diff --git "a/hw_llama_2_7B_food_order_understanding.ipynb" "b/hw_llama_2_7B_food_order_understanding.ipynb" new file mode 100644--- /dev/null +++ "b/hw_llama_2_7B_food_order_understanding.ipynb" @@ -0,0 +1,8114 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "28e4c4d1-a73f-437b-a1bd-c2cc3874924a" + }, + "source": [ + "# 강의 11주차: llama2-food-order-understanding\n", + "\n", + "1. llama-2-7b-chat-hf 를 주문 문장 이해에 미세 튜닝\n", + "\n", + "- food-order-understanding-small-3200.json (학습)\n", + "- food-order-understanding-small-800.json (검증)\n", + "\n", + "\n", + "종속적인 필요 내용\n", + "- huggingface 계정 설정 및 llama-2 사용 승인\n", + "- 로깅을 위한 wandb" + ], + "id": "28e4c4d1-a73f-437b-a1bd-c2cc3874924a" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nDZe_wqKU6J3", + "outputId": "39634a40-9521-42e0-d048-e3f78b7a8306" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + 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accelerate-0.25.0 bitsandbytes-0.41.3.post1 coloredlogs-15.0.1 datasets-2.15.0 dill-0.3.7 docker-pycreds-0.4.0 docstring-parser-0.15 gitdb-4.0.11 humanfriendly-10.0 multiprocess-0.70.15 optimum-1.15.0 peft-0.7.0 pyarrow-hotfix-0.6 sentencepiece-0.1.99 sentry-sdk-1.38.0 setproctitle-1.3.3 shtab-1.6.5 smmap-5.0.1 trl-0.7.4 tyro-0.6.0 wandb-0.16.1\n" + ] + } + ], + "source": [ + "pip install transformers peft accelerate optimum bitsandbytes trl wandb" + ], + "id": "nDZe_wqKU6J3" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "51eb00d7-2928-41ad-9ae9-7f0da7d64d6d", + "outputId": "e8550fbb-e4fc-4b70-fb40-1babe806f850" + }, + "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": [ + "99532ff414024109b47d52f85d12756c", + "c9c40f2edc544032bd50d8020f8aa5b4", + "c2bdbf8108174891a74270ba291a3519", + "adac7366f35c47bd8f608d8a3986cbe7", + "a0cc6856d4e947cb90001e1679abe061", + "e4c56fa7facb42cdb400172737f5b66b", + "939dae7eac1143c0851249ab6b239eaa", + "a9c5cc0353944b2eb8649f08c51ddd26", + "7b8e04ff45a34ad38e50c3f71acf31b8", + "31bcd6aba0be45fd8a5d69a1cee4d1e2", + "c2ed791223104173b44419665b6b611e", + "ae10fc8777724a5781ad4665f868cb87", + "b51df9eb8d70473b9eb828f988bc9430", + "34d6b8f3945c46e7830f54f5ea30a2ae", + "c558553d832347d2aef2de23bdea73ac", + "3843b559ef054100bc6e4d0666ba70fe", + "6d34bb28660d40b3b58a454d85e9684b", + "9b2fff53449641a88210178ced5ffa3f", + "1261f868f75342e394503bbd9ca8305d", + "bf4a5a4f9a294a679f6725d72520d0f6", + "e8983362fd0046a99608eed2d0f42f92", + "9f0d306f3bdc4cc9a4cee4dc8f10ee5b", + "f971e68bef0440918129e8ed6d882bb7", + "ce73f650db034858890e20452f74ddaa", + "ca4c88b41aea474ca29fe7b25774dae6", + "798c89074789407e86f87578a3ec0cc7", + "abf493394d644ed18b57066d214b13d0", + "4a52e47448584335b9d143b0a96b9095", + "459bd9737cbb43e898d7fa9e2f341417", + "c5fa2300fc664eb0a36526b87ff53055", + "2604feda7a204e6e8e17dd0ccd8649b2", + "db4161ded47c4c09ab50c0a431a2831a" + ] + }, + "id": "tX7gYxZaVhYL", + "outputId": "ff182a8d-bd90-47cc-d957-2ecde7eeb44a" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
,\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_11-07-56_d524b47e3779,\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/nlp/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=,\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=, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type='CAUSAL_LM', inference_mode=False, r=8, target_modules={'v_proj', 'o_proj', 'down_proj', 'up_proj', 'q_proj', 'k_proj', 'gate_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": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 332, + "referenced_widgets": [ + "ecd3c088e75e4dedbebc03eb86a2a838", + "9a890a666bea4fdca9ddd62e4085bc4f", + "ecde480028a74609bbde8710b2d567f4", + "499bba7f829747eaa92eafda4cf77a61", + "155b8023c57343b8b1fde160964b2063", + "f71d6db8c12a4bdab6a3e9ac1e612fc5", + "20a33e5049f1486d98077632ae2d7948", + "c41ae5f296cc46cb9b1463d4543575d9", + "e8ab8894caea47f7a54ab077e5c215b7", + "f3072765ae394f84a4b93925456bc001", + "2e8fea382bea4cea895e5c97fa004e90", + "bbb175022c5241cb8d6adf58aee9bead", + "4a612ead590a4079975d89aae0cc42c9", + "4d5d9f96a291417787bfa668ad5705a2", + "7705b94878bc4d8f93c54bfe0e7b4cf3", + "a7a9f8a940994efbbc7a4230b6cadaf1", + "8926e697f2df40e0a59b956619b52847", + "1fa87975cda24bbc8ba232c8b0bbd771", + "602341ef1c014e9a8363ad609a6258b1", + 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Please use `token` instead.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/614 [00:00, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type='CAUSAL_LM', inference_mode=False, r=8, target_modules={'v_proj', 'o_proj', 'down_proj', 'up_proj', 'q_proj', 'k_proj', 'gate_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" + ], + "id": "4420fcc4-2bac-413d-b7aa-89455c512419" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "7d2592bc9c964f4b85c92dc5822dcfbc", + 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" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=852, training_loss=0.3792879995605755, metrics={'train_runtime': 3082.7229, 'train_samples_per_second': 1.038, 'train_steps_per_second': 0.519, 'total_flos': 3.469201579494605e+16, 'train_loss': 0.3792879995605755, 'epoch': 0.53})" + ] + }, + "metadata": {}, + "execution_count": 26 + } + ], + "source": [ + "trainer.train()" + ], + "id": "14019fa9-0c6f-4729-ac99-0d407af375b8" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3Y4FQSyRghQt", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "outputId": "09ff0896-0972-4f07-8a8c-89786f3c7149" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'/gdrive/MyDrive/nlp/lora-llama-2-7b-food-order-understanding'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 27 + } + ], + "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, 계���말이 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):\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):])" + ], + "id": "1919cf1f-482e-4185-9d06-e3cea1918416" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eaac1f6f-c823-4488-8edb-2f931ddf0daa", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "13193a97-1b7b-4aed-90a1-b6c0e5a0a15b" + }, + "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)}" + ], + "id": "eaac1f6f-c823-4488-8edb-2f931ddf0daa" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fefd04ba-2ed8-4f84-bdd0-86d52b3f39f6", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e65f131a-0e86-46be-8671-d88f782b0e04" + }, + "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])" + ], + "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", + "source": [], + "metadata": { + "id": "KpCEf3DK0K6j" + }, + "id": "KpCEf3DK0K6j", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "b0b75ca4-730d-4bde-88bb-a86462a76d52", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 251 + }, + "outputId": "bfe7f03d-b1c2-4950-cad6-db301ed9fa48" + }, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m tokenizer = AutoTokenizer.from_pretrained(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mscript_args\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_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[0mtrust_remote_code\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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[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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m )\n", + "\u001b[0;31mNameError\u001b[0m: name 'AutoTokenizer' is not defined" + ] + } + ], + "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": "TPU", + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "99532ff414024109b47d52f85d12756c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + 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Copy a token from your Hugging Face\ntokens page and paste it below.
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