.88G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a504c45398c54d6288a0714be20b109b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00003-of-00005.safetensors:   0%|          | 0.00/3.93G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0f62761fa07f4d0f92938663dcc24d90"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00004-of-00005.safetensors:   0%|          | 0.00/3.84G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8dc738c044584454bd2e5f56069dd2cb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00005-of-00005.safetensors:   0%|          | 0.00/591M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8ecbf65ad80041278c8018f18b3714c4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"15b26d5f206046238031a9c817c25353"}},"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%|          | 0.00/137 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"092880df40e34a12b78f39e70a4a2d57"}},"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":1702304684146,"user":{"displayName":"강소연","userId":"16433369612605492183"},"user_tz":-540},"id":"E9D239NqbDba","outputId":"1f2d9652-1108-41b5-e23d-61cf15a0b65c"},"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":15}],"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":2,"status":"ok","timestamp":1702304688484,"user":{"displayName":"강소연","userId":"16433369612605492183"},"user_tz":-540},"id":"4420fcc4-2bac-413d-b7aa-89455c512419","outputId":"0e66d5a2-bd5e-4849-fd6f-70c89c477756"},"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_attn', 'c_fc', 'c_proj'}, 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={}, megatron_config=None, megatron_core='megatron.core', loftq_config={})"]},"metadata":{},"execution_count":17}],"source":["peft_config"],"id":"4420fcc4-2bac-413d-b7aa-89455c512419"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":167,"referenced_widgets":["cf4d3b7afacc4518a0f174bcab0e59b6","a4c86263f5644b0b8e9c49fc7be82e65","ab8bcc2f02124330a8d11a34f5f4682b","53784307c79746cc89c2bba7bb1ca35e","dc39d5c9846d47378e33e9696ebdc230","bba35e288ca6481caef705897cec5f4a","3f21e701f54a4d968ffb27f65bb431fd","c2e1991494614439ac0b789c75f8362c","dc2c88e8488141988525d9253bc39b6d","3b98ca72c0244d288998dc456f641970","b869a22561f34436ab3da7b23223aaba","43e4816eb0004d32968d6687cfa6ae4b","0aa804b0c02c4dd7b9570290635a08b7","cd72e2b1d8d34f2481522b1af034b14d","091c77c84ddb48c3a94baad6b92d49f6","8e55440d0ea7437197646e536259a2f8","eacba5b74370418b8d52221ae0c00f79","663f902d678f4c359e377f632cd3ce2f","654f753d20d94ff694ffdc0cb8e8df33","e452ae89030342748b01835b06cc0464","f95a9bc27ab049538a7bc8a50d6df152","4008d72943464e0092722227ad6c105f","1dda1a4ce4034fcba23beb66a6934664","a591ab02b61b4723b4a44f5f208e4926","3790ff7e9e12489a950bbc38b1172d38","8035ca10e48444869a7743cba74bd360","2492b7e2a9c44e4bb6fcd1e21beff79e","d6a7d5b5db434a54b82572c587febdfe","9b1050b2c7174545a4792d25d09aa4ac","2f8f199973e149d9aa430aedbab8d4fb","d7d75a1fd96a4eda8ec92eb18aad49d1","68d0e031d807475eb4b340d873f4fca5","c1cba5d268f94c74a8cb30a362613178"]},"executionInfo":{"elapsed":3954,"status":"ok","timestamp":1702304694556,"user":{"displayName":"강소연","userId":"16433369612605492183"},"user_tz":-540},"id":"f47f9584-3988-46b8-a062-29dcde75a0e2","outputId":"7aeb6dd0-6585-41a5-a0ea-513ef7a400ee"},"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":"cf4d3b7afacc4518a0f174bcab0e59b6"}},"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":"43e4816eb0004d32968d6687cfa6ae4b"}},"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":"1dda1a4ce4034fcba23beb66a6934664"}},"metadata":{}}],"source":["tokenizer = AutoTokenizer.from_pretrained(\n","    'KT-AI/midm-bitext-S-7B-inst-v1',\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":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":606},"executionInfo":{"elapsed":356,"status":"error","timestamp":1702304759090,"user":{"displayName":"강소연","userId":"16433369612605492183"},"user_tz":-540},"id":"62e8139f-5179-4c75-84a7-0c818ab0a35a","outputId":"598de3f5-3dcc-46a3-9811-9abfdbb9cf6d"},"outputs":[{"output_type":"stream","name":"stderr","text":["  0%|          | 0/400 [00:00<?, ?it/s]\n"]},{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-22-81375ac8d032>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_datasets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscript_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m<ipython-input-9-86534dea5c6d>\u001b[0m in \u001b[0;36mcreate_datasets\u001b[0;34m(tokenizer, args)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mtrain_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mchars_per_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchars_token_ratio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"The character to token ratio of the dataset is: {chars_per_token:.2f}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-7-1d81ce8f89e0>\u001b[0m in \u001b[0;36mchars_token_ratio\u001b[0;34m(dataset, tokenizer, nb_examples)\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mtotal_characters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexample\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_examples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_examples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprepare_sample_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0mtotal_characters\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_fast\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-8-8ccd466ec9fc>\u001b[0m in \u001b[0;36mprepare_sample_text\u001b[0;34m(example)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     text = (\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0mprompt_template\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSystem\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdefault_system_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUser\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"document\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mMidm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"긍정\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mexample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"label\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"부정\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     )\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'document'"]}],"source":["train_dataset = create_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":3,"status":"ok","timestamp":1702304002306,"user":{"displayName":"강소연","userId":"16433369612605492183"},"user_tz":-540},"id":"000314e9-f10b-4685-8da6-0511494a9eb4","outputId":"3eab8cdd-b10c-4e26-c893-47ea962b6305"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["3200"]},"metadata":{},"execution_count":22}],"source":["len(train_dataset)"],"id":"000314e9-f10b-4685-8da6-0511494a9eb4"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"executionInfo":{"elapsed":2,"status":"error","timestamp":1702304003755,"user":{"displayName":"강소연","userId":"16433369612605492183"},"user_tz":-540},"id":"4ba80a64-0ec7-4b29-ac95-7b3d34549f17","outputId":"ad7dcefc-d0bc-443e-ed0c-1ba663dbf2ad"},"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-23-092984174f31>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m trainer = SFTTrainer(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0meval_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mpeft_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpeft_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, args, data_collator, train_dataset, eval_dataset, tokenizer, model_init, compute_metrics, callbacks, optimizers, preprocess_logits_for_metrics, peft_config, dataset_text_field, packing, formatting_func, max_seq_length, infinite, num_of_sequences, chars_per_token, dataset_num_proc, dataset_batch_size, neftune_noise_alpha, model_init_kwargs)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient_checkpointing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_peft_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpeft_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/mapping.py\u001b[0m in \u001b[0;36mget_peft_model\u001b[0;34m(model, peft_config, adapter_name, mixed)\u001b[0m\n\u001b[1;32m    131\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpeft_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_prompt_learning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[0mpeft_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_prepare_prompt_learning_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeft_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mMODEL_TYPE_TO_PEFT_MODEL_MAPPING\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpeft_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_type\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpeft_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0madapter_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, peft_config, adapter_name)\u001b[0m\n\u001b[1;32m   1039\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1040\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpeft_config\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mPeftConfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"default\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1041\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpeft_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1042\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model_prepare_inputs_for_generation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_inputs_for_generation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, peft_config, adapter_name)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_peft_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m             \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPEFT_TYPE_TO_MODEL_MAPPING\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpeft_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpeft_type\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0madapter_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpeft_config\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_additional_trainable_modules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeft_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, config, adapter_name)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_new_adapter_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLoraConfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/tuners/tuners_utils.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, peft_config, adapter_name)\u001b[0m\n\u001b[1;32m     93\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"model_type\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"custom\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minject_adapter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m         \u001b[0;31m# Copy the peft_config in the injected model.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/tuners/tuners_utils.py\u001b[0m in \u001b[0;36minject_adapter\u001b[0;34m(self, model, adapter_name)\u001b[0m\n\u001b[1;32m    250\u001b[0m                 \u001b[0;34m\"current_key\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m             }\n\u001b[0;32m--> 252\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_and_replace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeft_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptional_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_target_modules_in_base_model\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/model.py\u001b[0m in \u001b[0;36m_create_and_replace\u001b[0;34m(self, lora_config, adapter_name, target, target_name, parent, current_key, **optional_kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m             )\n\u001b[1;32m    199\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m             \u001b[0mnew_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_new_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlora_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0madapter_name\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactive_adapter\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m                 \u001b[0;31m# adding an additional adapter: it is not automatically trainable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/model.py\u001b[0m in \u001b[0;36m_create_new_module\u001b[0;34m(lora_config, adapter_name, target, **kwargs)\u001b[0m\n\u001b[1;32m    284\u001b[0m             fourbit_kwargs.update(\n\u001b[1;32m    285\u001b[0m                 {\n\u001b[0;32m--> 286\u001b[0;31m                     \u001b[0;34m\"compute_dtype\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m                     \u001b[0;34m\"compress_statistics\"\u001b[0m\u001b[0;34m:\u001b[0m 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\u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1695\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"'{type(self).__name__}' object has no attribute '{name}'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1697\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Module'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'Linear4bit' object has no attribute 'compute_dtype'"]}],"source":["trainer = SFTTrainer(\n","    model=base_model,\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/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1700152410163,"user":{"displayName":"오장민 (AI융합학부)","userId":"14191339932351591583"},"user_tz":-540},"id":"gw9xbeUgbZEo","outputId":"a0136813-a258-46e7-f7a2-2dc7c1c378e6"},"outputs":[{"data":{"text/plain":["device(type='cuda', index=0)"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["base_model.device"],"id":"gw9xbeUgbZEo"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1700152410163,"user":{"displayName":"오장민 (AI융합학부)","userId":"14191339932351591583"},"user_tz":-540},"id":"edb204be-ec15-4800-af49-6cfbad2f7f9a","outputId":"447ad5f8-b601-4915-ef75-1557d0d70198"},"outputs":[{"name":"stdout","output_type":"stream","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":"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":493},"executionInfo":{"elapsed":938230,"status":"ok","timestamp":1700153348391,"user":{"displayName":"오장민 (AI융합학부)","userId":"14191339932351591583"},"user_tz":-540},"id":"14019fa9-0c6f-4729-ac99-0d407af375b8","outputId":"6fbf488d-fbca-4c57-de4b-2ce8ccfe455d"},"outputs":[{"data":{"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","    "],"text/plain":["<IPython.core.display.Javascript object>"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","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"]},{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"]},{"data":{"text/html":["Tracking run with wandb version 0.16.0"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Run data is saved locally in <code>/content/wandb/run-20231116_163415-mthnd6sk</code>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Syncing run <strong><a href='https://wandb.ai/ozlab/huggingface/runs/mthnd6sk' target=\"_blank\">midm-7b-food-order-understanding</a></strong> to <a href='https://wandb.ai/ozlab/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[" View project at <a href='https://wandb.ai/ozlab/huggingface' target=\"_blank\">https://wandb.ai/ozlab/huggingface</a>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[" View run at <a href='https://wandb.ai/ozlab/huggingface/runs/mthnd6sk' target=\"_blank\">https://wandb.ai/ozlab/huggingface/runs/mthnd6sk</a>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='300' max='300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [300/300 14:42, 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>1.043500</td>\n","    </tr>\n","    <tr>\n","      <td>100</td>\n","      <td>0.547800</td>\n","    </tr>\n","    <tr>\n","      <td>150</td>\n","      <td>0.505000</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>0.495700</td>\n","    </tr>\n","    <tr>\n","      <td>250</td>\n","      <td>0.518000</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>0.497200</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["TrainOutput(global_step=300, training_loss=0.6011957613627116, metrics={'train_runtime': 937.8508, 'train_samples_per_second': 0.64, 'train_steps_per_second': 0.32, 'total_flos': 9315508499251200.0, 'train_loss': 0.6011957613627116, 'epoch': 0.19})"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["trainer.train()"],"id":"14019fa9-0c6f-4729-ac99-0d407af375b8"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1700153348391,"user":{"displayName":"오장민 (AI융합학부)","userId":"14191339932351591583"},"user_tz":-540},"id":"3Y4FQSyRghQt","outputId":"9006abad-b204-4369-97b0-f074ded79e94"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'/gdrive/MyDrive/Lectures/2023/nlp/lora-midm-7b-food-order-understanding'"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"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 매장에서 고객이 음식을 주문하는 주문 문장이다. 이를 분석하여 음식명, 옵션명, 수량을 추출하여 고객의 의도를 이해하고자 한다.\n","분석 결과를 완성해주기 바란다.\n","\n","### 주문 문장: {0} ### 분석 결과:\n","\"\"\"\n","\n","prompt_template = \"\"\"###System;{System}\n","###User;{User}\n","###Midm;\"\"\"\n","\n","default_system_msg = (\n","    \"너는 먼저 사용자가 입력한 주문 문장을 분석하는 에이전트이다. 이로부터 주문을 구성하는 음식명, 옵션명, 수량을 차례대로 추출해야 한다.\"\n",")"],"id":"52626888-1f6e-46b6-a8dd-836622149ff5"},{"cell_type":"code","execution_count":null,"metadata":{"id":"46e844fa-8f63-4359-a4fb-df66e8171796"},"outputs":[],"source":["evaluation_queries = [\n","    \"오늘은 비가오니깐 이거 먹자. 삼선짬뽕 곱배기 하나하구요, 사천 탕수육 중짜 한그릇 주세요.\",\n","    \"아이스아메리카노 톨사이즈 한잔 하고요. 딸기스무디 한잔 주세요. 또, 콜드브루라떼 하나요.\",\n","    \"참이슬 한병, 코카콜라 1.5리터 한병, 테슬라 한병이요.\",\n","    \"꼬막무침 1인분하고요, 닭도리탕 중자 주세요. 그리고 소주도 한병 주세요.\",\n","    \"김치찌개 3인분하고요, 계란말이 주세요.\",\n","    \"불고기버거세트 1개하고요 감자튀김 추가해주세요.\",\n","    \"불닭볶음면 1개랑 사리곰탕면 2개 주세요.\",\n","    \"카페라떼 아이스 샷추가 한잔하구요. 스콘 하나 주세요\",\n","    \"여기요 춘천닭갈비 4인분하고요. 라면사리 추가하겠습니다. 콜라 300ml 두캔주세요.\",\n","    \"있잖아요 조랭이떡국 3인분하고요. 떡만두 한세트 주세요.\",\n","    \"깐풍탕수 2인분 하고요 콜라 1.5리터 한병이요.\",\n","]"],"id":"46e844fa-8f63-4359-a4fb-df66e8171796"},{"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","            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","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":512427,"status":"ok","timestamp":1700153863386,"user":{"displayName":"오장민 (AI융합학부)","userId":"14191339932351591583"},"user_tz":-540},"id":"eaac1f6f-c823-4488-8edb-2f931ddf0daa","outputId":"602f1e87-c5cc-4291-ba19-8ce217ceaf33"},"outputs":[{"name":"stderr","output_type":"stream","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","/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.5` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n","  warnings.warn(\n"]},{"name":"stdout","output_type":"stream","text":["- 분석 결과 0: 음식명:삼선짬뽕, 옵션:곱배기, 수량:하나<[!newline]>- 분석 결과 1: 음식명:사천 탕수육, 옵션:중짜, 수량:한그릇\n","- 분석 결과 0: 음식명:아이스아메리카노,옵션:톨사이즈,수량:한잔<[!newline]>- 분석 결과 1: 음식명:딸기스무디,수량:한잔<[!newline]>- 분석 결과 2: 음식명:콜드브루라떼,수량:하나\n","- 분석 결과 0: 음식명:참이슬,수량:한병<[!newline]>- 분석 결과 1: 음식명:코카콜라,옵션:1.5리터,수량:한병<[!newline]>- 분석 결과 2: 음식명:테슬라,수량:한병\n","- 분석 결과 0: 음식명:꼬막무침, 수량:1인분<[!newline]>- 분석 결과 1: 음식명:닭도리탕, 옵션:중자<[!newline]>- 분석 결과 2: 음식명:소주, 수량:한병\n","- 분석 결과 0: 음식명:김치찌개, 수량:3인분<[!newline]>- 분석 결과 1: 음식명:계란말이\n","- 분석 결과 0: 음식명:불고기버거세트, 수량:1개<[!newline]>- 분석 결과 1: 음식명:감자튀김, 수량:추가\n","- 분석 결과 0: 음식명:불닭볶음면,수량:1개<[!newline]>- 분석 결과 1: 음식명:사리곰탕면,수량:2개\n","- 분석 결과 0: 음식명:카페라떼, 옵션:아이스, 샷추가, 수량:한잔<[!newline]>- 분석 결과 1: 음식명:스콘, 수량:하나\n","- 분석 결과 0: 음식명:춘천닭갈비, 수량:4인분<[!newline]>- 분석 결과 1: 음식명:라면사리, 수량:추가<[!newline]>- 분석 결과 2: 음식명:콜라, 옵션:300ml, 수량:두캔\n","- 분석 결과 0: 음식명:조랭이떡국, 수량:3인분<[!newline]>- 분석 결과 1: 음식명:떡만두, 수량:한세트\n","- 분석 결과 0: 음식명:깐풍탕수, 수량:2인분<[!newline]>- 분석 결과 1: 음식명:콜라, 옵션:1.5리터, 수량:한병\n"]}],"source":["eval_dic = {i:wrapper_generate(model=base_model, input_prompt=prompt_template.format(System=default_system_msg, User=evaluation_queries[i]))for i, query in enumerate(evaluation_queries)}"],"id":"eaac1f6f-c823-4488-8edb-2f931ddf0daa"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1700153863386,"user":{"displayName":"오장민 (AI융합학부)","userId":"14191339932351591583"},"user_tz":-540},"id":"fefd04ba-2ed8-4f84-bdd0-86d52b3f39f6","outputId":"96c760f6-fe45-4fa0-b9ec-b3405e4f915b"},"outputs":[{"name":"stdout","output_type":"stream","text":["요.<[!newline]>###Midm;- 분석 결과 0: 음식명:삼선짬뽕, 옵션:곱배기, 수량:하나<[!newline]>- 분석 결과 1: 음식명:사천 탕수육, 옵션:중짜, 수량:한그릇\n"]}],"source":["print(eval_dic[0])"],"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","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":"code","execution_count":null,"metadata":{"id":"39db2ee4-23c8-471f-89b2-bca34964bf81"},"outputs":[],"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":"code","execution_count":null,"metadata":{"id":"b0b75ca4-730d-4bde-88bb-a86462a76d52"},"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","trained_model.config.pad_token_id = tokenizer.pad_token_id"],"id":"b0b75ca4-730d-4bde-88bb-a86462a76d52"},{"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":{"id":"e374555b-9f8a-4617-8ea7-c1e6ee1b2999"},"outputs":[],"source":["eval_dic = {i:wrapper_generate(model=trained_model, input_prompt=prompt_template.format(System=default_system_msg, User=evaluation_queries[i]))for i, query in enumerate(evaluation_queries)}"],"id":"e374555b-9f8a-4617-8ea7-c1e6ee1b2999"},{"cell_type":"code","execution_count":null,"metadata":{"id":"5d055bb0-5e5f-4221-a634-45d903c0f3b5"},"outputs":[],"source":["print(eval_dic[0])"],"id":"5d055bb0-5e5f-4221-a634-45d903c0f3b5"},{"cell_type":"code","execution_count":null,"metadata":{"id":"Z6tBUSgQiU1i"},"outputs":[],"source":[],"id":"Z6tBUSgQiU1i"}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"kernelspec":{"display_name":"Python 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