{ "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", "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 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(from pandas->datasets->optimum) (2023.3.post1)\n", "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro>=0.5.11->trl) (0.1.2)\n", "Installing collected packages: sentencepiece, bitsandbytes, smmap, shtab, setproctitle, sentry-sdk, pyarrow-hotfix, humanfriendly, docstring-parser, docker-pycreds, dill, multiprocess, gitdb, coloredlogs, tyro, GitPython, accelerate, wandb, datasets, trl, peft, optimum\n", "Successfully installed GitPython-3.1.40 accelerate-0.25.0 bitsandbytes-0.41.3.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", "dc3e988524274ec3876272448d215269", "c863ccf65f6946c0a7893e0afecd8e1c", "c416fd0b2f1c49c6b4bb7f14ae71a2ae", "b320011ce8fb46838f48401a37825e7e", "50a109908eb54a638a7e98c821e3f0dd", "c897a8564d654df39a78741c4e3f38f4", "eb9874c9729447869f8dbc96d445b3f4", "842af04a2e434a2eb14de701549385ee", "09adc4539f3141dab67c4a2d0453fd4c", "7c2607765f55496f93f0c2771f4e5854", "6c1a9c249ef84bba95ed52504b10398c", "844518be442c418598f0306653495aff", "90a8e2ade9f04360b57b73d88ea01518", "13e3a9db5e80476ca342a4d6ad44abc2", "008aff88a07d460e95750278aa88613e", "9b0466b5f9344e2088b1345364a2f9b1", "4660734c07254aba83d342cb0f9b96d1", "e3b6fcea8adf423587d48fe02accef90", "dd63d454ce74497f926702fa8af4dcbf", "9ae4982964f24bef8637fdb8c8484ca4", "02bac9beb8e34110abeed7ed0ad7f938", "9081091ebb84446c80291369b1efb2c8", "a1e8498dc1f744799bee7c650c644107", "cf6d4aa5aad84afd9408e1ccf2468ef9", "f6aa882dbf7141bda0bbf37ff2f4b8c5", "465e35c8d439401abbf8f345a931cfdb", "7ae18a002ccc473297d4a24d64354a20", "c6443d8cc09544db8d592201530dacb3", "e09b2886142e47b78f90b4dd3758319b", "92b163bf2f1141b78cc37ea842224687", "d0c5b412a5314eef89c09e13ba388f50", "32e5ea026d714e1e8c06b46d3fedd2a3", "3b130eab08084044954383beda225fc9", "71d13daba527497a8897bb02fb30c2b4", "8b7c4705347a43f9903ac7989bc4d581", "562b6df84964427f800e8d5c168cf207", "d859f05898864626af289f2c55021801", "ef42ce54b7484a12911e402b5ad1c92a", "d0797b6e395d410f991e6fab8689afd1", "44baec5300554a1b8e29498bade582f1", "bd5a4cf07b774ec7b3640eb8b12e55a3", "615f324aecfd406f9449daf8dfb8fa85", "6d144369323d44d2a808b27c1251321d", "d548e136c3864b43bb8976626f97ff2e", "565e55884974463ba0060abd7ac4b9cc", "e7eef61d6f11453e83075b4aab2d2a5e", "5d1f11aad25d406997f788907b165b19", "2ff7c0f7be7d4bf09192437967da8af4", "f8ab546e3f0b4bf89e9c0ff50e0b1410", "bcf86e249ed047aa8926a6121aa3ac2b", "adc3ab4afa2742899960730e8338d8aa", "518ba963374e405e98826ae7650c3935", "0a55487942de4647ab077f7c41397c80", "97ab3af6501e43e8975a64a2371b9091", "0085dc8918aa440cb83668517a04f6be", "0fbf9d484e0c4b48aa4ac69e3c9fa59d", "d18c2e65f8de4943ab0141ad447990cd", "e7686f970c3e4a849b3146a15a85782f" ] }, "id": "15c8425e-bb0b-40c5-bfe8-385bac699b9d", "outputId": "8fa22b6b-7ff5-40a0-918f-97e5ac10e874" }, "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, 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", "0763e0a6e0b34659908701403a640d8a", "882a1e561b5c406f8812875fc276495d", "1e207dea169842e8931c79e33c691730", "eba7df9472474ab792b930aecac998a6", "28f8eedc6df24007ac47790a9aee8a44", "06e14f872410472b8ef96869784dc3d6", "c27dcf54b7a748deac9905f9aeeba998", "ab30a6d39b6045c39d870d59eb4b8a21", "abd769d5568541d596b95ec02e6a19ef", "2c355ab0aecf41fea574747fee9411dd", "cdfcd017bcae4b239748500d2c1bbca6", "7cd3f007297a4d778038207eb0fc4584", "19ee87c603fd49b2b1b37a924a099bdb", "86d73e4bc4164eeba2c8d808712baeaa", "c731a9d74d43435382f1ec0cb4d2ffa3", "2a3a417e2a4e4678b618100804786191", "d22bff949df94d58a165634ce7683cae", "6b2c9696365e4844ae43004e86f93420", "8cf1ecb9349f4801a255eb72bf0ad5fb", "401275f281f546e08f910530c939dec9", "2dedd1afdbe44c67ae0740010c374587", "7344151a3bcf4672902ef6660bfa012b", "46a876ab492b4bc5b89143ddebecac0f", "f2e7258439fa4421abaffe270e0be063", "26bf150605394f84a18d3d217bd6eb1b", "346230e7639744b0af64aa0cbe0b2d9a", "d0f4d89375304315a67b06e9401fcc04", "88de586a5c434bc686ce3a8f472c238c", "981128655e2243b7ab0b35e2177d71ba", "47b1b935091a48c9b2d3d8b99a668e77", "722a48224fea4163af146ec6f18e4128", "6a05454acebc4c18a01c251950ce469f", "817885678829478db0ec3e4912455d44", "ddd9264757804a6fa1e7a92aa7f18ee0", "e38662b831744bed85ac686a11e3aa18", "0e115a72a2094266835b32881bb4e522", "ae608c1910d94f20b7432eed18a0c6d8", "17e8ebd429f442918e104c9c941c1ad2", "6fe771944abf48ab84373da4ec2dfa10", "3a21b6707b894bb9939849a028e09069", "f4dc66976a2f4a168b3067a2981d5eb6", "1b0d93c348b545ffbbad71bcad841b9f", "228e97d23558488e8ccadebde9ea930e" ] }, "id": "f47f9584-3988-46b8-a062-29dcde75a0e2", "outputId": "6dcf9835-6616-4504-9cbb-865c97ef0c0e" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/1.62k [00:00" ], "application/javascript": [ "\n", " window._wandbApiKey = new Promise((resolve, reject) => {\n", " function loadScript(url) {\n", " return new Promise(function(resolve, reject) {\n", " let newScript = document.createElement(\"script\");\n", " newScript.onerror = reject;\n", " newScript.onload = resolve;\n", " document.body.appendChild(newScript);\n", " newScript.src = url;\n", " });\n", " }\n", " loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n", " const iframe = document.createElement('iframe')\n", " iframe.style.cssText = \"width:0;height:0;border:none\"\n", " document.body.appendChild(iframe)\n", " const handshake = new Postmate({\n", " container: iframe,\n", " url: 'https://wandb.ai/authorize'\n", " });\n", " const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n", " handshake.then(function(child) {\n", " child.on('authorize', data => {\n", " clearTimeout(timeout)\n", " resolve(data)\n", " });\n", " });\n", " })\n", " });\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.011112337766667224, max=1.0…" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "a07e2c6b91b74e119b3324c021e9cad0" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Tracking run with wandb version 0.16.1" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Run data is saved locally in /content/wandb/run-20231211_111154-7fn0vhfg" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Syncing run llama-2-7b-food-order-understanding to Weights & Biases (docs)
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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, 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):\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": 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Copy a token from your Hugging Face\ntokens page and paste it below.
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