lecture-llama-2-7B-food-order-understanding (1) (1).ipynb

{"cells":[{"cell_type":"markdown","id":"28e4c4d1-a73f-437b-a1bd-c2cc3874924a","metadata":{"id":"28e4c4d1-a73f-437b-a1bd-c2cc3874924a"},"source":["# 강의 11주차: llama2-food-order-understanding\n","\n","1. llama-2-7b-chat-hf 를 주문 문장 이해에 미세 튜닝\n","\n","- food-order-understanding-small-3200.json (학습)\n","- food-order-understanding-small-800.json (검증)\n","\n","\n","종속적인 필요 내용\n","- huggingface 계정 설정 및 llama-2 사용 승인\n","- 로깅을 위한 wandb"]},{"cell_type":"code","source":["pip install transformers peft accelerate optimum bitsandbytes trl wandb"],"metadata":{"id":"nDZe_wqKU6J3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702298092542,"user_tz":-540,"elapsed":28936,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"b1b9d5bf-2b71-4974-fb52-304fa901564a"},"id":"nDZe_wqKU6J3","execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in 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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"]}]},{"cell_type":"code","execution_count":2,"id":"51eb00d7-2928-41ad-9ae9-7f0da7d64d6d","metadata":{"id":"51eb00d7-2928-41ad-9ae9-7f0da7d64d6d","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702298115386,"user_tz":-540,"elapsed":19506,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"287a72a9-e173-419f-d057-3b06d7c1dce4"},"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"]},{"cell_type":"code","source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"],"metadata":{"id":"tX7gYxZaVhYL","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["22e8a9d9cda84d92b1813f8929f4eea0","657a33fb2b544f85930194969feed3c4","8e305c09fb884c35b4eff11a101541ea","f23410ca73f04660ac6428a3eb2f0487","6c4553801fb54d9495d5ee3636de9ce4","1565660880f4460993bceaf233525be8","36948d25a53d4084a82c91fb37e1c1b8","e47ec0f7ec7d45708dfcf38ba066c84e","8b1aa9a9d061465f932b30ac87495e3b","f76f0c3de150487cb028fac16e3638cb","1a24b471df0c4e0b82f0394d4bbda2d8","9e29fc343f1143158f8cd0a42394f133","312b7ba4d0d34e8c98f5737b35fc0e75","feed29fb48db4887b5e39a3662efa4eb","84ead8c6a8f34b9a98f6dc7e48544bf7","5ebfe7fb5c914c36b47e501bccb8c4d5","ef576e415f4045c6a66bc5056f169b33","4f4a174309c94b1faafe6f6bbff4f08e","81d2e99eeece47c2a0f27d915b55afee","7d2e6f866f074889901fd7093e0bb81a","61f2d328438c4df987de5376b9e48bb4","86e6cb6b34a34a19846b6452a1cf3d11","04a8cbf69f7b402188a76dd560c38b44","2b1cbbfb38954d1ab819b564e5db29c6","a54ab155dddf45fdaaeefdf1d574613d","8d583097f2ec413fa25de9c5f1ecfcfd","845eb52754a549238112bae5c0c1b751","e787c4ef3a3e4306876093df43d5fa8d","7ef0269e33204383bf4213ac14c048a2","6e052d1117ae48e6b469b0051d5a8e13","4d84f767af3d45af94b72234127f4788","c61999ab9f194d4a89ec849308f4eaa4"]},"executionInfo":{"status":"ok","timestamp":1702298118857,"user_tz":-540,"elapsed":347,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"74676d5a-7075-4a3e-d19a-33e2df450021"},"id":"tX7gYxZaVhYL","execution_count":3,"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":"22e8a9d9cda84d92b1813f8929f4eea0"}},"metadata":{}}]},{"cell_type":"markdown","source":["드라이브 마운트 후 파일 업로드\n","- food-order-understanding-small-3200.json\n","- food-order-understanding-small-800.json"],"metadata":{"id":"FuKA5uZihmdh"},"id":"FuKA5uZihmdh"},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/gdrive')"],"metadata":{"id":"4DF9D2SXVpHP","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702298150996,"user_tz":-540,"elapsed":22674,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"aadb9e70-955d-46de-a415-393c968af8e0"},"id":"4DF9D2SXVpHP","execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /gdrive\n"]}]},{"cell_type":"code","source":["# /gdrive/MyDrive/food-order-understanding-small-3200.json\n","# /gdrive/MyDrive/food-order-understanding-small-800.json"],"metadata":{"id":"VFgITUI8WjKe"},"id":"VFgITUI8WjKe","execution_count":null,"outputs":[]},{"cell_type":"markdown","id":"036eece3-5f89-4fec-b0cd-268478b5e83d","metadata":{"id":"036eece3-5f89-4fec-b0cd-268478b5e83d"},"source":["# 매개 변수 설정"]},{"cell_type":"code","execution_count":5,"id":"e03d01b5-eaeb-4626-9dc5-47c3691e7fcf","metadata":{"id":"e03d01b5-eaeb-4626-9dc5-47c3691e7fcf","executionInfo":{"status":"ok","timestamp":1702298152631,"user_tz":-540,"elapsed":8,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"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=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"down_proj\", \"up_proj\", \"gate_proj\"],\n","            bias=\"none\",\n","            task_type=\"CAUSAL_LM\",\n","        )\n","    )\n","\n","    merge_with_final_checkpoint: Optional[bool] = field(\n","        default=False, metadata={\"help\": \"Do only merge with final checkpoint\"}\n","    )"]},{"cell_type":"markdown","id":"b0b34850-006c-4c87-a7d5-27c6c871e7de","metadata":{"id":"b0b34850-006c-4c87-a7d5-27c6c871e7de"},"source":["# 유틸리티"]},{"cell_type":"code","execution_count":6,"id":"8224d213-8766-4b40-899e-1a1b8d164365","metadata":{"id":"8224d213-8766-4b40-899e-1a1b8d164365","executionInfo":{"status":"ok","timestamp":1702298156021,"user_tz":-540,"elapsed":429,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"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","    )"]},{"cell_type":"markdown","id":"58085944-b951-4c9b-bdeb-0ccc60c2a3b3","metadata":{"id":"58085944-b951-4c9b-bdeb-0ccc60c2a3b3"},"source":["# 데이터 로딩"]},{"cell_type":"code","execution_count":7,"id":"b49c3470-480e-4ff2-b2c5-fcf1d3a13fba","metadata":{"id":"b49c3470-480e-4ff2-b2c5-fcf1d3a13fba","executionInfo":{"status":"ok","timestamp":1702298158063,"user_tz":-540,"elapsed":6,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"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 먼저 사용자가 입력한 주문 문장을 분석하는 에이전트이다. 이로부터 주문을 구성하는 음식명, 옵션명, 수량을 차례대로 추출해야 한다.\"\n","    )\n","\n","    text = (\n","        prompt_template.format(System=default_system_msg, User=example[\"input\"],Midm=example[\"output\"])\n","    )\n","\n","    return text"]},{"cell_type":"code","execution_count":8,"id":"5d9abfb4-339d-414b-855b-ced51631b752","metadata":{"id":"5d9abfb4-339d-414b-855b-ced51631b752","executionInfo":{"status":"ok","timestamp":1702298160698,"user_tz":-540,"elapsed":2,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"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"]},{"cell_type":"markdown","id":"2e7ef79d-a354-4c80-9435-c130ffed9e32","metadata":{"id":"2e7ef79d-a354-4c80-9435-c130ffed9e32"},"source":["# 미세 튜닝용 모델 로딩"]},{"cell_type":"code","execution_count":9,"id":"bf4c65e0-5ffa-4a20-9e78-6c1f030572ff","metadata":{"id":"bf4c65e0-5ffa-4a20-9e78-6c1f030572ff","executionInfo":{"status":"ok","timestamp":1702298162678,"user_tz":-540,"elapsed":9,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"outputs":[],"source":["script_args = ScriptArguments(\n","    num_workers=2,\n","    seq_length=512,\n","    dataset_name='/gdrive/MyDrive/food-order-understanding-small-3200.json',\n","    model_name='meta-llama/Llama-2-7b-chat-hf',\n","    )"]},{"cell_type":"code","execution_count":10,"id":"372c64be-8fc0-4cc8-bda8-92ecf3632cc3","metadata":{"id":"372c64be-8fc0-4cc8-bda8-92ecf3632cc3","executionInfo":{"status":"ok","timestamp":1702298165430,"user_tz":-540,"elapsed":367,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"outputs":[],"source":["script_args.training_args.logging_steps = 100\n","# script_args.training_args.max_steps = 100\n","script_args.training_args.output_dir = '/gdrive/MyDrive/lora-llama-2-7b-food-order-understanding'\n","script_args.training_args.run_name = 'llama-2-7b-food-order-understanding'"]},{"cell_type":"code","execution_count":11,"id":"bac62c01-21ef-491e-a686-cf4988186c58","metadata":{"tags":[],"id":"bac62c01-21ef-491e-a686-cf4988186c58","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702298167985,"user_tz":-540,"elapsed":403,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"cde9b6fa-c706-4d9b-c50b-f03817da9e65"},"outputs":[{"output_type":"stream","name":"stdout","text":["ScriptArguments(cache_dir=None, model_name='meta-llama/Llama-2-7b-chat-hf', dataset_name='/gdrive/MyDrive/food-order-understanding-small-3200.json', seq_length=512, num_workers=2, training_args=TrainingArguments(\n","_n_gpu=1,\n","adafactor=False,\n","adam_beta1=0.9,\n","adam_beta2=0.999,\n","adam_epsilon=1e-08,\n","auto_find_batch_size=False,\n","bf16=False,\n","bf16_full_eval=False,\n","data_seed=None,\n","dataloader_drop_last=False,\n","dataloader_num_workers=0,\n","dataloader_pin_memory=True,\n","ddp_backend=None,\n","ddp_broadcast_buffers=None,\n","ddp_bucket_cap_mb=None,\n","ddp_find_unused_parameters=None,\n","ddp_timeout=1800,\n","debug=[],\n","deepspeed=None,\n","disable_tqdm=False,\n","dispatch_batches=None,\n","do_eval=False,\n","do_predict=False,\n","do_train=False,\n","eval_accumulation_steps=None,\n","eval_delay=0,\n","eval_steps=None,\n","evaluation_strategy=no,\n","fp16=True,\n","fp16_backend=auto,\n","fp16_full_eval=False,\n","fp16_opt_level=O1,\n","fsdp=[],\n","fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},\n","fsdp_min_num_params=0,\n","fsdp_transformer_layer_cls_to_wrap=None,\n","full_determinism=False,\n","gradient_accumulation_steps=2,\n","gradient_checkpointing=False,\n","gradient_checkpointing_kwargs=None,\n","greater_is_better=None,\n","group_by_length=False,\n","half_precision_backend=auto,\n","hub_always_push=False,\n","hub_model_id=None,\n","hub_private_repo=False,\n","hub_strategy=every_save,\n","hub_token=<HUB_TOKEN>,\n","ignore_data_skip=False,\n","include_inputs_for_metrics=False,\n","include_tokens_per_second=False,\n","jit_mode_eval=False,\n","label_names=None,\n","label_smoothing_factor=0.0,\n","learning_rate=0.0001,\n","length_column_name=length,\n","load_best_model_at_end=False,\n","local_rank=0,\n","log_level=passive,\n","log_level_replica=warning,\n","log_on_each_node=True,\n","logging_dir=./results/runs/Dec11_12-36-02_a6ae71e673f2,\n","logging_first_step=False,\n","logging_nan_inf_filter=True,\n","logging_steps=100,\n","logging_strategy=steps,\n","lr_scheduler_type=cosine,\n","max_grad_norm=0.3,\n","max_steps=-1,\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/lora-llama-2-7b-food-order-understanding,\n","overwrite_output_dir=False,\n","past_index=-1,\n","per_device_eval_batch_size=1,\n","per_device_train_batch_size=1,\n","prediction_loss_only=False,\n","push_to_hub=False,\n","push_to_hub_model_id=None,\n","push_to_hub_organization=None,\n","push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n","ray_scope=last,\n","remove_unused_columns=False,\n","report_to=['wandb'],\n","resume_from_checkpoint=None,\n","run_name=llama-2-7b-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={'v_proj', 'q_proj', 'gate_proj', 'k_proj', 'o_proj', 'up_proj', 'down_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)"]},{"cell_type":"code","execution_count":12,"id":"1ff1422e-184d-4438-b033-40ae8bdaa5fd","metadata":{"id":"1ff1422e-184d-4438-b033-40ae8bdaa5fd","executionInfo":{"status":"ok","timestamp":1702298170836,"user_tz":-540,"elapsed":504,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"outputs":[],"source":["bnb_config = BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch.bfloat16,\n",")"]},{"cell_type":"code","execution_count":15,"id":"15c8425e-bb0b-40c5-bfe8-385bac699b9d","metadata":{"tags":[],"id":"15c8425e-bb0b-40c5-bfe8-385bac699b9d","colab":{"base_uri":"https://localhost:8080/","height":330,"referenced_widgets":["a9f64069eff34d53bc540e3caf488639","0bc589d64ec24bdca623987480e03e25","d30211fb0bdc4777a10f51d8af87291e","c7f092b487dd48c691ebe6c07d016b54","b07cc6d3714a44899a4633b815b0016b","cc00163d2efa4945ad1d2169a662e431","591791fc45d24c5e8ad9580c49ca1cf3","abbb2403cd664d7cb5213374869284f6","3afb1e60a33149508680e4c6074da7b6","59ebcf055f1f40408729de3e1b12805a","2181a182d69d4e09b51b95a95daffeeb","6ab2d23e42ea4dca8e7db2e29ae061ca","0b43ddc1c310411e9f0567f082b845b7","36d3bdf3ed9b4c24adffe9a8965179ad","3f21789a752b4eb3bb8bf2be7b3ed617","f7387ed5458d40dcb2da60c72d2950fa","d4ce2b21febb4cbbb43761328c9a0d52","21531b944c454c7fb9912023594809ad","0266db5c2d3f4f0f922a2e8e5196a5c6","9fd7d597dfbf4929a193735eb585473b","48f3f737b1f04c0e8ff5a9d6b6c85538","30c09548aef94b36b118754c640333ef","f19b706b3f194339af66f6db9d1a8100","e94d3b3e80404160b9f5c8952cb97d6a","7c7056fa1ca74a15bbea2679e19154e5","9ea94a0c2c064805974f9a73c8683d76","1a7913b4901a4f09ade59f286b548e6e","73fa4994a8f44cf7bb07503432db19db","166dc373273f4b619002824cdd06009a","2bf147c3443b4d89a19b3186fa7b0651","def2bb11041643dba7adac942f15a1bc","cb4f4ee4afe648139d2a436e8190adf2","f36ef64cb14c45219c8ea4007db710ca","5cca4d64278545869b715b222591b84c","446b4026740f4ff59e9b81371e81caf9","5bdc2d0fd7044c379a759cfa38f8268c","a817a3713fac4994a047c44249729a98","f9d012cad9e84a6383b990b6fe6fb242","f23771b981b44e49a77a670c67dedd11","7359c4bb484f48fe9714263ee6157fa3","04c96bcf060a4da6b8cef8c8d1c27db7","721117ab7cff4b1c97384139da136bd0","03fe49624c344dbebadc7cb08b59d63b","03a585c527314424accd3fbc365a83c9","45940b96451f433e8818963c4594e1fc","d294475d227c4bbd89cebd947b80b5f7","765f90da58ea470cb797830b1ce71733","7c3830b5aa254fdf96acd9014dc13ac5","d27b777ebed344ab9d198fdd5f5c5079","bddb105f65994db091ee4eec8fdcbe19","74b5b842c5e04708b9a27bea69918033","02aed0b5815d47999aa4a2b284ca96c7","b9c8a1dcbc7248e08c0871a2aac4f4a0","2fa5cd827c6b400e81f3d14f9ebaae0c","4ea36756311e4361a75ec4ed30c6a623","df528ae6efc64e78a5d853745afb80aa","d3db1da8732c49a0b7c3ee3d9d6adf7b","30f6081c40cb4356b89004a18e8133ab","b439e623ff3a4303a57c3bb1c5b4b2f7","ae515a5e15a2484f93f7dc70e6ea1c71","900fabe1fe444143a8eaf5d6e26a861d","bc46288914da4f7393b5c1764fac84a4","ce9be6e3e109437db5ff437fb472625c","1ac8f543e34e43d49deb7cf62e93d345","92a9503355ff440898a6fdfdd3d0b4b5","e6b32896af044f6587ed3af003237ba9","c9d638ca4fc44a57b992180733550654","7152c67b82764b829c08d6cdd46c079e","35aacdbc64a54a37a849a59b85892def","7494fd5de21a4265a59f7bf3787e8d8a","58632e93bc364527a607f6323f9a5a3c","d7d25fab102c473dbfc6b3cf06ce0bad","b2fede7156cc4a9281dbd3ebcec268e7","b808d33260834b0d8aea909e66043eec","839167e730bb48ba909fceacff0661d9","7c02c4e70afb4151876adced52f55651","3a8ee0e1eb4f465d9aa3e71c3ed50d0c"]},"executionInfo":{"status":"ok","timestamp":1702298672268,"user_tz":-540,"elapsed":172262,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"4e9965e8-315e-40f0-85d0-3aedf7e67883"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:472: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a9f64069eff34d53bc540e3caf488639"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6ab2d23e42ea4dca8e7db2e29ae061ca"}},"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":"f19b706b3f194339af66f6db9d1a8100"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5cca4d64278545869b715b222591b84c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"45940b96451f433e8818963c4594e1fc"}},"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":"df528ae6efc64e78a5d853745afb80aa"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:374: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/188 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c9d638ca4fc44a57b992180733550654"}},"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"]},{"cell_type":"code","source":["base_model"],"metadata":{"id":"E9D239NqbDba","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702298684565,"user_tz":-540,"elapsed":485,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"8a34c750-ec3b-4777-9af7-7f02d9426fe4"},"id":"E9D239NqbDba","execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LlamaForCausalLM(\n","  (model): LlamaModel(\n","    (embed_tokens): Embedding(32000, 4096)\n","    (layers): ModuleList(\n","      (0-31): 32 x LlamaDecoderLayer(\n","        (self_attn): LlamaAttention(\n","          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (k_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (v_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (rotary_emb): LlamaRotaryEmbedding()\n","        )\n","        (mlp): LlamaMLP(\n","          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n","          (act_fn): SiLUActivation()\n","        )\n","        (input_layernorm): LlamaRMSNorm()\n","        (post_attention_layernorm): LlamaRMSNorm()\n","      )\n","    )\n","    (norm): LlamaRMSNorm()\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",")"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","execution_count":17,"id":"d37b485f-4fd3-404f-ab02-2bf3e93b3fc2","metadata":{"tags":[],"id":"d37b485f-4fd3-404f-ab02-2bf3e93b3fc2","executionInfo":{"status":"ok","timestamp":1702298687555,"user_tz":-540,"elapsed":363,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"outputs":[],"source":["peft_config = script_args.peft_config"]},{"cell_type":"code","execution_count":18,"id":"4420fcc4-2bac-413d-b7aa-89455c512419","metadata":{"id":"4420fcc4-2bac-413d-b7aa-89455c512419","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702298689082,"user_tz":-540,"elapsed":7,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"af5c2414-d1a5-429d-cb85-1f3d0b9591c5"},"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={'v_proj', 'q_proj', 'gate_proj', 'k_proj', 'o_proj', 'up_proj', 'down_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":18}],"source":["peft_config"]},{"cell_type":"code","execution_count":19,"id":"f47f9584-3988-46b8-a062-29dcde75a0e2","metadata":{"id":"f47f9584-3988-46b8-a062-29dcde75a0e2","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["c9cb63d7744b4b58b68d966afdb4bc86","8d2d054424e24a2283c6762abbbeedcf","d2d97e0cb7544e52bd99b6a3cbe40084","4ea576baf3a04854856ccb412d1e02ac","fbbae75661bc46199ff95bf79f218914","6d2a3f65a5784167b8cc14c4ee998195","6c028f99a33d4b7097a466a534af34f4","0fbf363db8144a4ca291e3564e584cf0","05fb6b99333049d2b2987a3679244003","0bcf2fa8efd94eb993a39da57df50099","43449a979df74e43818d1e27f0f22eab","933b2dcbd84c41a39d458f02c5d41e7a","9960a5c9f1314af5aa989845a7364c5e","73b29e715fbb42bc8d0fa30aa97de57a","03a84609f76146798e72278437d0e424","05b2abef473b46f5ae4996e770327764","8bfeb990c1fc4d159efc47c659eda222","c88dbd6d3ff947eb8eebc150f33dea1f","15f1e493ad4249a6a057e8b991827375","bac83c3df4e54536b3ba0c9dc05baaec","232510716572400e85df728b58b621c3","7b4cc79736b0437a8acba262a52cf0e9","0b2f78c952674c2488cb1971c63c1a7a","794c090d0fff4f7298c31ad1b40f0323","b450e78b76ca4d4b9c30a3a915842b27","fb3394b8e308489a8ed877ef39fd69c4","9ae552408bfa494999cbf250eaf7f0a2","12dba2ba6ac64f1390e9ac9e90ebc8ca","9b56f34621ed48acbbeb2ab1a6cbb18e","95e89f38ec8e432b9c92e9a0be048c95","cba92981928e4853bcdfb369ec2db977","6ec16680720246598ae73eedc572f3e6","ef9652736e474763a9401457af3ae8e0","2facc98e5a9a497aac2e524555eda699","fee152b86fb542298a62109835bc6f84","07385bdefb604ce789dc3b51fe0b0f88","4f4961b2718d451298f01d39d5fb887d","73c4c2a4323c48ef81f21ea67dcc0ab4","ec1f7e31b9fd40e3a9dd2d8d3ff86574","3d1239f4683e47b8b19b60aa4cef27d2","845d29560c334a05a2d107c72891fe44","d8540e4f76a24c39b97e0f4f5ba71c4e","c024e01ab4cc4bf8806fdd39b9af56f6","d2d144239a7540c69e96c682d4fd6a0a"]},"executionInfo":{"status":"ok","timestamp":1702298692333,"user_tz":-540,"elapsed":1356,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"2e36892c-6f04-46e0-aad8-47ce7912f6b8"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c9cb63d7744b4b58b68d966afdb4bc86"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"933b2dcbd84c41a39d458f02c5d41e7a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0b2f78c952674c2488cb1971c63c1a7a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2facc98e5a9a497aac2e524555eda699"}},"metadata":{}}],"source":["tokenizer = AutoTokenizer.from_pretrained(\n","    script_args.model_name,\n","    trust_remote_code=True,\n","    cache_dir=script_args.cache_dir,\n",")\n","\n","if getattr(tokenizer, \"pad_token\", None) is None:\n","    tokenizer.pad_token = tokenizer.eos_token\n","tokenizer.padding_side = \"right\"  # Fix weird overflow issue with fp16 training\n","\n","base_model.config.pad_token_id = tokenizer.pad_token_id"]},{"cell_type":"code","execution_count":20,"id":"abd17c83-ab8d-44cb-b69b-fc0936c2cec5","metadata":{"id":"abd17c83-ab8d-44cb-b69b-fc0936c2cec5","executionInfo":{"status":"ok","timestamp":1702298695180,"user_tz":-540,"elapsed":317,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"outputs":[],"source":["training_args = script_args.training_args"]},{"cell_type":"code","execution_count":21,"id":"62e8139f-5179-4c75-84a7-0c818ab0a35a","metadata":{"tags":[],"id":"62e8139f-5179-4c75-84a7-0c818ab0a35a","colab":{"base_uri":"https://localhost:8080/","height":202,"referenced_widgets":["62a06a2ba5fd443184cb895e0d973ec6","739012b14ae3423e816bd99b27f769b5","8098761b5913454d96c29fd5029d99a5","58cb2dd0a5ea422db4ddd9be1302364e","089591a0d5c34741af4ba74d8ad1de5c","b91accc66f88455d983d2d52fe68d473","148a09ed4a6b4ef885d8fd5e9d7e5661","1e2e5afb7199473b91565e40f9e6749c","460dd4be240145589ca37139b4c73d0e","6dceee5ef9ed411c8cd47c52c343afd2","545aeb2e58e24c10be828e95271d1b65","55affbee4cec49fc91f81ec2c26c6009","2ba109aa14d64fa9aeef66a38757f711","a3a2c565c38540429e128d1d5a202377","0f055623ab3646b4975a5c33cf299281","9efc4a8191054c078df2d4930a6a87a0","3b94d6eeb17b4e1ba3bd136c637f82a4","4ce3535be2bf47a9bdae5b0ed210c0bc","18ad6d51a9914f54a561c8ae338f19d3","a872040239584a52b2ace190d8b1f825","16752b5a21ef495e90b9cef6421be1d5","832e1e813d9e492c94c4c68e15dc78da","eee26278b7494368af06fcd22d63a25b","3a2037eb1133467f818784559e71a1a9","5d6461f8aa104c2fa17d16a3f3bf15d2","1363ca85925549f5831d5e12b639fb59","28d80d4b8bed475c99da0b46afa87ac1","883898d8e05f46508523ebcc96d95b5d","68bd551c957d4415be29bb94051c6120","99226af7fc394158be80eba2be9f90f2","709ca7cac58249aeb1eac2bdaeb7dbcf","5899ff51790348f598cbf1c79c9920f1","4dcb2d57a9fa465a8a63f721b5ea4e74"]},"executionInfo":{"status":"ok","timestamp":1702298699560,"user_tz":-540,"elapsed":2861,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"06b736a7-41c9-49a5-a019-9a3704b8691d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"62a06a2ba5fd443184cb895e0d973ec6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"55affbee4cec49fc91f81ec2c26c6009"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eee26278b7494368af06fcd22d63a25b"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["100%|██████████| 400/400 [00:00<00:00, 2239.85it/s]"]},{"output_type":"stream","name":"stdout","text":["The character to token ratio of the dataset is: 0.77\n"]},{"output_type":"stream","name":"stderr","text":["\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"]}],"source":["train_dataset = create_datasets(tokenizer, script_args)"]},{"cell_type":"code","execution_count":22,"id":"000314e9-f10b-4685-8da6-0511494a9eb4","metadata":{"id":"000314e9-f10b-4685-8da6-0511494a9eb4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702298702486,"user_tz":-540,"elapsed":11,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"8cf69799-5964-43aa-ce94-e407fa69170f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["3200"]},"metadata":{},"execution_count":22}],"source":["len(train_dataset)"]},{"cell_type":"code","execution_count":23,"id":"4ba80a64-0ec7-4b29-ac95-7b3d34549f17","metadata":{"id":"4ba80a64-0ec7-4b29-ac95-7b3d34549f17","executionInfo":{"status":"ok","timestamp":1702298704659,"user_tz":-540,"elapsed":330,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"outputs":[],"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",")"]},{"cell_type":"code","source":["base_model"],"metadata":{"id":"gw9xbeUgbZEo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702298706997,"user_tz":-540,"elapsed":355,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"dc4b9e28-4f4b-45e4-db94-e60fe3d2d60c"},"id":"gw9xbeUgbZEo","execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LlamaForCausalLM(\n","  (model): LlamaModel(\n","    (embed_tokens): Embedding(32000, 4096)\n","    (layers): ModuleList(\n","      (0-31): 32 x LlamaDecoderLayer(\n","        (self_attn): LlamaAttention(\n","          (q_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.05, 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","          )\n","          (k_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.05, 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","          )\n","          (v_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.05, 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","          )\n","          (o_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.05, 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","          )\n","          (rotary_emb): LlamaRotaryEmbedding()\n","        )\n","        (mlp): LlamaMLP(\n","          (gate_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.05, 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=11008, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (up_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.05, 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=11008, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (down_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=11008, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.05, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=11008, 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","          )\n","          (act_fn): SiLUActivation()\n","        )\n","        (input_layernorm): LlamaRMSNorm()\n","        (post_attention_layernorm): LlamaRMSNorm()\n","      )\n","    )\n","    (norm): LlamaRMSNorm()\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",")"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","execution_count":25,"id":"edb204be-ec15-4800-af49-6cfbad2f7f9a","metadata":{"id":"edb204be-ec15-4800-af49-6cfbad2f7f9a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702298710346,"user_tz":-540,"elapsed":331,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"6a82f949-4733-4e8a-ffba-ba30ac7dcc9f"},"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 19988480 || all params: 3520401408 || trainable%: 0.5677897967708119\n"]}],"source":["print_trainable_parameters(base_model)"]},{"cell_type":"markdown","source":["구글 코랩 T-4 GPU: 1:37:34 예상시간\n","- 총 1,600 스텝 필요\n","- 하지만 이보다 일찍 종료됨 약 900번 미만 스텝에서 종료됨\n","\n","시퀀스 길이 512의 경우\n","- 14.4 G / 15.0 G 사용\n","- 메모리 오버플로우 발생시 512보다 줄일 것"],"metadata":{"id":"76sRe172fGlm"},"id":"76sRe172fGlm"},{"cell_type":"code","execution_count":27,"id":"14019fa9-0c6f-4729-ac99-0d407af375b8","metadata":{"id":"14019fa9-0c6f-4729-ac99-0d407af375b8","colab":{"base_uri":"https://localhost:8080/","height":638},"executionInfo":{"status":"ok","timestamp":1702305410110,"user_tz":-540,"elapsed":223334,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"0ea27f0f-e6b3-4f99-9ee9-ae98658466a4"},"outputs":[{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='794' max='1600' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [ 794/1600 50:05 < 50:58, 0.26 it/s, Epoch 0.50/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>100</td>\n","      <td>0.268500</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>0.274100</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>0.278600</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>0.270100</td>\n","    </tr>\n","    <tr>\n","      <td>500</td>\n","      <td>0.271700</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>0.268900</td>\n","    </tr>\n","    <tr>\n","      <td>700</td>\n","      <td>0.263000</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='852' max='1600' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [ 852/1600 53:50 < 47:23, 0.26 it/s, Epoch 0/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>100</td>\n","      <td>0.268500</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>0.274100</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>0.278600</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>0.270100</td>\n","    </tr>\n","    <tr>\n","      <td>500</td>\n","      <td>0.271700</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>0.268900</td>\n","    </tr>\n","    <tr>\n","      <td>700</td>\n","      <td>0.263000</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>0.262800</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=852, training_loss=0.26882281997394114, metrics={'train_runtime': 3234.9061, 'train_samples_per_second': 0.989, 'train_steps_per_second': 0.495, 'total_flos': 3.469201579494605e+16, 'train_loss': 0.26882281997394114, 'epoch': 0.53})"]},"metadata":{},"execution_count":27}],"source":["trainer.train()"]},{"cell_type":"code","source":["script_args.training_args.output_dir"],"metadata":{"id":"3Y4FQSyRghQt","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1702305413065,"user_tz":-540,"elapsed":574,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"aea190d1-e8b6-4cd0-c020-11b748fdfd8f"},"id":"3Y4FQSyRghQt","execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/gdrive/MyDrive/lora-llama-2-7b-food-order-understanding'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":28}]},{"cell_type":"code","execution_count":29,"id":"49f05450-da2a-4edd-9db2-63836a0ec73a","metadata":{"id":"49f05450-da2a-4edd-9db2-63836a0ec73a","executionInfo":{"status":"ok","timestamp":1702305461462,"user_tz":-540,"elapsed":476,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"outputs":[],"source":["trainer.save_model(script_args.training_args.output_dir)"]},{"cell_type":"markdown","id":"652f307e-e1d7-43ae-b083-dba2d94c2296","metadata":{"id":"652f307e-e1d7-43ae-b083-dba2d94c2296"},"source":["# 추론 테스트"]},{"cell_type":"code","execution_count":30,"id":"ea8a1fea-7499-4386-9dea-0509110f61af","metadata":{"id":"ea8a1fea-7499-4386-9dea-0509110f61af","executionInfo":{"status":"ok","timestamp":1702305464590,"user_tz":-540,"elapsed":1618,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"outputs":[],"source":["from transformers import pipeline, TextStreamer"]},{"cell_type":"code","execution_count":31,"id":"52626888-1f6e-46b6-a8dd-836622149ff5","metadata":{"id":"52626888-1f6e-46b6-a8dd-836622149ff5","executionInfo":{"status":"ok","timestamp":1702305466602,"user_tz":-540,"elapsed":460,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"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",")"]},{"cell_type":"code","execution_count":32,"id":"46e844fa-8f63-4359-a4fb-df66e8171796","metadata":{"id":"46e844fa-8f63-4359-a4fb-df66e8171796","executionInfo":{"status":"ok","timestamp":1702305469440,"user_tz":-540,"elapsed":316,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"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","]"]},{"cell_type":"code","execution_count":33,"id":"1919cf1f-482e-4185-9d06-e3cea1918416","metadata":{"id":"1919cf1f-482e-4185-9d06-e3cea1918416","executionInfo":{"status":"ok","timestamp":1702305485661,"user_tz":-540,"elapsed":364,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"outputs":[],"source":["def wrapper_generate(model, input_prompt):\n","    data = tokenizer(input_prompt, return_tensors=\"pt\")\n","    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n","    input_ids = data.input_ids[..., :-1]\n","    with torch.no_grad():\n","        pred = model.generate(\n","            input_ids=input_ids.cuda(),\n","            streamer=streamer,\n","            use_cache=True,\n","            max_new_tokens=float('inf'),\n","            temperature=0.5\n","        )\n","    decoded_text = tokenizer.batch_decode(pred, skip_special_tokens=True)\n","    return (decoded_text[0][len(input_prompt):])"]},{"cell_type":"code","execution_count":34,"id":"eaac1f6f-c823-4488-8edb-2f931ddf0daa","metadata":{"id":"eaac1f6f-c823-4488-8edb-2f931ddf0daa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702305643417,"user_tz":-540,"elapsed":156747,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"c575755a-0edd-42f5-a282-6548dc600566"},"outputs":[{"output_type":"stream","name":"stdout","text":[";- 분석 결과 0: 음식명:삼선짬뽕,옵션:곱배기,수량:하나\n","- 분석 결과 1: 음식명:사천 탕수육,옵션:중짜,수량:한그릇\n",";- 분석 결과 0: 음식명:아이스아메리카노,옵션:톨,수량:한잔\n","- 분석 결과 1: 음식명:딸기스무디,수량:한잔\n","- 분석 결과 2: 음식명:콜드브루라떼,수량:하나\n",";- 분석 결과 0: 음식명:참이슬, 수량:한병\n","- 분석 결과 1: 음식명:코카콜라, 옵션:1.5리터, 수량:한병\n","- 분석 결과 2: 음식명:테슬라, 수량:한병\n",";- 분석 결과 0: 음식명:꼬막무침, 수량:1인분\n","- 분석 결과 1: 음식명:닭도리탕, 옵션:중자\n","- 분석 결과 2: 음식명:소주, 수량:한병\n",";- 분석 결과 0: 음식명:김치찌개,수량:3인분\n","- 분석 결과 1: 음식명:계란말이\n",";- 분석 결과 0: 음식명:불고기버거세트, 수량:1개\n","- 분석 결과 1: 음식명:감자튀김\n",";- 분석 결과 0: 음식명:불닭볶음면, 수량:1개\n","- 분석 결과 1: 음식명:사리곰탕면, 수량:2개\n",";- 분석 결과 0: 음식명:카페라떼,옵션:아이스,샷추가,수량:한잔\n","- 분석 결과 1: 음식명:스콘,수량:하나\n",";- 분석 결과 0: 음식명:춘천닭갈비, 수량:4인분\n","- 분석 결과 1: 음식명:라면사리\n","- 분석 결과 2: 음식명:콜라, 옵션:300ml, 수량:두캔\n",";- 분석 결과 0: 음식명:조랭이떡국,수량:3인분\n","- 분석 결과 1: 음식명:떡만두,수량:한세트\n",";- 분석 결과 0: 음식명:깐풍탕수, 수량:2인분\n","- 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)}"]},{"cell_type":"code","execution_count":35,"id":"fefd04ba-2ed8-4f84-bdd0-86d52b3f39f6","metadata":{"id":"fefd04ba-2ed8-4f84-bdd0-86d52b3f39f6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702305654864,"user_tz":-540,"elapsed":637,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"27f78464-41e0-4990-8343-68f13c0e8592"},"outputs":[{"output_type":"stream","name":"stdout","text":["- 분석 결과 0: 음식명:삼선짬뽕,옵션:곱배기,수량:하나\n","- 분석 결과 1: 음식명:사천 탕수육,옵션:중짜,수량:한그릇\n"]}],"source":["print(eval_dic[0])"]},{"cell_type":"markdown","id":"3f471e3a-723b-4df5-aa72-46f571f6bab6","metadata":{"id":"3f471e3a-723b-4df5-aa72-46f571f6bab6"},"source":["# 미세튜닝된 모델 로딩 후 테스트"]},{"cell_type":"code","execution_count":36,"id":"a43bdd07-7555-42b2-9888-a614afec892f","metadata":{"id":"a43bdd07-7555-42b2-9888-a614afec892f","executionInfo":{"status":"ok","timestamp":1702305663863,"user_tz":-540,"elapsed":348,"user":{"displayName":"조수연","userId":"02851953064562485279"}}},"outputs":[],"source":["bnb_config = BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch.bfloat16,\n",")"]},{"cell_type":"code","execution_count":37,"id":"39db2ee4-23c8-471f-89b2-bca34964bf81","metadata":{"id":"39db2ee4-23c8-471f-89b2-bca34964bf81","colab":{"base_uri":"https://localhost:8080/","height":531},"executionInfo":{"status":"error","timestamp":1702305668066,"user_tz":-540,"elapsed":1963,"user":{"displayName":"조수연","userId":"02851953064562485279"}},"outputId":"097a1b44-da71-4f28-d2f8-03a15d0e3aae"},"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-37-386f261ee854>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m trained_model = AutoPeftModelForCausalLM.from_pretrained(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mscript_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_dir\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[0mquantization_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbnb_config\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[0mdevice_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"auto\"\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[0mcache_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscript_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache_dir\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/auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, adapter_name, is_trainable, config, **kwargs)\u001b[0m\n\u001b[1;32m     99\u001b[0m             )\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0mbase_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget_class\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_model_path\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    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m         return cls._target_peft_class.from_pretrained(\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m    564\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\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[1;32m    565\u001b[0m             \u001b[0mmodel_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_model_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m             return model_class.from_pretrained(\n\u001b[0m\u001b[1;32m    567\u001b[0m                 \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmodel_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhub_kwargs\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[0m\n\u001b[1;32m    568\u001b[0m             )\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   3418\u001b[0m                 }\n\u001b[1;32m   3419\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdevice_map_without_lm_head\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"disk\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdevice_map_without_lm_head\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\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-> 3420\u001b[0;31m                     raise ValueError(\n\u001b[0m\u001b[1;32m   3421\u001b[0m                         \"\"\"\n\u001b[1;32m   3422\u001b[0m                         \u001b[0mSome\u001b[0m \u001b[0mmodules\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdispatched\u001b[0m \u001b[0mon\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mCPU\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdisk\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mMake\u001b[0m \u001b[0msure\u001b[0m \u001b[0myou\u001b[0m \u001b[0mhave\u001b[0m \u001b[0menough\u001b[0m \u001b[0mGPU\u001b[0m \u001b[0mRAM\u001b[0m \u001b[0mto\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: \n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom\n                        `device_map` to `from_pretrained`. Check\n                        https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu\n                        for more details.\n                        "]}],"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",")"]},{"cell_type":"code","execution_count":null,"id":"b0b75ca4-730d-4bde-88bb-a86462a76d52","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"]},{"cell_type":"markdown","source":["추론 과정에서는 GPU 메모리를 약 5.5 GB 활용"],"metadata":{"id":"X1tRCa4EiYXp"},"id":"X1tRCa4EiYXp"},{"cell_type":"code","execution_count":null,"id":"e374555b-9f8a-4617-8ea7-c1e6ee1b2999","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)}"]},{"cell_type":"code","execution_count":null,"id":"5d055bb0-5e5f-4221-a634-45d903c0f3b5","metadata":{"id":"5d055bb0-5e5f-4221-a634-45d903c0f3b5"},"outputs":[],"source":["print(eval_dic[0])"]},{"cell_type":"code","source":[],"metadata":{"id":"Z6tBUSgQiU1i"},"id":"Z6tBUSgQiU1i","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 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