vi/2_preference_alignment/notebooks/orpo_finetuning

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tối Ưu Hóa Ưu Tiên Theo Tỷ Lệ Odds (Odds Ratio Preference Optimization - ORPO)\n", "\n", "Bài tập này sẽ hướng dẫn bạn quy trình tinh chỉnh mô hình ngôn ngữ sử dụng phương pháp ORPO. Chúng ta sẽ sử dụng mô hình `HuggingFaceTB/SmolLM2-135M` bản gốc (chưa được huấn luyện SFT), nên nó không tương thích với DPO. Điều này có nghĩa là bạn không thể sử dụng mô hình bạn đã huấn luyện trong [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n", "\n", "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n", " <h2 style='margin: 0;color:blue'>Bài tập: Tinh chỉnh SmolLM2 với DPOTrainer</h2>\n", " <p>Lấy một tập bộ liệu từ Hugging Face hub và điều chỉnh mô hình trên bộ dữ liệu đó. </p> \n", " <p><b>Các bài tập</b></p>\n", " <p>🐢 Sử dụng bộ dữ liệu `trl-lib/ultrafeedback_binarized`</p>\n", " <p>🐕 Thử nghiệm với bộ dữ liệu `argilla/ultrafeedback-binarized-preferences`</p>\n", " <p>🦁 Thử nghiệm với một phần của bộ dữ liệu `orpo-dpo-mix-40k` của mlabonne</p>\n", "</div>\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EwNxh0pCQhfI", "outputId": "e4c288e1-2fa3-4717-d9dd-ae3ec0b91e3b" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ben/code/smol-course/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "# Các thư viện cần thiết\n", "\n", "import torch\n", "import os\n", "from datasets import load_dataset\n", "from transformers import (\n", " AutoModelForCausalLM,\n", " AutoTokenizer,\n", ")\n", "from trl import ORPOConfig, ORPOTrainer, setup_chat_format\n", "\n", "# Authenticate to Hugging Face\n", "from huggingface_hub import login\n", "\n", "login()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chuẩn bị dữ liệu" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Generating train split: 100%|██████████| 62135/62135 [00:00<00:00, 450465.27 examples/s]\n", "Generating test split: 100%|██████████| 1000/1000 [00:00<00:00, 382866.64 examples/s]\n" ] } ], "source": [ "# Tải tập dữ liệu\n", "\n", "# TODO: 🦁🐕 thay đổi tập dữ liệu theo lựa chọn của bạn\n", "dataset = load_dataset(path=\"trl-lib/ultrafeedback_binarized\", split=\"train\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# TODO: 🐕 Nếu bộ dữ liệu của bạn không được biểu diễn dưới dạng danh sách hội thoại,\n", "# bạn có thể sử dụng hàm `process_dataset` để chuyển đổi nó." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 419, "referenced_widgets": [ "d6f783223244417195a9ef506f1562d0", "252cc9541f2e47daa0c93d3fb24fa619", "766a1a9240ac4d0b8d050809ccef63a8", "b90475ba4e984255a649ce30f159cda9", "728381a9d628447ba292424c3fd70c21", "0e6347d4944640f38a033857824867da", "4bf457a1b3a94a489a8640e022c5cf77", "b607bd7776264f9eb156d8e051034a13", "1e31ebe04bb6435dbbecd1f6f8b753c2", "f2266c7feb0746269403cea6dd79c10c", "3437dd301c8d42f7b70fde092b9615e1", "43000069460d4b68a69f5b66741a44e8", "f568c9c850464ef29359f6bd01acd986", "4bdb9f13e04c497e9f710cf925854d3a", 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tokenizer.eos_token\n", "\n", "# Đặt tên cho bản tinh chỉnh để lưu &/ tải lên\n", "finetune_name = \"SmolLM2-FT-ORPO\"\n", "finetune_tags = [\"smol-course\", \"module_2\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Huấn luyện mô hình với ORPO" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 570, "referenced_widgets": [ "f5b1be102e5d4bb0827ecddd171d760a", "2a2c46b05e644d5788c807a40a47958a", "a490ba1931bc47ff9ba471018d446341", "dfba698e55a9413c8b50dafcdc0a31ca", "a6772529bef1479a961666b910711096", "457ee0e53cf74996938ddc639499a863", "63cd15f55e864ea9b0b48486e15fab81", "61e7c36dc3494c24be7f34de597d203d", "e887b04955404bcaa98b75ae79690bc8", "787a968dc6a94d79ab285bbf8a81b385", "86851cd4780546c6961a0acf4065d911", "0e5fbf1673694cbda1970b15ee49b187", "dc79ca94e196475aa0ff4f180e6375db", "ddfe7f0aa41e48f89478c7179def7573", "6aa9770c9467456fb6671db5e9d26fde", "eaddc613c0fe4aec9b1af2428de88027", "771183add0dc4162a7de5c7884b8c62e", "9e648eff1e98457dbd930b5048e85a4f", "d7ed9a48994945fcbb2576c0cb5db065", "dbd16f06b2c7402fbc6283ae97c16c00", "75f52d3302224a588017c42b0f16fb44", "eb89ca875d3045ba9e897c274e44fe9b" ] }, "id": "AWDwJe7_Qqgb", "outputId": "2a5d8940-c72b-4e45-ac89-24164f45e0e5" }, "outputs": [], "source": [ "orpo_args = ORPOConfig(\n", " # Learning rate nhỏ để tránh catastrophic forgetting\n", " # Đọc thêm về catastrophic forgetting: https://blog.vinbigdata.org/robot-co-the-hoc-suot-doi-khong/\n", " learning_rate=8e-6,\n", " # Giảm learning rate tuyến tính trong quá trình huấn luyện\n", " lr_scheduler_type=\"linear\",\n", " # Độ dài tối đa kết hợp của chỉ thị + câu trả lời\n", " max_length=1024,\n", " # Độ dài tối đa cho input prompts\n", " max_prompt_length=512,\n", " # Kiểm soát trọng số của hàm mất mát odds ratio (λ trong paper)\n", " beta=0.1,\n", " # Kích thước batch cho huấn luyện\n", " per_device_train_batch_size=2,\n", " per_device_eval_batch_size=2,\n", " # Giúp ổn định quá trình huấn luyện bằng cách tích luỹ gradient trước khi cập nhật\n", " gradient_accumulation_steps=4,\n", " # Optimizer tiết kiệm bộ nhớ cho CUDA, sử dụng adamw_torch cho CPU/MPS\n", " optim=\"paged_adamw_8bit\" if device == \"cuda\" else \"adamw_torch\",\n", " # Số epoch huấn luyện\n", " num_train_epochs=1,\n", " # Khi nào chạy đánh giá\n", " evaluation_strategy=\"steps\",\n", " # Đánh giá sau mỗi 20% huấn luyện\n", " eval_steps=0.2,\n", " # Ghi log metrics mỗi step\n", " logging_steps=1,\n", " # Tăng learning rate dần dần\n", " warmup_steps=10,\n", " # Tắt logging bên ngoài\n", " report_to=\"none\",\n", " # Nơi lưu model/checkpoints\n", " output_dir=\"./results/\",\n", " # Bật MPS (Metal Performance Shaders) nếu có\n", " use_mps_device=device == \"mps\",\n", " hub_model_id=finetune_name,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "trainer = ORPOTrainer(\n", " model=model,\n", " args=orpo_args,\n", " train_dataset=dataset[\"train\"],\n", " eval_dataset=dataset[\"test\"],\n", " processing_class=tokenizer,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "trainer.train() # Huấn luyện mô hình\n", "\n", "# Lưu mô hình\n", "trainer.save_model(f\"./{finetune_name}\")\n", "\n", "# Lưu lên huggingface hub nếu đã đăng nhập (HF_TOKEN đã được thiết lập)\n", "if os.getenv(\"HF_TOKEN\"):\n", " trainer.push_to_hub(tags=finetune_tags)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 💐 Bạn đã hoàn thành!\n", "\n", "Bài tập này đã hướng dẫn bạn từng bước cách tinh chỉnh mô hình `HuggingFaceTB/SmolLM2-135M` sử dụng `ORPOTrainer`. Theo các bước này, bạn có thể điều chỉnh mô hình để thực hiện các tác vụ cụ thể một cách hiệu quả hơn. Nếu bạn muốn tiếp tục học khóa học này, đây là một số bước bạn có thể thử:\n", "\n", "- Thử bài tập này ở các mức độ khó hơn\n", "- Đánh giá PR của bạn học khác\n", "- Cải thiện khóa học thông qua Issue hoặc PR" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "L4", "machine_shape": "hm", "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": "3.11.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "02ebf8115af9421ba7c69c551f36a466": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": 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vi/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb (8,635 lines of code) (raw):