From bf6de637f1a552cdc06758619d624c3afed74118 Mon Sep 17 00:00:00 2001 From: hahuyhoang411 Date: Fri, 20 Dec 2024 22:14:47 +0100 Subject: [PATCH 1/7] init --- vi/3_parameter_efficient_finetuning/README.md | 38 + .../images/lora_adapter.png | Bin 0 -> 1239572 bytes .../images/prompt_tuning.png | Bin 0 -> 85192 bytes .../lora_adapters.md | 124 + .../notebooks/finetune_sft_peft.ipynb | 510 ++ .../notebooks/load_lora_adapter.ipynb | 5059 +++++++++++++++++ .../prompt_tuning.md | 76 + vi/README.md | 4 +- 8 files changed, 5809 insertions(+), 2 deletions(-) create mode 100644 vi/3_parameter_efficient_finetuning/README.md create mode 100644 vi/3_parameter_efficient_finetuning/images/lora_adapter.png create mode 100644 vi/3_parameter_efficient_finetuning/images/prompt_tuning.png create mode 100644 vi/3_parameter_efficient_finetuning/lora_adapters.md create mode 100644 vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb create mode 100644 vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb create mode 100644 vi/3_parameter_efficient_finetuning/prompt_tuning.md diff --git a/vi/3_parameter_efficient_finetuning/README.md b/vi/3_parameter_efficient_finetuning/README.md new file mode 100644 index 00000000..207aa18d --- /dev/null +++ b/vi/3_parameter_efficient_finetuning/README.md @@ -0,0 +1,38 @@ +# Tinh chỉnh hiệu quả tham số (Parameter-Efficient Fine-Tuning - PEFT) + +Khi các mô hình ngôn ngữ ngày càng lớn hơn, việc tinh chỉnh truyền thống trở nên ngày càng thách thức. Việc tinh chỉnh đầy đủ một mô hình với 1.7B tham số đòi hỏi bộ nhớ GPU lớn, việc lưu trữ các bản sao mô hình riêng biệt tốn kém, và có nguy cơ làm mất đi các khả năng ban đầu của mô hình. Các phương pháp tinh chỉnh hiệu quả tham số (PEFT) giải quyết những thách thức này bằng cách chỉ điều chỉnh một tập nhỏ các tham số mô hình trong khi giữ nguyên phần lớn mô hình. + +Tinh chỉnh truyền thống cập nhật tất cả các tham số mô hình trong quá trình huấn luyện, điều này trở nên không khả thi với các mô hình lớn. Các phương pháp PEFT giới thiệu cách tiếp cận để điều chỉnh mô hình sử dụng ít tham số có thể huấn luyện hơn - thường ít hơn 1% kích thước mô hình gốc. Việc giảm đáng kể số lượng tham số có thể huấn luyện cho phép: + +- Tinh chỉnh trên phần cứng tiêu dùng với bộ nhớ GPU hạn chế +- Lưu trữ nhiều phiên bản điều chỉnh (adapters) cho từng tác vụ một cách hiệu quả +- Khả năng tổng quát hóa tốt hơn trong các trường hợp dữ liệu ít +- Chu kỳ huấn luyện và thử nghiệm nhanh hơn + +## Các phương pháp hiện có + +Trong chương này, chúng ta sẽ tìm hiểu hai phương pháp PEFT phổ biến: + +### 1️⃣ Phương Pháp LoRA (Low-Rank Adaptation) + +LoRA đã nổi lên như phương pháp PEFT được áp dụng rộng rãi nhất, cung cấp giải pháp hoàn hảo cho việc điều chỉnh mô hình hiệu quả mà không tốn nhiều tài nguyên tính toán. Thay vì sửa đổi toàn bộ mô hình, **LoRA đưa các ma trận có thể huấn luyện vào các lớp attention của mô hình.** Cách tiếp cận này thường giảm các tham số có thể huấn luyện khoảng 90% trong khi vẫn duy trì hiệu suất tương đương với tinh chỉnh đầy đủ. Chúng ta sẽ khám phá LoRA trong phần [LoRA (Low-Rank Adaptation)](./lora_adapters.md). + +### 2️⃣ Phương Pháp Điều Chỉnh Chỉ Thị (Prompt Tuning) + +Prompt tuning cung cấp cách tiếp cận **thậm chí nhẹ hơn** bằng cách **thêm các token có thể huấn luyện vào đầu vào** thay vì sửa đổi trọng số mô hình. Prompt tuning ít phổ biến hơn LoRA, nhưng có thể là kỹ thuật hữu ích để nhanh chóng điều chỉnh mô hình cho các tác vụ hoặc lĩnh vực mới. Chúng ta sẽ khám phá prompt tuning trong phần [Prompt Tuning](./prompt_tuning.md). + +## Notebooks bài tập + +| Tiêu đề | Mô tả | Bài tập | Link | Colab | +|---------|--------|---------|------|-------| +| Tinh chỉnh LoRA | Học cách tinh chỉnh mô hình sử dụng LoRA adapters | 🐢 Huấn luyện mô hình sử dụng LoRA
🐕 Thử nghiệm với các giá trị rank khác nhau
🦁 So sánh hiệu suất với tinh chỉnh đầy đủ | [Notebook](./notebooks/finetune_sft_peft.ipynb) | Open In Colab | +| Tải LoRA Adapters | Học cách tải và sử dụng LoRA adapters đã huấn luyện | 🐢 Tải adapters đã huấn luyện trước
🐕 Gộp adapters với mô hình cơ sở
🦁 Chuyển đổi giữa nhiều adapters | [Notebook](./notebooks/load_lora_adapter.ipynb) | Open In Colab | + +## Tài nguyên +- [Tài liệu PEFT](https://huggingface.co/docs/peft) +- [Bài báo nghiên cứu LoRA](https://arxiv.org/abs/2106.09685) +- [Bài báo nghiên cứu QLoRA](https://arxiv.org/abs/2305.14314) +- [Bài báo nghiên cứu Prompt Tuning](https://arxiv.org/abs/2104.08691) +- [Hướng dẫn sử dụng PEFT của Hugging Face](https://huggingface.co/blog/peft) +- [Cách Tinh chỉnh LLM với Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) +- [Thư viện TRL](https://huggingface.co/docs/trl/index) \ No newline at end of file diff --git a/vi/3_parameter_efficient_finetuning/images/lora_adapter.png b/vi/3_parameter_efficient_finetuning/images/lora_adapter.png new file mode 100644 index 0000000000000000000000000000000000000000..65aba13fc2656cf0b63c685166c1d401a6285bea GIT binary patch literal 1239572 zcmeFZby$?^w>}O7$PC>j-5o@DZmd2@B;^jkPi=s 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z^J)O+2ibfEMU8%1I4{_9jfk6y)N&|$aujPrV_7wW+?>`N?P#^0+{URXtj6OtaEAS)WyGl$N~x)>v_ECWXHAZ3U%KwuYX3hdcB-@O99Dm zqth3je?+Fr*}kQM=Ic&|d4pFsOcq(cB1v|z6oio!l1u#wWLvr^s?SQ~1;`4%;SNp2 zs7)8NLws`V`oHPyJwo7EK1%=&R#x4=DItE_&6OP zJ~b=sr0nTAovL~-bzsr$Pms}scm7PUe_pTW@FZJYTRTyCJUI@fML}i5zcW*N2tbV{ zBL_$RBZ);N%Qper3n*G{ACC_f9Ua{WzvPPa@1uabB+Tdk{ufi#fYKHofk?*x2XO^x zVOzFb(RW?0ufzknAwz8rw;7BFlt*(Yd(E?ABAb8pUw;~P^~yhr>NLtoI0an^FMY!3 zpe{~{ymxox5ny={WhES&a}p#DONu+sxMz#;yCcn9fHd&5-Q0|xC$PCtYn9PzWkp~f zC!gX5Q?1LnI9f4j%~T5*R?^0W7l={rSwzJ4yCaSCKpWg4q&7(sSovdxk6dl|WXHko zGcgaB86SA(K@6UESgX*q;hJM0bS$LiE#@5y8G8-UIH{$_pUceg7hyofV#VvGu1^gR1;74m2I|FZUhVwFo~{o$M)@N7)yCMO6n6h{eUy@z z1NWDR<3E1zL)B7pX~k?_pZaf;QQO!^{av&E_GN(!IMc-s`BSTBz(aEm6n>Na$F&^k zv3TaY`abbvjY!fweX99fQqonk@$PvrfTYOmL7x&B){z;6b%lDy-MEAp@3Iq;ldpPO z-kfRmVhjQ~J_RW49i0GwHr5l{O`V^&X-j-BPM=inS)7};rCoA+@ScyjWOx!|jlA+V z{GJ`TZezGC=&IP-+N##MMD%ME7IIPI-&!4t|7aD^X93z~!Fr`f<+uNK<goUXUt") +peft_model_id = "" +model = PeftModel.from_pretrained(base_model, peft_model_id) +``` + +![lora_load_adapter](./images/lora_adapter.png) + +## Gộp LoRA Adapters + +Sau khi huấn luyện với LoRA, bạn có thể muốn gộp các trọng số adapter trở lại mô hình cơ sở để triển khai dễ dàng hơn. Điều này tạo ra một mô hình duy nhất với các trọng số đã kết hợp, loại bỏ nhu cầu tải adapters riêng biệt trong quá trình suy luận. + +Quá trình gộp đòi hỏi chú ý đến quản lý bộ nhớ và độ chính xác. Vì bạn sẽ cần tải cả mô hình cơ sở và trọng số adapter cùng lúc, hãy đảm bảo có đủ bộ nhớ GPU/CPU. Sử dụng `device_map="auto"` trong `transformers` sẽ giúp tự động quản lý bộ nhớ. Duy trì độ chính xác nhất quán (ví dụ: float16) trong suốt quá trình, phù hợp với độ chính xác được sử dụng trong quá trình huấn luyện và lưu mô hình đã gộp trong cùng một định dạng để triển khai. Trước khi triển khai, luôn xác thực mô hình đã gộp bằng cách so sánh đầu ra và các chỉ số hiệu suất với phiên bản dựa trên adapter. + +Adapters cũng thuận tiện cho việc chuyển đổi giữa các tác vụ hoặc lĩnh vực khác nhau. Bạn có thể tải mô hình cơ sở và trọng số adapter riêng biệt. Điều này cho phép chuyển đổi nhanh chóng giữa các trọng số cho từng tác vụ cụ thể. + +## Hướng dẫn triển khai + +Thư mục `notebooks/` chứa các hướng dẫn thực hành và bài tập để triển khai các phương pháp PEFT khác nhau. Bắt đầu với `load_lora_adapter_example.ipynb` để có giới thiệu cơ bản, sau đó khám phá `lora_finetuning.ipynb` để xem xét chi tiết hơn về cách tinh chỉnh một mô hình với LoRA và SFT. + +Khi triển khai các phương pháp PEFT, hãy bắt đầu với các giá trị hạng nhỏ (4-8) cho LoRA và theo dõi mất mát trong quá trình huấn luyện. Sử dụng tập validation để tránh overfitting và so sánh kết quả với các baseline tinh chỉnh đầy đủ khi có thể. Hiệu quả của các phương pháp khác nhau có thể thay đổi theo tác vụ, vì vậy thử nghiệm là chìa khóa. + +## OLoRA + +[OLoRA](https://arxiv.org/abs/2406.01775) sử dụng phân rã QR để khởi tạo các adapter LoRA. OLoRA dịch chuyển các trọng số cơ sở của mô hình bằng một hệ số của phân rã QR của chúng, tức là nó thay đổi trọng số trước khi thực hiện bất kỳ huấn luyện nào trên chúng. Cách tiếp cận này cải thiện đáng kể tính ổn định, tăng tốc độ hội tụ và cuối cùng đạt được hiệu suất vượt trội. + +## Sử dụng TRL với PEFT + +Các phương pháp PEFT có thể được kết hợp với thư viện TRL để tinh chỉnh hiệu quả. Sự tích hợp này đặc biệt hữu ích cho RLHF (Reinforcement Learning from Human Feedback) vì nó giảm yêu cầu bộ nhớ. + +```python +from peft import LoraConfig +from transformers import AutoModelForCausalLM + +# Tải mô hình với cấu hình PEFT +lora_config = LoraConfig( + r=16, + lora_alpha=32, + lora_dropout=0.05, + bias="none", + task_type="CAUSAL_LM" +) + +# Tải mô hình trên thiết bị cụ thể +model = AutoModelForCausalLM.from_pretrained( + "your-model-name", + load_in_8bit=True, # Tùy chọn: sử dụng độ chính xác 8-bit + device_map="auto", + peft_config=lora_config +) +``` + +Ở trên, chúng ta đã sử dụng `device_map="auto"` để tự động gán mô hình cho thiết bị phù hợp. Bạn cũng có thể gán thủ công mô hình cho một thiết bị cụ thể bằng cách sử dụng `device_map={"": device_index}`. Bạn cũng có thể mở rộng việc huấn luyện trên nhiều GPU trong khi vẫn giữ việc sử dụng bộ nhớ hiệu quả. + +## Triển khai gộp cơ bản + +Sau khi huấn luyện một adapter LoRA, bạn có thể gộp trọng số adapter trở lại mô hình cơ sở. Đây là cách thực hiện: + +```python +import torch +from transformers import AutoModelForCausalLM +from peft import PeftModel + +# 1. Tải mô hình cơ sở +base_model = AutoModelForCausalLM.from_pretrained( + "base_model_name", + torch_dtype=torch.float16, + device_map="auto" +) + +# 2. Tải LoRA adapter +peft_model = PeftModel.from_pretrained( + base_model, + "path/to/adapter", + torch_dtype=torch.float16 +) + +# 3. Gộp trọng số adapter với mô hình cơ sở +try: + merged_model = peft_model.merge_and_unload() +except RuntimeError as e: + print(f"Gộp thất bại: {e}") + # Triển khai chiến lược dự phòng hoặc tối ưu hóa bộ nhớ + +# 4. Lưu mô hình đã gộp +merged_model.save_pretrained("path/to/save/merged_model") +``` + +Nếu bạn gặp sự khác biệt về kích thước trong mô hình đã lưu, hãy đảm bảo bạn cũng lưu tokenizer: + +```python +# Lưu cả mô hình và tokenizer +tokenizer = AutoTokenizer.from_pretrained("base_model_name") +merged_model.save_pretrained("path/to/save/merged_model") +tokenizer.save_pretrained("path/to/save/merged_model") +``` + +## Các bước tiếp theo + +⏩ Chuyển sang [Hướng dẫn Prompt Tuning](prompt_tuning.md) để tìm hiểu cách tinh chỉnh một mô hình bằng prompt tuning. +⏩ Chuyển sang [Hướng dẫn lắp LoRA Adapters](./notebooks/load_lora_adapter.ipynb) để tìm hiểu cách lắp LoRA adapters. + +# Tài nguyên + +- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685) +- [Tài liệu PEFT](https://huggingface.co/docs/peft) +- [Bài viết blog của Hugging Face về PEFT](https://huggingface.co/blog/peft) \ No newline at end of file diff --git a/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb new file mode 100644 index 00000000..f53c614a --- /dev/null +++ b/vi/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb @@ -0,0 +1,510 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "z-6LLOPZouLg" + }, + "source": [ + "# Cách tinh chỉnh LLM với LoRA Adapters sử dụng Hugging Face TRL\n", + "\n", + "Trong Notebook này bạn sẽ được học cách tinh chỉnh hiệu quả các mô hình ngôn ngữ lớn sử dụng LoRA (Low-Rank Adaptation) adapters. LoRA là một kỹ thuật tinh chỉnh tham số hiệu quả:\n", + "- Đóng băng các trọng số mô hình đã huấn luyện trước\n", + "- Thêm các ma trận phân rã hạng nhỏ có thể huấn luyện vào các lớp attention \n", + "- Thường giảm khoảng 90% tham số có thể huấn luyện\n", + "- Duy trì hiệu suất mô hình trong khi sử dụng bộ nhớ hiệu quả\n", + "\n", + "Chúng ta sẽ tìm hiểu: \n", + "1. Cài đặt môi trường phát triển và cấu hình LoRA\n", + "2. Tạo và chuẩn bị dữ liệu để huấn luyện adapter \n", + "3. Tinh chỉnh sử dụng `trl` và `SFTTrainer` với LoRA adapters\n", + "4. Kiểm tra mô hình và gộp adapters (tùy chọn)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fXqd9BXgouLi" + }, + "source": [ + "## 1. Cài đặt môi trường phát triển\n", + "\n", + "Bước đầu tiên là cài đặt các thư viện Hugging Face và PyTorch, bao gồm `trl, transformers và datasets`. Nếu bạn chưa nghe nói về `trl`, đừng lo lắng. Đó là một thư viện mới trên nền tảng transformers và datasets, giúp việc tinh chỉnh các mô hình ngôn ngữ lớn (LLM) trở nên dễ dàng hơn." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tKvGVxImouLi" + }, + "outputs": [], + "source": [ + "# Cài đặt các yêu cầu trong Google Colab\n", + "# !pip install transformers datasets trl huggingface_hub\n", + "\n", + "# Xác thực với Hugging Face\n", + "from huggingface_hub import login\n", + "\n", + "login()\n", + "\n", + "# để thuận tiện bạn có thể tạo một biến môi trường chứa token hub của bạn là HF_TOKEN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XHUzfwpKouLk" + }, + "source": [ + "## 2. Tải dữ liệu " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "z4p6Bvo7ouLk" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['full_topic', 'messages'],\n", + " num_rows: 2260\n", + " })\n", + " test: Dataset({\n", + " features: ['full_topic', 'messages'],\n", + " num_rows: 119\n", + " })\n", + "})" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Tải một tập dữ liệu mẫu\n", + "from datasets import load_dataset\n", + "\n", + "# TODO: xác định dataset và config của bạn sử dụng các tham số path và name\n", + "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n", + "dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9TOhJdtsouLk" + }, + "source": [ + "## 3. Tinh chỉnh LLM sử dụng `trl` và `SFTTrainer` với LoRA\n", + "\n", + "[SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) từ `trl` cung cấp tích hợp với LoRA adapters thông qua thư viện [PEFT](https://huggingface.co/docs/peft/en/index). Những lợi thế chính của cài đặt này bao gồm:\n", + "\n", + "1. **Hiệu quả bộ nhớ**:\n", + " - Chỉ các tham số adapter được lưu trữ trong bộ nhớ GPU \n", + " - Trọng số mô hình cơ sở vẫn đóng băng và có thể được tải với độ chính xác thấp hơn\n", + " - Cho phép tinh chỉnh các mô hình lớn trên GPU tiêu dùng\n", + "\n", + "2. **Tính năng huấn luyện**:\n", + " - Tích hợp PEFT/LoRA sẵn có với cài đặt tối thiểu\n", + " - Hỗ trợ QLoRA (LoRA lượng tử hóa) cho hiệu quả bộ nhớ tốt hơn\n", + "\n", + "3. **Quản lý Adapter**: \n", + " - Lưu trọng số adapter trong quá trình checkpoint\n", + " - Tính năng gộp adapters trở lại mô hình cơ sở\n", + "\n", + "Chúng ta sẽ sử dụng LoRA trong ví dụ của mình, **kết hợp LoRA với lượng tử hóa 4-bit** để giảm thêm việc sử dụng bộ nhớ mà không ảnh hưởng đến hiệu suất. Cài đặt chỉ yêu cầu một vài bước cấu hình:\n", + "1. Xác định cấu hình LoRA (rank, alpha, dropout)\n", + "2. Tạo SFTTrainer với cấu hình PEFT \n", + "3. Huấn luyện và lưu trọng số adapter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Các thư viện cần thiết \n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "from datasets import load_dataset\n", + "from trl import SFTConfig, SFTTrainer, setup_chat_format\n", + "import torch\n", + "\n", + "device = (\n", + " \"cuda\"\n", + " if torch.cuda.is_available()\n", + " else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n", + ")\n", + "\n", + "# Tải mô hình và tokenizer\n", + "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " pretrained_model_name_or_path=model_name\n", + ").to(device)\n", + "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n", + "\n", + "# Thiết lập định dạng chat\n", + "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n", + "\n", + "# Đặt tên cho bản tinh chỉnh để lưu &/ tải lên\n", + "finetune_name = \"SmolLM2-FT-LoRA-Adapter\"\n", + "finetune_tags = [\"smol-course\", \"module_3\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZbuVArTHouLk" + }, + "source": [ + "`SFTTrainer` hỗ trợ tích hợp sẵn với `peft`, điều này giúp tinh chỉnh hiệu quả các LLM dễ dàng hơn bằng cách sử dụng, ví dụ như LoRA. Chúng ta chỉ cần tạo `LoraConfig` và cung cấp nó cho trainer.\n", + "\n", + "

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "blDSs9swouLk" + }, + "outputs": [], + "source": [ + "from peft import LoraConfig\n", + "\n", + "# TODO: Điều chỉnh cấu hình tham số LoRA\n", + "# r: chiều rank cho ma trận cập nhật LoRA (nhỏ hơn = nén nhiều hơn)\n", + "rank_dimension = 4\n", + "# lora_alpha: hệ số tỷ lệ cho các lớp LoRA (cao hơn = điều chỉnh mạnh hơn) \n", + "lora_alpha = 8\n", + "# lora_dropout: xác suất dropout cho các lớp LoRA (giúp tránh overfitting)\n", + "lora_dropout = 0.05\n", + "\n", + "peft_config = LoraConfig(\n", + " r=rank_dimension, # Chiều rank - thường từ 4-32\n", + " lora_alpha=lora_alpha, # Hệ số tỷ lệ LoRA - thường gấp 2 lần rank\n", + " lora_dropout=lora_dropout, # Xác suất dropout cho các lớp LoRA \n", + " bias=\"none\", # Loại bias cho LoRA. các bias tương ứng sẽ được cập nhật trong quá trình huấn luyện.\n", + " target_modules=\"all-linear\", # Những module nào áp dụng LoRA\n", + " task_type=\"CAUSAL_LM\", # Loại tác vụ cho kiến trúc mô hình\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l5NUDPcaouLl" + }, + "source": [ + "Trước khi bắt đầu huấn luyện, chúng ta cần xác định các `siêu tham số (TrainingArguments)` mà chúng ta muốn sử dụng." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NqT28VZlouLl" + }, + "outputs": [], + "source": [ + "# Cấu hình huấn luyện \n", + "# Siêu tham số dựa trên khuyến nghị từ bài báo QLoRA \n", + "args = SFTConfig(\n", + " # Cài đặt đầu ra\n", + " output_dir=finetune_name, # Thư mục để lưu checkpoint mô hình\n", + " # Thời gian huấn luyện\n", + " num_train_epochs=1, # Số epoch huấn luyện\n", + " # Cài đặt kích thước batch \n", + " per_device_train_batch_size=2, # Kích thước batch cho mỗi GPU\n", + " gradient_accumulation_steps=2, # Tích lũy gradient cho batch hiệu quả lớn hơn\n", + " # Tối ưu bộ nhớ\n", + " gradient_checkpointing=True, # Đánh đổi tính toán để tiết kiệm bộ nhớ\n", + " # Cài đặt optimizer\n", + " optim=\"adamw_torch_fused\", # Sử dụng AdamW fusion cho hiệu quả\n", + " learning_rate=2e-4, # Tốc độ học (từ bài báo QLoRA)\n", + " max_grad_norm=0.3, # Ngưỡng cắt gradient\n", + " # Lịch trình học\n", + " warmup_ratio=0.03, # Phần bước cho warmup\n", + " lr_scheduler_type=\"constant\", # Giữ tốc độ học không đổi sau warmup\n", + " # Ghi log và lưu\n", + " logging_steps=10, # Ghi metrics mỗi N bước\n", + " save_strategy=\"epoch\", # Lưu checkpoint mỗi epoch\n", + " # Cài đặt độ chính xác\n", + " bf16=True, # Sử dụng độ chính xác bfloat16\n", + " # Cài đặt tích hợp\n", + " push_to_hub=False, # Không đẩy lên HuggingFace Hub\n", + " report_to=None, # Tắt ghi log bên ngoài\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cGhR7uFBouLl" + }, + "source": [ + "Bây giờ chúng ta đã có mọi thứ cần thiết để tạo `SFTTrainer` và bắt đầu huấn luyện mô hình của mình." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "M00Har2douLl" + }, + "outputs": [], + "source": [ + "max_seq_length = 1512 # độ dài chuỗi tối đa cho mô hình và đóng gói (packing) bộ dữ liệu\n", + "\n", + "# Tạo SFTTrainer với cấu hình LoRA\n", + "trainer = SFTTrainer(\n", + " model=model,\n", + " args=args,\n", + " train_dataset=dataset[\"train\"],\n", + " peft_config=peft_config, # Cấu hình LoRA\n", + " max_seq_length=max_seq_length, # Độ dài chuỗi tối đa \n", + " tokenizer=tokenizer,\n", + " packing=True, # Bật đóng gói đầu vào cho hiệu quả \n", + " dataset_kwargs={\n", + " \"add_special_tokens\": False, # Token đặc biệt được xử lý bởi template\n", + " \"append_concat_token\": False, # Không cần thêm separator \n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zQ_kRN24ouLl" + }, + "source": [ + "Bắt đầu huấn luyện mô hình bằng cách gọi phương thức `train()` trên `Trainer` của chúng ta. Việc này sẽ bắt đầu vòng lặp huấn luyện và huấn luyện mô hình của chúng ta trong `3 epochs`. Vì chúng ta đang sử dụng phương pháp PEFT, chúng ta sẽ chỉ lưu phần trọng số của adapter đã điều chỉnh và không lưu toàn bộ mô hình." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Tq4nIYqKouLl" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "300e5dfbb4b54750b77324345c7591f9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/72 [00:00\n", + "

Bài tập Bonus: Tải LoRA Adapter

\n", + "

Sử dụng những gì bạn đã học được từ notebook ví dụ để tải adapter LoRA đã huấn luyện của bạn cho suy luận.

\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "I5B494OdouLl" + }, + "outputs": [], + "source": [ + "# giải phóng bộ nhớ một lần nữa\n", + "del model\n", + "del trainer\n", + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "P1UhohVdouLl" + }, + "outputs": [], + "source": [ + "import torch\n", + "from peft import AutoPeftModelForCausalLM \n", + "from transformers import AutoTokenizer, pipeline\n", + "\n", + "# Tải Mô hình với PEFT adapter\n", + "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n", + "model = AutoPeftModelForCausalLM.from_pretrained(\n", + " finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n", + ")\n", + "pipe = pipeline(\n", + " \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "99uFDAuuouLl" + }, + "source": [ + "Hãy thử một số chỉ định mẫu và xem mô hình hoạt động như thế nào." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "id": "-shSmUbvouLl", + "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824" + }, + "outputs": [], + "source": [ + "prompts = [\n", + " \"Thủ đô của Việt Nam thành phố nào? Giải thích tại sao là như vậy và liệu nó có khác trong quá khứ không?\",\n", + " \"Viết một hàm Python để tính giai thừa của một số.\",\n", + " \"Một khu vườn hình chữ nhật có chiều dài 25 mét và chiều rộng 15 mét. Nếu bạn muốn xây một hàng rào xung quanh toàn bộ khu vườn, bạn sẽ cần bao nhiêu mét hàng rào?\",\n", + " \"Sự khác biệt giữa trái cây và rau củ là gì? Đưa ra ví dụ cho mỗi loại.\",\n", + "]\n", + "\n", + "\n", + "def test_inference(prompt):\n", + " prompt = pipe.tokenizer.apply_chat_template(\n", + " [{\"role\": \"user\", \"content\": prompt}],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + " )\n", + " outputs = pipe(\n", + " prompt,\n", + " )\n", + " return outputs[0][\"generated_text\"][len(prompt):].strip()\n", + "\n", + "\n", + "for prompt in prompts:\n", + " print(f\" prompt:\\n{prompt}\")\n", + " print(f\" response:\\n{test_inference(prompt)}\")\n", + " print(\"-\" * 50)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": ".venv", + "language": "python", + "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" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb new file mode 100644 index 00000000..73d258e9 --- /dev/null +++ b/vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb @@ -0,0 +1,5059 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "otj46qRbtpnd", + "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4" + }, + "outputs": [], + "source": [ + "!pip install -q bitsandbytes datasets accelerate loralib\n", + "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 446, + "referenced_widgets": [ + 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"4f1b47e2d7d64765b4f12dcc16878f1f", + "993fcaf75d47402d9f9f7c2b094295f1", + "e6787d84d8234d368dc48f8d43bdb2a1", + "5ecb188fa26a499ea67954aa90108f6e", + "1d3184ff92b7465da9f36eeb5726f021", + "74af354462ec4d4a83654c5b8ba7a2f9" + ] + }, + "id": "cg3fiQOvmI3Q", + "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89" + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n", + "import torch\n", + "import torch.nn as nn\n", + "import bitsandbytes as bnb\n", + "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S65GcxNGA9kz" + }, + "source": [ + "## Sử dụng adapters từ Hugging Face Hub" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 517, + "referenced_widgets": [ + "ff2454cf69b346fea70070522cf93689", + "87b36eb4ad3b4047a32c7d67a5aabc5e", + 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get_peft_model\n", + "from peft import PeftModel, PeftConfig\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "\n", + "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n", + "config = PeftConfig.from_pretrained(peft_model_id)\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " config.base_model_name_or_path,\n", + " return_dict=True,\n", + " load_in_8bit=True,\n", + " device_map=\"auto\",\n", + ")\n", + "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n", + "\n", + "# Tải Lora adapter\n", + "model = PeftModel.from_pretrained(model, peft_model_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MDqJWba-tpnv", + "outputId": "dd65644f-6921-46e1-920c-400bd7d888cf" + }, + "outputs": [], + "source": [ + "batch = tokenizer(\"Machine learning is: \", return_tensors='pt')\n", + "\n", + "with torch.cuda.amp.autocast():\n", + " output_tokens = 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"nbformat_minor": 0 +} diff --git a/vi/3_parameter_efficient_finetuning/prompt_tuning.md b/vi/3_parameter_efficient_finetuning/prompt_tuning.md new file mode 100644 index 00000000..241f624f --- /dev/null +++ b/vi/3_parameter_efficient_finetuning/prompt_tuning.md @@ -0,0 +1,76 @@ +# Phương Pháp Điều Chỉnh Chỉ Thị (Prompt Tuning) + +Prompt tuning là một phương pháp tiết kiệm tham số bằng cách điều chỉnh biểu diễn đầu vào thay vì trọng số mô hình. Không giống như tinh chỉnh truyền thống cập nhật tất cả các tham số mô hình, prompt tuning thêm và tối ưu hóa một tập nhỏ các token có thể huấn luyện trong khi giữ nguyên mô hình cơ sở. + +## Tìm Hiểu Về Prompt Tuning + +Prompt tuning là một phương pháp thay thế tiết kiệm tham số cho việc tinh chỉnh mô hình bằng cách thêm các vector liên tục có thể huấn luyện (soft prompts) vào trước văn bản đầu vào. Không giống như các chỉ thị rời rạc (discrete prompt), những soft prompt này được học thông qua truyền ngược (back-propagation) trong khi giữ nguyên mô hình ngôn ngữ. Phương pháp này được giới thiệu trong ["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691) (Lester et al., 2021), cho thấy prompt tuning trở nên cạnh tranh hơn với việc tinh chỉnh mô hình khi kích thước mô hình tăng lên. Trong bài báo, với khoảng 10 tỷ tham số, prompt tuning đạt hiệu suất tương đương với tinh chỉnh mô hình trong khi chỉ sửa đổi vài trăm tham số cho mỗi tác vụ. + +Các soft prompt này là các vector liên tục trong không gian embedding của mô hình được tối ưu hóa trong quá trình huấn luyện. Không giống như các chỉ thị rời rạc truyền thống sử dụng các token ngôn ngữ tự nhiên, soft prompt là các chỉ thị không có ý nghĩa nhưng được học để tạo ra hành vi mong muốn từ mô hình đã đóng băng thông qua gradient descent. Kỹ thuật này đặc biệt hiệu quả cho các kịch bản đa tác vụ vì mỗi tác vụ chỉ cần lưu trữ một vector prompt nhỏ (thường là vài trăm tham số) thay vì một bản sao mô hình đầy đủ. Phương pháp này không chỉ duy trì bộ nhớ tối thiểu mà còn cho phép chuyển đổi tác vụ nhanh chóng bằng cách chỉ cần hoán đổi vector prompt mà không cần tải lại mô hình. + +## Quá trình huấn luyện + +Soft prompt thường có từ **8 đến 32 token** và có thể được **khởi tạo ngẫu nhiên hoặc từ văn bản hiện có**. Phương pháp khởi tạo đóng vai trò quan trọng trong quá trình huấn luyện, với việc khởi tạo dựa trên văn bản thường hoạt động tốt hơn so với khởi tạo ngẫu nhiên. + +Trong quá trình huấn luyện, chỉ các tham số prompt được cập nhật trong khi mô hình cơ sở vẫn đóng băng. Cách tiếp cận tập trung này sử dụng các mục tiêu huấn luyện tiêu chuẩn nhưng đòi hỏi chú ý đến tốc độ học và hành vi gradient của các token prompt. + +![prompt_tuning_explain](./images/prompt_tuning.png) + +## Triển khai với PEFT + +Thư viện PEFT giúp việc triển khai prompt tuning trở nên đơn giản. Đây là một ví dụ cơ bản: + +```python +from peft import PromptTuningConfig, TaskType, get_peft_model +from transformers import AutoModelForCausalLM, AutoTokenizer + +# Tải mô hình cơ sở +model = AutoModelForCausalLM.from_pretrained("your-base-model") +tokenizer = AutoTokenizer.from_pretrained("your-base-model") + +# Điều chỉnh thông số prompt tuning +peft_config = PromptTuningConfig( + task_type=TaskType.CAUSAL_LM, + num_virtual_tokens=8, # Số token có thể huấn luyện + prompt_tuning_init="TEXT", # Khởi tạo từ văn bản + prompt_tuning_init_text="Phân loại xem văn bản này là tích cực hay tiêu cực:", + tokenizer_name_or_path="your-base-model", +) + +# Tạo mô hình có thể prompt-tune +model = get_peft_model(model, peft_config) +``` + +## So sánh với các phương pháp khác + +Khi so sánh với các phương pháp PEFT khác, prompt tuning nổi bật về hiệu quả. Trong khi LoRA cung cấp số lượng tham số và bộ nhớ thấp nhưng yêu cầu tải adapter khi chuyển đổi tác vụ, prompt tuning đạt được mức sử dụng tài nguyên thậm chí thấp hơn và cho phép chuyển đổi tác vụ ngay lập tức. Ngược lại, tinh chỉnh đầy đủ đòi hỏi tài nguyên đáng kể và yêu cầu các bản sao mô hình riêng biệt cho các tác vụ khác nhau. + +| Phương pháp | Tham số | Bộ nhớ | Chuyển đổi tác vụ | +|--------|------------|---------|----------------| +| Prompt Tuning | Rất thấp | Tối thiểu | Dễ dàng | +| LoRA | Thấp | Thấp | Yêu cầu tải | +| Tinh chỉnh đầy đủ | Cao | Cao | Cần bản sao mô hình mới | + +Khi triển khai prompt tuning, hãy bắt đầu với một số lượng nhỏ token ảo (8-16) và chỉ tăng lên nếu độ phức tạp của tác vụ yêu cầu. Khởi tạo từ văn bản thường cho kết quả tốt hơn so với khởi tạo ngẫu nhiên, đặc biệt khi sử dụng văn bản liên quan đến tác vụ. Chiến lược khởi tạo nên phản ánh độ phức tạp của tác vụ mục tiêu của bạn. + +Huấn luyện đòi hỏi những cân nhắc hơi khác so với tinh chỉnh đầy đủ. Tốc độ học (learning rates) cao hơn thường sẽ cho kết quả tốt hơn, nhưng cũng cần theo dõi cẩn thận *gradient* của token prompt. Kiểm tra thường xuyên trên các ví dụ đa dạng giúp đảm bảo hiệu suất mạnh mẽ trong các tình huống khác nhau. + +## Ứng dụng + +Prompt tuning xuất sắc trong một số kịch bản: + +1. Triển khai đa tác vụ +2. Môi trường hạn chế tài nguyên +3. Thích ứng tác vụ nhanh chóng +4. Ứng dụng nhạy cảm về quyền riêng tư + +Khi mô hình nhỏ hơn, prompt tuning trở nên kém cạnh tranh hơn so với tinh chỉnh đầy đủ. Ví dụ, trên các mô hình như SmolLM2, prompt tuning ít liên quan hơn so với tinh chỉnh đầy đủ. + +## Các bước tiếp theo + +⏭️ Chuyển sang [Hướng dẫn LoRA Adapters](./notebooks/finetune_sft_peft.ipynb) để tìm hiểu cách tinh chỉnh một mô hình với LoRA adapters. + +## Tài nguyên +- [Tài liệu về thư viện PEFT](https://huggingface.co/docs/peft) +- [Bài báo nghiên cứu về Prompt Tuning](https://arxiv.org/abs/2104.08691) +- [Hướng dẫn thực hành của Hugging Face](https://huggingface.co/learn/cookbook/prompt_tuning_peft) \ No newline at end of file diff --git a/vi/README.md b/vi/README.md index 3e3a5a17..ce109d5e 100644 --- a/vi/README.md +++ b/vi/README.md @@ -1,4 +1,4 @@ -![smolcourse image](./banner.png) +![smolcourse image](../banner.png) # Khoá học Mô hình ngôn ngữ cơ bản @@ -6,7 +6,7 @@ *Lưu ý: Vì khóa học được dịch từ bản gốc tiếng Anh, chúng tôi sẽ giữ lại một số thuật ngữ gốc nhằm tránh gây hiểu lầm.* - +s From 4cea4cc8e55c239d765b3857d1b1b340eb459a23 Mon Sep 17 00:00:00 2001 From: hahuyhoang411 Date: Sun, 22 Dec 2024 00:06:06 +0100 Subject: [PATCH 2/7] typo --- vi/3_parameter_efficient_finetuning/README.md | 2 +- .../lora_adapters.md | 2 +- .../prompt_tuning.md | 2 +- vi/4_evaluation/README.md | 37 + vi/4_evaluation/automatic_benchmarks.md | 131 + vi/4_evaluation/custom_evaluation.md | 132 + ...hteval_evaluate_and_analyse_your_LLM.ipynb | 19422 ++++++++++++++++ vi/4_evaluation/project/README.md | 85 + vi/4_evaluation/project/annotate_dataset.py | 129 + vi/4_evaluation/project/create_dataset.py | 72 + vi/4_evaluation/project/evaluation_task.py | 86 + vi/4_evaluation/project/generate_dataset.py | 167 + .../images/domain_eval_argilla_view.png | Bin 0 -> 197693 bytes .../images/domain_eval_dataset_viewer.png | Bin 0 -> 89114 bytes 14 files changed, 20264 insertions(+), 3 deletions(-) create mode 100644 vi/4_evaluation/README.md create mode 100644 vi/4_evaluation/automatic_benchmarks.md create mode 100644 vi/4_evaluation/custom_evaluation.md create mode 100644 vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb create mode 100644 vi/4_evaluation/project/README.md create mode 100644 vi/4_evaluation/project/annotate_dataset.py create mode 100644 vi/4_evaluation/project/create_dataset.py create mode 100644 vi/4_evaluation/project/evaluation_task.py create mode 100644 vi/4_evaluation/project/generate_dataset.py create mode 100644 vi/4_evaluation/project/images/domain_eval_argilla_view.png create mode 100644 vi/4_evaluation/project/images/domain_eval_dataset_viewer.png diff --git a/vi/3_parameter_efficient_finetuning/README.md b/vi/3_parameter_efficient_finetuning/README.md index 207aa18d..447097e3 100644 --- a/vi/3_parameter_efficient_finetuning/README.md +++ b/vi/3_parameter_efficient_finetuning/README.md @@ -28,7 +28,7 @@ Prompt tuning cung cấp cách tiếp cận **thậm chí nhẹ hơn** bằng c | Tinh chỉnh LoRA | Học cách tinh chỉnh mô hình sử dụng LoRA adapters | 🐢 Huấn luyện mô hình sử dụng LoRA
🐕 Thử nghiệm với các giá trị rank khác nhau
🦁 So sánh hiệu suất với tinh chỉnh đầy đủ | [Notebook](./notebooks/finetune_sft_peft.ipynb) | Open In Colab | | Tải LoRA Adapters | Học cách tải và sử dụng LoRA adapters đã huấn luyện | 🐢 Tải adapters đã huấn luyện trước
🐕 Gộp adapters với mô hình cơ sở
🦁 Chuyển đổi giữa nhiều adapters | [Notebook](./notebooks/load_lora_adapter.ipynb) | Open In Colab | -## Tài nguyên +## Tài liệu tham khảo - [Tài liệu PEFT](https://huggingface.co/docs/peft) - [Bài báo nghiên cứu LoRA](https://arxiv.org/abs/2106.09685) - [Bài báo nghiên cứu QLoRA](https://arxiv.org/abs/2305.14314) diff --git a/vi/3_parameter_efficient_finetuning/lora_adapters.md b/vi/3_parameter_efficient_finetuning/lora_adapters.md index 449dc2b9..d8fdafe4 100644 --- a/vi/3_parameter_efficient_finetuning/lora_adapters.md +++ b/vi/3_parameter_efficient_finetuning/lora_adapters.md @@ -117,7 +117,7 @@ tokenizer.save_pretrained("path/to/save/merged_model") ⏩ Chuyển sang [Hướng dẫn Prompt Tuning](prompt_tuning.md) để tìm hiểu cách tinh chỉnh một mô hình bằng prompt tuning. ⏩ Chuyển sang [Hướng dẫn lắp LoRA Adapters](./notebooks/load_lora_adapter.ipynb) để tìm hiểu cách lắp LoRA adapters. -# Tài nguyên +# Tài liệu tham khảo - [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685) - [Tài liệu PEFT](https://huggingface.co/docs/peft) diff --git a/vi/3_parameter_efficient_finetuning/prompt_tuning.md b/vi/3_parameter_efficient_finetuning/prompt_tuning.md index 241f624f..3e09a107 100644 --- a/vi/3_parameter_efficient_finetuning/prompt_tuning.md +++ b/vi/3_parameter_efficient_finetuning/prompt_tuning.md @@ -70,7 +70,7 @@ Khi mô hình nhỏ hơn, prompt tuning trở nên kém cạnh tranh hơn so v ⏭️ Chuyển sang [Hướng dẫn LoRA Adapters](./notebooks/finetune_sft_peft.ipynb) để tìm hiểu cách tinh chỉnh một mô hình với LoRA adapters. -## Tài nguyên +## Tài liệu tham khảo - [Tài liệu về thư viện PEFT](https://huggingface.co/docs/peft) - [Bài báo nghiên cứu về Prompt Tuning](https://arxiv.org/abs/2104.08691) - [Hướng dẫn thực hành của Hugging Face](https://huggingface.co/learn/cookbook/prompt_tuning_peft) \ No newline at end of file diff --git a/vi/4_evaluation/README.md b/vi/4_evaluation/README.md new file mode 100644 index 00000000..525723c6 --- /dev/null +++ b/vi/4_evaluation/README.md @@ -0,0 +1,37 @@ +# Đánh giá mô hình + +Đánh giá là bước quan trọng trong việc phát triển và triển khai các mô hình ngôn ngữ. Nó giúp chúng ta hiểu được mô hình hoạt động tốt như thế nào qua các khả năng khác nhau và xác định những lĩnh vực cần cải thiện. Chương này bao gồm các phương pháp đánh giá (benchmark) tiêu chuẩn và các phương pháp đánh giá theo lĩnh vực cụ thể để đánh giá toàn diện mô hình của bạn. + +Chúng ta sẽ sử dụng [`lighteval`](https://github.com/huggingface/lighteval), một thư viện chuyên được sử dụng để đánh giá mô hình ngôn ngữ được phát triển bởi Hugging Face. Thư viện này được thiết kế tối giản giúp người sử dụng dễ dàng tiếp cận và được tích hợp để phù hợp với hệ sinh thái của Hugging Face. Để tìm hiểu sâu hơn về các khái niệm và thực hành về đánh giá mô hình ngôn ngữ, hãy tham khảo [hướng dẫn](https://github.com/huggingface/evaluation-guidebook) về đánh giá. + +## Tổng quan chương học + +Một quy trình đánh giá mô hình thường bao gồm nhiều khía cạnh về hiệu năng của mô hình. Chúng ta đánh giá các khả năng theo tác vụ cụ thể như *trả lời câu hỏi* và *tóm tắt* để hiểu mô hình xử lý các loại vấn đề khác nhau tốt đến đâu. Chúng ta đo lường chất lượng đầu ra thông qua các yếu tố như *tính mạch lạc* và *độ chính xác*. Đánh giá về *độ an toàn* giúp xác định các đầu ra của mô hình có chứa các hành vi khuyển khích thực hiện hành đông độc hại hoặc định kiến tiềm ẩn. Cuối cùng, kiểm tra *chuyên môn* về lĩnh vực xác minh kiến thức chuyên biệt của mô hình trong lĩnh vực mục tiêu của bạn. + +## Nội dung + +### 1️⃣ [Đánh giá mô hình tự động](./automatic_benchmarks.md) + +Học cách đánh giá mô hình của bạn bằng các phương pháp đánh giá và số liệu chuẩn hoá. Chúng ta sẽ khám phá các phương pháp đánh giá phổ biến như `MMLU` và `TruthfulQA`, hiểu các chỉ số đánh giá và cài đặt quan trọng, đồng thời đề cập đến các thực hành tốt nhất cho việc đánh giá có thể tái tạo lại. + +### 2️⃣ [Đánh giá theo yêu cầu đặc biệt](./custom_evaluation.md) +Khám phá cách tạo quy trình (pipeline) đánh giá phù hợp với trường hợp sử dụng cụ thể của bạn. Chúng ta sẽ hướng dẫn thiết kế các tác vụ đánh giá theo yêu cầu (custom), triển khai các số liệu chuyên biệt và xây dựng tập dữ liệu đánh giá phù hợp với yêu cầu của bạn. + +### 3️⃣ [Dự án đánh giá theo lĩnh vực cụ thể](./project/README.md) +Theo dõi một ví dụ hoàn chỉnh về việc xây dựng quy trình đánh giá cho lĩnh vực cụ thể. Bạn sẽ học cách tạo tập dữ liệu đánh giá, sử dụng `Argilla` để gán nhãn dữ liệu, tạo tập dữ liệu chuẩn hoá và đánh giá mô hình bằng `LightEval`. + +### Các notebook bài tập + +| Tiêu đề | Mô tả | Bài tập | Link | Colab | +|---------|-------|---------|------|-------| +| Đánh giá và phân tích mô hình LLM của bạn | Học cách sử dụng LightEval để đánh giá và so sánh các mô hình trên các lĩnh vực cụ thể | 🐢 Sử dụng các tác vụ trong lĩnh vực y tế để đánh giá mô hình
🐕 Tạo đánh giá lĩnh vực mới với các tác vụ MMLU khác nhau
🦁 Tạo tác vụ đánh giá tùy chỉnh cho lĩnh vực của bạn | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | Open In Colab | + +## Tài nguyên + +- [Hướng dẫn đánh giá](https://github.com/huggingface/evaluation-guidebook) - Hướng dẫn toàn diện về đánh giá LLM +- [Tài liệu LightEval](https://github.com/huggingface/lighteval) - Tài liệu chính thức cho thư viện LightEval +- [Tài liệu Argilla](https://docs.argilla.io) - Tìm hiểu về nền tảng gán nhãn Argilla +- [Paper MMLU](https://arxiv.org/abs/2009.03300) - Paper mô tả benchmark MMLU +- [Tạo tác vụ tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task) +- [Tạo số liệu tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric) +- [Sử dụng số liệu có sẵn](https://github.com/huggingface/lighteval/wiki/Metric-List) \ No newline at end of file diff --git a/vi/4_evaluation/automatic_benchmarks.md b/vi/4_evaluation/automatic_benchmarks.md new file mode 100644 index 00000000..6a980fa4 --- /dev/null +++ b/vi/4_evaluation/automatic_benchmarks.md @@ -0,0 +1,131 @@ +# Automatic Benchmarks + +Automatic benchmarks serve as standardized tools for evaluating language models across different tasks and capabilities. While they provide a useful starting point for understanding model performance, it's important to recognize that they represent only one piece of a comprehensive evaluation strategy. + +## Understanding Automatic Benchmarks + +Automatic benchmarks typically consist of curated datasets with predefined tasks and evaluation metrics. These benchmarks aim to assess various aspects of model capability, from basic language understanding to complex reasoning. The key advantage of using automatic benchmarks is their standardization - they allow for consistent comparison across different models and provide reproducible results. + +However, it's crucial to understand that benchmark performance doesn't always translate directly to real-world effectiveness. A model that excels at academic benchmarks may still struggle with specific domain applications or practical use cases. + +## Benchmarks and Their Limitations + +### General Knowledge Benchmarks + +MMLU (Massive Multitask Language Understanding) tests knowledge across 57 subjects, from science to humanities. While comprehensive, it may not reflect the depth of expertise needed for specific domains. TruthfulQA evaluates a model's tendency to reproduce common misconceptions, though it can't capture all forms of misinformation. + +### Reasoning Benchmarks +BBH (Big Bench Hard) and GSM8K focus on complex reasoning tasks. BBH tests logical thinking and planning, while GSM8K specifically targets mathematical problem-solving. These benchmarks help assess analytical capabilities but may not capture the nuanced reasoning required in real-world scenarios. + +### Language Understanding +HELM provides a holistic evaluation framework, while WinoGrande tests common sense through pronoun disambiguation. These benchmarks offer insights into language processing capabilities but may not fully represent the complexity of natural conversation or domain-specific terminology. + +## Alternative Evaluation Approaches + +Many organizations have developed alternative evaluation methods to address the limitations of standard benchmarks: + +### LLM-as-Judge +Using one language model to evaluate another's outputs has become increasingly popular. This approach can provide more nuanced feedback than traditional metrics, though it comes with its own biases and limitations. + +### Evaluation Arenas +Platforms like Anthropic's Constitutional AI Arena allow models to interact and evaluate each other in controlled environments. This can reveal strengths and weaknesses that might not be apparent in traditional benchmarks. + +### Custom Benchmark Suites +Organizations often develop internal benchmark suites tailored to their specific needs and use cases. These might include domain-specific knowledge tests or evaluation scenarios that mirror actual deployment conditions. + +## Creating Your Own Evaluation Strategy + +Remember that while LightEval makes it easy to run standard benchmarks, you should also invest time in developing evaluation methods specific to your use case. + +While standard benchmarks provide a useful baseline, they shouldn't be your only evaluation method. Here's how to develop a more comprehensive approach: + +1. Start with relevant standard benchmarks to establish a baseline and enable comparison with other models. + +2. Identify the specific requirements and challenges of your use case. What tasks will your model actually perform? What kinds of errors would be most problematic? + +3. Develop custom evaluation datasets that reflect your actual use case. This might include: + - Real user queries from your domain + - Common edge cases you've encountered + - Examples of particularly challenging scenarios + +4. Consider implementing a multi-layered evaluation strategy: + - Automated metrics for quick feedback + - Human evaluation for nuanced understanding + - Domain expert review for specialized applications + - A/B testing in controlled environments + +## Using LightEval for Benchmarking + +LightEval tasks are defined using a specific format: +``` +{suite}|{task}|{num_few_shot}|{auto_reduce} +``` + +- **suite**: The benchmark suite (e.g., 'mmlu', 'truthfulqa') +- **task**: Specific task within the suite (e.g., 'abstract_algebra') +- **num_few_shot**: Number of examples to include in prompt (0 for zero-shot) +- **auto_reduce**: Whether to automatically reduce few-shot examples if prompt is too long (0 or 1) + +Example: `"mmlu|abstract_algebra|0|0"` evaluates on MMLU's abstract algebra task with zero-shot inference. + +### Example Evaluation Pipeline + +Here's a complete example of setting up and running an evaluation on automatic benchmarks relevant to one specific domain: + +```python +from lighteval.tasks import Task, Pipeline +from transformers import AutoModelForCausalLM + +# Define tasks to evaluate +domain_tasks = [ + "mmlu|anatomy|0|0", + "mmlu|high_school_biology|0|0", + "mmlu|high_school_chemistry|0|0", + "mmlu|professional_medicine|0|0" +] + +# Configure pipeline parameters +pipeline_params = { + "max_samples": 40, # Number of samples to evaluate + "batch_size": 1, # Batch size for inference + "num_workers": 4 # Number of worker processes +} + +# Create evaluation tracker +evaluation_tracker = EvaluationTracker( + output_path="./results", + save_generations=True +) + +# Load model and create pipeline +model = AutoModelForCausalLM.from_pretrained("your-model-name") +pipeline = Pipeline( + tasks=domain_tasks, + pipeline_parameters=pipeline_params, + evaluation_tracker=evaluation_tracker, + model=model +) + +# Run evaluation +pipeline.evaluate() + +# Get and display results +results = pipeline.get_results() +pipeline.show_results() +``` + +Results are displayed in a tabular format showing: +``` +| Task |Version|Metric|Value | |Stderr| +|----------------------------------------|------:|------|-----:|---|-----:| +|all | |acc |0.3333|± |0.1169| +|leaderboard:mmlu:_average:5 | |acc |0.3400|± |0.1121| +|leaderboard:mmlu:anatomy:5 | 0|acc |0.4500|± |0.1141| +|leaderboard:mmlu:high_school_biology:5 | 0|acc |0.1500|± |0.0819| +``` + +You can also handle the results in a pandas DataFrame and visualise or represent them as you want. + +# Next Steps + +⏩ Explore [Custom Domain Evaluation](./custom_evaluation.md) to learn how to create evaluation pipelines tailored to your specific needs diff --git a/vi/4_evaluation/custom_evaluation.md b/vi/4_evaluation/custom_evaluation.md new file mode 100644 index 00000000..c82868a7 --- /dev/null +++ b/vi/4_evaluation/custom_evaluation.md @@ -0,0 +1,132 @@ +# Custom Domain Evaluation + +While standard benchmarks provide valuable insights, many applications require specialized evaluation approaches tailored to specific domains or use cases. This guide will help you create custom evaluation pipelines that accurately assess your model's performance in your target domain. + +## Designing Your Evaluation Strategy + +A successful custom evaluation strategy starts with clear objectives. Consider what specific capabilities your model needs to demonstrate in your domain. This might include technical knowledge, reasoning patterns, or domain-specific formats. Document these requirements carefully - they'll guide both your task design and metric selection. + +Your evaluation should test both standard use cases and edge cases. For example, in a medical domain, you might evaluate both common diagnostic scenarios and rare conditions. In financial applications, you might test both routine transactions and complex edge cases involving multiple currencies or special conditions. + +## Implementation with LightEval + +LightEval provides a flexible framework for implementing custom evaluations. Here's how to create a custom task: + +```python +from lighteval.tasks import Task, Doc +from lighteval.metrics import SampleLevelMetric, MetricCategory, MetricUseCase + +class CustomEvalTask(Task): + def __init__(self): + super().__init__( + name="custom_task", + version="0.0.1", + metrics=["accuracy", "f1"], # Your chosen metrics + description="Description of your custom evaluation task" + ) + + def get_prompt(self, sample): + # Format your input into a prompt + return f"Question: {sample['question']}\nAnswer:" + + def process_response(self, response, ref): + # Process model output and compare to reference + return response.strip() == ref.strip() +``` + +## Custom Metrics + +Domain-specific tasks often require specialized metrics. LightEval provides a flexible framework for creating custom metrics that capture domain-relevant aspects of performance: + +```python +from aenum import extend_enum +from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping +import numpy as np + +# Define a sample-level metric function +def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict: + """Example metric that returns multiple scores per sample""" + response = predictions[0] + return { + "accuracy": response == formatted_doc.choices[formatted_doc.gold_index], + "length_match": len(response) == len(formatted_doc.reference) + } + +# Create a metric that returns multiple values per sample +custom_metric_group = SampleLevelMetricGrouping( + metric_name=["accuracy", "length_match"], # Names of sub-metrics + higher_is_better={ # Whether higher values are better for each metric + "accuracy": True, + "length_match": True + }, + category=MetricCategory.CUSTOM, + use_case=MetricUseCase.SCORING, + sample_level_fn=custom_metric, + corpus_level_fn={ # How to aggregate each metric + "accuracy": np.mean, + "length_match": np.mean + } +) + +# Register the metric with LightEval +extend_enum(Metrics, "custom_metric_name", custom_metric_group) +``` + +For simpler cases where you only need one metric value per sample: + +```python +def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool: + """Example metric that returns a single score per sample""" + response = predictions[0] + return response == formatted_doc.choices[formatted_doc.gold_index] + +simple_metric_obj = SampleLevelMetric( + metric_name="simple_accuracy", + higher_is_better=True, + category=MetricCategory.CUSTOM, + use_case=MetricUseCase.SCORING, + sample_level_fn=simple_metric, + corpus_level_fn=np.mean # How to aggregate across samples +) + +extend_enum(Metrics, "simple_metric", simple_metric_obj) +``` + +You can then use your custom metrics in your evaluation tasks by referencing them in the task configuration. The metrics will be automatically computed across all samples and aggregated according to your specified functions. + +For more complex metrics, consider: +- Using metadata in your formatted documents to weight or adjust scores +- Implementing custom aggregation functions for corpus-level statistics +- Adding validation checks for your metric inputs +- Documenting edge cases and expected behavior + +For a complete example of custom metrics in action, see our [domain evaluation project](./project/README.md). + +## Dataset Creation + +High-quality evaluation requires carefully curated datasets. Consider these approaches for dataset creation: + +1. Expert Annotation: Work with domain experts to create and validate evaluation examples. Tools like [Argilla](https://github.com/argilla-io/argilla) make this process more efficient. + +2. Real-World Data: Collect and anonymize real usage data, ensuring it represents actual deployment scenarios. + +3. Synthetic Generation: Use LLMs to generate initial examples, then have experts validate and refine them. + +## Best Practices + +- Document your evaluation methodology thoroughly, including any assumptions or limitations +- Include diverse test cases that cover different aspects of your domain +- Consider both automated metrics and human evaluation where appropriate +- Version control your evaluation datasets and code +- Regularly update your evaluation suite as you discover new edge cases or requirements + +## References + +- [LightEval Custom Task Guide](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task) +- [LightEval Custom Metrics](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric) +- [Argilla Documentation](https://docs.argilla.io) for dataset annotation +- [Evaluation Guidebook](https://github.com/huggingface/evaluation-guidebook) for general evaluation principles + +# Next Steps + +⏩ For a complete example of implementing these concepts, see our [domain evaluation project](./project/README.md). \ No newline at end of file diff --git a/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb new file mode 100644 index 00000000..9597634b --- /dev/null +++ b/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb @@ -0,0 +1,19422 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "TZVw9f5QYWPL" + }, + "source": [ + "# lighteval is your AI evaluation library\n", + "\n", + "This notebook explores how you can use lighteval to evaluate and compare LLMs.\n", + "\n", + "`lighteval` has been around a while and it's a great tool for getting eval score on major benchmarks. It's just been refactored to support being used like a library in Python, which makes it great for comparing models across benchmarks.\n", + "\n", + "So let's dig in to some eval scores.\n", + "\n", + "
\n", + "

Exercise: Evaluate Your Own Model

\n", + "

Now that you've seen how to evaluate models on specific domains, try evaluating a model on a domain that interests you.

\n", + "

Difficulty Levels

\n", + "

🐢 Use the existing medical domain tasks but evaluate a different model from the Hugging Face hub

\n", + "

🐕 Create a new domain evaluation by selecting different MMLU tasks (e.g., computer science, mathematics, physics)

\n", + "

🦁 Create a custom evaluation task using LightEval's task framework and evaluate models on your specific domain

\n", + "
\n", + "\n", + "## Install dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "afW_CLJoPCnF", + "outputId": "91611cd5-51d6-4f73-9b3d-ac79c918d074" + }, + "outputs": [], + "source": [ + "!pip install -qqq -U \"torch<2.5\" \"torchvision<2.5\" \"torchaudio<2.5\" --index-url https://download.pytorch.org/whl/cu121\n", + "!pip list | grep torch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8xMnn_cQ1EEi", + "outputId": "bcaf8498-19c3-46af-9ae1-0306326f006b" + }, + "outputs": [], + "source": [ + "!pip install -qqq git+https://github.com/huggingface/lighteval.git tiktoken" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TDKs5ShvXw8K" + }, + "source": [ + "## Setup `lighteval` evaluation\n", + "\n", + "We need to setup the evaluation environment and pipeline. Much of this we will disable because we're keeping things in the notebook, but we could also use `push_to_hub` or `push_to_tensorboard`.\n", + "\n", + "### `push_to_hub`\n", + "\n", + "This is useful if we're evaluating a model and want to persist its evaluation with weights and configuration on the Hugging Face hub.\n", + "\n", + "### `push_to_tensorboard`\n", + "\n", + "This would be useful if we were building an evaluation tool or script, where we wanted to view results within tensorboard." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296, + "referenced_widgets": [ + "711ff69b059c421f84405dc798c84297", + "3c90ad18d29042caa880291056159985", + "66d9640217374065a9af74765929d326", + "5965b90591764931a635cc9305f2fa40", + "b34849662b994d87a912fdee127e1eed", + "1c358b2f428a4bb9bc0245b94d6394e8", + "9e9b42ee82614d56ab89f79bc2639d29", + "39db106760854445bc1d1c0a246400f9", + "737225a54b724c60ac0fb543b94bd39a", + "b6b2341d52504399840f08eb6d25bd84", + "4d137ae7c6e442f380e7764a741572d6", + "f837792155cc4e93884c80bd0bad7881", + "0ed8df4245654e928f9c9b584e02e203", + "2b426e2817f84313bbaf6c8f78ff3150", + "0ade751a6028417aa6f305217007bc64", + "5b2d53ff4a0a40f280757c5b396c2d88", + "b1201caf6e9d43a0b44f93307c248085", + "4fe67e045c8e4123beff0f1fb603cc0f", + "f292cff5aecd4010af6d20b8ba4d319d", + "4e4b570bb15b4f23b2340e5e2eb5622e", + "6b323ba03e1041299fb6a4e9d917027f", + "ceca2dcda90c444aa2e909e7f7109a53", + "f7c7eac52fde4cf5bd8c462765f0b8f9", + "d158043b045345b19594a903a43dbcf7", + "002f933919c148489c0667ac03f944f4", + "8ea9f11530f74a0e91a4200ba7f40ddb", + "c8db56b43c9342e392b86ddfac68f8f2", + "92d72302ec504f3c8f9546cef7723d75", + "169354e13fc8418ca386911da39511f8", + "61543cdfbeb147f4a75366d7bcde1fc0", + "986bbdd11ad94de194185530660a6953", + "17889c7b25df47b7b988c6b6b7b5c83a", + "186e4b1bdb844c769ed171b00a2bd66c", + "d2f11824da4a49eb9f4bc912247b2eb4", + "59f8e743cc7a48f9a7112962a7ec98c9", + "05bf3bda35b048869e34bed2225a34d4", + "8a0d497c293348678a64e22fb2f12d49", + "6c7d9ef6ea40439e9f0667f72e27e18d", + "f6a50efc22f54714af17113da7acd02b", + "1e2a39609cb34dc8b5f29c4d57a95746", + "22a4c40951984de0b0e8a71786119997", + "fa835fc797194aff91961cddad559e44", + "de11455c9560467b921f13a51c02b914", + "9d9d37d6faf04bfe9618a851c986daff", + "ec76a28c1d7a41b2926527ea45a4e574", + "051f5d56561f4ce49621fb8bb9d2a005", + "4a664ec8153b48458553d1c8757a7842", + "6c1692a29a814dc7b7d4b0333dd56627", + "10e14de256084f96957e2cb68034ce38", + "90e6bd13f39b4109922b2504dff7cd93", + "e551833c9d1640d98a698e82e17faffb", + "ecf5eca6b8d64175a39f9fb8d239f6fd", + "6ec6def8ee894aed8648d4d9464c64dd", + "09aef2e473464b85b7c5e2d124de83e5", + "3ebbb1cf8ac846e28d6a611c144abd02", + "47de67c7c71a4b09a010d6e5e7230a4c", + "be5933b10a074ade99632e5da6836820", + "863d305f19ef49b79ea62a27d94508cd", + "5ed5a979f7a54dfa9980717329a5c496", + "e82245d7d8c742ed8b8efe515750f6fc", + "be5fb5250a2e442cb767866442fdd4df", + "2ddf3728840144949e3b23521833c9ae", + "68cb76f44f5446d28fd4895a5cfbfee2", + "7d0f948b4ec7480abf9900370f698581", + "598f3d372159464181ea84e1bb5bcf86", + "18a28139ade541f2938042fff9ad4ec8" + ] + }, + "id": "3cUebd-z6IWs", + "outputId": "4b5ef2ce-04a0-4b2a-f3d7-5ecb7afb662e" + }, + "outputs": [], + "source": [ + "import lighteval\n", + "import os\n", + "from datetime import timedelta\n", + "from transformers import AutoModelForCausalLM\n", + "\n", + "from lighteval.logging.evaluation_tracker import EvaluationTracker\n", + "from lighteval.logging.hierarchical_logger import hlog_warn, htrack\n", + "from lighteval.models.model_config import create_model_config\n", + "from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters\n", + "\n", + "TOKEN = os.getenv(\"HF_TOKEN\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "muikmXNQXgFv" + }, + "outputs": [], + "source": [ + "env_config = EnvConfig(token=TOKEN, cache_dir=\"~/tmp\")\n", + "\n", + "evaluation_tracker = EvaluationTracker(\n", + " output_dir=\"~/tmp\",\n", + " save_details=False,\n", + " push_to_hub=False,\n", + " push_to_tensorboard=False,\n", + " public=False,\n", + " hub_results_org=False,\n", + ")\n", + "\n", + "pipeline_params = PipelineParameters(\n", + " launcher_type=ParallelismManager.ACCELERATE,\n", + " env_config=env_config,\n", + " job_id=1,\n", + " override_batch_size=1,\n", + " num_fewshot_seeds=0,\n", + " max_samples=10,\n", + " use_chat_template=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nsNjwzCtltkA" + }, + "source": [ + "# Compares models with `lighteval`\n", + "\n", + "We are going to compare two small LLMs on a domain. We will use `Qwen2.5-0.5B` and `SmolLM2-360M-Instruct` and we will evaluate them on a medical domain.\n", + "\n", + "We can create a domain evaluation from a subset of MMLU evaluations, by defining the evaluation tasks. In lighteval, tasks are described as strings.\n", + "\n", + "`{suite}|{task}:{subtask}|{num_few_shot}|{0 or 1 to reduce num_few_shot if prompt is too long}`\n", + "\n", + "Therefore, we will pass our list of medicine related tasks like this:\n", + "\n", + "```\n", + "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n", + "```\n", + "\n", + "Which can be translated to :\n", + "\n", + "| Suite | Task | Num Fewshot Example | Limit Fewshots |\n", + "|---|---|---|---|\n", + "| leaderboard | mmlu:anatomy | 5 | False |\n", + "| leaderboard | mmlu:professional_medicine | 5 | False |\n", + "| leaderboard | mmlu:high_school_biology | 5 | False |\n", + "| leaderboard | mmlu:high_school_chemistry | 5 | False |\n", + "\n", + "For a full list of lighteval supported tasks. Checkout this page in [the documentation](https://github.com/huggingface/lighteval/wiki/Available-Tasks)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qTqsizv9mVbO" + }, + "outputs": [], + "source": [ + "domain_tasks = \"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XwcJklSFX4H6" + }, + "source": [ + "# Evaluate Qwen2.5 0.5B" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "cc7ff121f1de4ebfa8f9ce73bfdb962e", + "47b6e922e9df42999f5bc7953c44e559", + "ffd2517a49d249baa6aac84bc8ed87d2", + "1903592fafb3497b87c8bff6c89d22b4", + "c2a9eb54064d476abb6000aad8d539c7", + "7b8aca3a7d134b15aad3b9f365042184", + "b89b4b5597e143d5be8db9bb41c50942", + "a9eec76a72ce4aefaf12b76268a56a2b", + "91ee892ccff34237b6a94da2a2961bc9", + "b1bcc93bbe244466a29e165879e3cebc", + "86de9c85391040cb96ce799a4d8d80f7", + "0c10c0f2c63a407cab9848ce14c74513", + "03e247ce7b634467acc9b366cf76d770", + "dcb62b29de2c461586b7cbce8e41d50b", + "f1ec2ad4ab1f4868961477525f3b0c38", + "10f200998209492dadd7a0890de3586e", + "95a20101d0364974a9372f6bd9ee567e", + "af5c0780b661430d8c757e4e54a473b4", + "f63a7cf741cf4d288238f11fd907552f", + "d44843515faa439fb37c68a2f9b4da9e", + "4fca7141116b477e88b84a8b4b7742ad", + "0252c1ade27f4466937f32450fb129ca", + "10cb0ba711764f52adf2c7a63a098099", + "8e31638ae77449eda54e05fafebd253a", + "fbeb24ecd3844295be2b5bfd20ded980", + "9fe08589b22d4406a9ff490f0937c4ba", + "e2e26b17c5b740b9964fc3e5a8bc72e1", + "76e0f1647dba4185a06ac5fe445c8aa8", + "db2e4915595f4b978825f74e9ee60168", + "3b8708128ac7414ca73c7a769e73f803", + "3c06ec3f3e1e48518780d87f484dac77", + "95d50b6b2a2142ad8a0f1f5ae805c514", + "164c716c78d648b589b522a85bf4dd64", + 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AutoModelForCausalLM.from_pretrained(\"HuggingFaceTB/SmolLM2-360M-Instruct\")\n", + "\n", + "pipeline = Pipeline(\n", + " tasks=domain_tasks,\n", + " pipeline_parameters=pipeline_params,\n", + " evaluation_tracker=evaluation_tracker,\n", + " model=smol_model\n", + ")\n", + "\n", + "pipeline.evaluate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pdjyzfKHVt52" + }, + "outputs": [], + "source": [ + "smol_results = pipeline.get_results()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eugvMFfgV1VD", + "outputId": "62956e3f-29c8-4493-f868-4ba63b7d8e96" + }, + "outputs": [], + "source": [ + "pipeline.show_results()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0HD8aFwSYGHu" + }, + "source": [ + "# Visualize Results\n", + "\n", + "Now that we have results from the two models we can visualize them side-by-side. We'll keep visualisation simple here, but with this data structure you could represent scores in many ways." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 + }, + "id": "1sReqrgQUO9r", + "outputId": "9a0779e1-c278-48ab-9ecc-5055028ea3fd" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.DataFrame.from_records(smol_results[\"results\"]).T[\"acc\"].rename(\"SmolLM2-360M-Instruct\")\n", + "_df = pd.DataFrame.from_records(qwen_results[\"results\"]).T[\"acc\"].rename(\"Qwen2-0.5B-DPO\")\n", + "df = pd.concat([df, _df], axis=1)\n", + "df.plot(kind=\"barh\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qJEbQeYDplKX" + }, + "source": [ + "# 💐 That's it!\n", + "\n", + "You have a handy notebook to view model evals. You could use this to:\n", + "\n", + "- select the right model for your inference use case\n", + "- evaluate checkpoints during training\n", + "- share model scores" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jWdS38syaipm" + }, + "source": [ + "🏃Next Steps\n", + "\n", + "- If you want to go deeper into your evaluation results check out this [notebook](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "A100", + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "002f933919c148489c0667ac03f944f4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + 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(For example, financial, legal, medical use cases) + +This tutorial shows you the full pipeline you can follow, from creating relevant data and annotating your samples to evaluating your model on them, with the easy to use [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel), and [lighteval](https://github.com/huggingface/lighteval). For our example, we'll focus on generating exam questions from multiple documents. + +## Project Structure + +For our process, we will follow 4 steps, with a script for each: generating a dataset, annotating it, extracting relevant samples for evaluation from it, and actually evaluating models. + +| Script Name | Description | +|-------------|-------------| +| generate_dataset.py | Generates exam questions from multiple text documents using a specified language model. | +| annotate_dataset.py | Creates an Argilla dataset for manual annotation of the generated exam questions. | +| create_dataset.py | Processes annotated data from Argilla and creates a Hugging Face dataset. | +| evaluation_task.py | Defines a custom LightEval task for evaluating language models on the exam questions dataset. | + +## Steps + +### 1. Generate Dataset + +The `generate_dataset.py` script uses the distilabel library to generate exam questions based on multiple text documents. It uses the specified model (default: Meta-Llama-3.1-8B-Instruct) to create questions, correct answers, and incrorect answers (known as distractors). You should add you own data samples and you might wish to use a different model. + +To run the generation: + +```sh +python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory +``` + +This will create a [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) containing the generated exam questions for all documents in the input directory. + +### 2. Annotate Dataset + +The `annotate_dataset.py` script takes the generated questions and creates an Argilla dataset for annotation. It sets up the dataset structure and populates it with the generated questions and answers, randomizing the order of answers to avoid bias. Once in Argilla, you or a domain expert can validate the dataset with the correct answers. + +You will see suggested correct answers from the LLM in random order and you can approve the correct answer or select a different one. The duration of this process will depend on the scale of your evaluation dataset, the complexity of your domain data, and the quality of your LLM. For example, we were able to create 150 samples within 1 hour on the domain of transfer learning, using Llama-3.1-70B-Instruct, mostly by approving the correct answer and discarding the incorrect ones. + +To run the annotation process: + +```sh +python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name +``` + +This will create an Argilla dataset that can be used for manual review and annotation. + +![argilla_dataset](./images/domain_eval_argilla_view.png) + +If you're not using Argilla, deploy it locally or on spaces following this [quickstart guide](https://docs.argilla.io/latest/getting_started/quickstart/). + +### 3. Create Dataset + +The `create_dataset.py` script processes the annotated data from Argilla and creates a Hugging Face dataset. It handles both suggested and manually annotated answers. The script will create a dataset with the question, possible answers, and the column name for the correct answer. To create the final dataset: + +```sh +huggingface_hub login +python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id +``` + +This will push the dataset to the Hugging Face Hub under the specified repository. You can view the sample dataset on the hub [here](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train), and a preview of the dataset looks like this: + +![hf_dataset](./images/domain_eval_dataset_viewer.png) + +### 4. Evaluation Task + +The `evaluation_task.py` script defines a custom LightEval task for evaluating language models on the exam questions dataset. It includes a prompt function, a custom accuracy metric, and the task configuration. + +To evaluate a model using lighteval with the custom exam questions task: + +```sh +lighteval accelerate \ + --model_args "pretrained=HuggingFaceH4/zephyr-7b-beta" \ + --tasks "community|exam_questions|0|0" \ + --custom_tasks domain-eval/evaluation_task.py \ + --output_dir "./evals" +``` + +You can find detailed guides in lighteval wiki about each of these steps: + +- [Creating a Custom Task](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task) +- [Creating a Custom Metric](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric) +- [Using existing metrics](https://github.com/huggingface/lighteval/wiki/Metric-List) + + diff --git a/vi/4_evaluation/project/annotate_dataset.py b/vi/4_evaluation/project/annotate_dataset.py new file mode 100644 index 00000000..dadffc10 --- /dev/null +++ b/vi/4_evaluation/project/annotate_dataset.py @@ -0,0 +1,129 @@ +import argparse +import json +from random import choices, sample + +import argilla as rg +from distilabel.distiset import Distiset + +################################################################################ +# Script Parameters +################################################################################ + +parser = argparse.ArgumentParser( + description="Annotate exam questions dataset using Argilla." +) +parser.add_argument( + "--argilla_api_key", + type=str, + default="argilla.apikey", + help="API key for Argilla", +) +parser.add_argument( + "--argilla_api_url", + type=str, + default="http://localhost:6900", + help="API URL for Argilla", +) +parser.add_argument( + "--dataset_path", + type=str, + default="exam_questions", + help="Path to the exam questions dataset", +) +parser.add_argument( + "--dataset_config", + type=str, + default="default", + help="Configuration of the dataset", +) +parser.add_argument( + "--dataset_split", + type=str, + default="train", + help="Split of the dataset to use", +) +parser.add_argument( + "--output_dataset_name", + type=str, + default="exam_questions", + help="Name of the output Argilla dataset", +) + +args = parser.parse_args() + +################################################################################ +# Create Argilla dataset with the feedback task for validation +################################################################################ + +client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url) + +if client.datasets(args.output_dataset_name): + print(f"Deleting existing dataset '{args.output_dataset_name}'") + client.datasets(args.output_dataset_name).delete() + +settings = rg.Settings( + fields=[ + rg.TextField("question"), + rg.TextField("answer_a"), + rg.TextField("answer_b"), + rg.TextField("answer_c"), + rg.TextField("answer_d"), + ], + questions=[ + rg.LabelQuestion( + name="correct_answer", + labels=["answer_a", "answer_b", "answer_c", "answer_d"], + ), + rg.TextQuestion( + name="improved_question", + description="Could you improve the question?", + ), + rg.TextQuestion( + name="improved_answer", + description="Could you improve the best answer?", + ), + ], +) + +dataset = rg.Dataset(settings=settings, name=args.output_dataset_name) +dataset.create() + +################################################################################ +# Load the Distiset and process and add records to Argilla dataset +# We will validate that questions appear in random order to avoid bias +# but we will show correct answers in the Argilla UI as suggestions. +################################################################################ + +distiset = Distiset.load_from_disk(args.dataset_path) +answer_names = ["answer_a", "answer_b", "answer_c", "answer_d"] +dataset_records = [] + +for exam in distiset[args.dataset_config][args.dataset_split]: + exam_json = json.loads(exam["generation"])["exam"] + + for question in exam_json: + answer = question["answer"] + distractors = question["distractors"] + distractors = choices(distractors, k=3) + answers = distractors + [answer] + answers = sample(answers, len(answers)) + suggestion_idx = answers.index(answer) + fields = dict(zip(answer_names, answers)) + fields["question"] = question["question"] + + record = rg.Record( + fields=fields, + suggestions=[ + rg.Suggestion( + question_name="correct_answer", + value=answer_names[suggestion_idx], + ) + ], + ) + dataset_records.append(record) + +dataset.records.log(dataset_records) + +print( + f"Dataset '{args.output_dataset_name}' has been created and populated in Argilla." +) diff --git a/vi/4_evaluation/project/create_dataset.py b/vi/4_evaluation/project/create_dataset.py new file mode 100644 index 00000000..822e268e --- /dev/null +++ b/vi/4_evaluation/project/create_dataset.py @@ -0,0 +1,72 @@ +import argparse + +import argilla as rg +from datasets import Dataset + +################################################################################ +# Script Parameters +################################################################################ + +parser = argparse.ArgumentParser( + description="Create a Hugging Face dataset from annotated Argilla data." +) +parser.add_argument( + "--argilla_api_key", + type=str, + default="argilla.apikey", + help="API key for Argilla", +) +parser.add_argument( + "--argilla_api_url", + type=str, + default="http://localhost:6900", + help="API URL for Argilla", +) +parser.add_argument( + "--dataset_path", + type=str, + default="exam_questions", + help="Path to the Argilla dataset", +) +parser.add_argument( + "--dataset_repo_id", + type=str, + default="burtenshaw/exam_questions", + help="Hugging Face dataset repository ID", +) + +args = parser.parse_args() + +################################################################################ +# Initialize Argilla client and load dataset +################################################################################ + +client = rg.Argilla(api_key=args.argilla_api_key, api_url=args.argilla_api_url) +dataset = client.datasets(args.dataset_path) + +################################################################################ +# Process Argilla records +################################################################################ + +dataset_rows = [] + +for record in dataset.records(with_suggestions=True, with_responses=True): + row = record.fields + + if len(record.responses) == 0: + answer = record.suggestions["correct_answer"].value + row["correct_answer"] = answer + else: + for response in record.responses: + if response.question_name == "correct_answer": + row["correct_answer"] = response.value + dataset_rows.append(row) + +################################################################################ +# Create Hugging Face dataset and push to Hub +################################################################################ + +hf_dataset = Dataset.from_list(dataset_rows) +hf_dataset.push_to_hub(repo_id=args.dataset_repo_id) + +print(f"Dataset has been successfully pushed to {args.dataset_repo_id}") diff --git a/vi/4_evaluation/project/evaluation_task.py b/vi/4_evaluation/project/evaluation_task.py new file mode 100644 index 00000000..397c75ad --- /dev/null +++ b/vi/4_evaluation/project/evaluation_task.py @@ -0,0 +1,86 @@ +import numpy as np + +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc +from lighteval.metrics.utils.metric_utils import ( + SampleLevelMetric, + MetricCategory, + MetricUseCase, +) + +################################################################################ +# Define the prompt function based on the structure of the dataset +################################################################################ + + +def prompt_fn(line, task_name: str = None): + """Converts a dataset line to a Doc object for evaluation.""" + instruction = "Choose the correct answer for the following exam question:" + return Doc( + task_name=task_name, + query=f"{instruction} {line['question']}", + choices=[ + f" {line['answer_a']}", + f" {line['answer_b']}", + f" {line['answer_c']}", + f" {line['answer_d']}", + ], + gold_index=["answer_a", "answer_b", "answer_c", "answer_d"].index( + line["correct_answer"] + ), + instruction=instruction, + ) + + +################################################################################ +# Define the custom metric based on guide here https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric +# Or use an existing metric based on the guide here: https://github.com/huggingface/lighteval/wiki/Metric-List +# Existing metrics can be imported from lighteval.metrics.metrics +################################################################################ + + +def sample_level_fn(formatted_doc: Doc, **kwargs) -> bool: + response = np.argmin(kwargs["choices_logprob"]) + return response == formatted_doc.gold_index + + +custom_metric = SampleLevelMetric( + metric_name="exam_question_accuracy", + higher_is_better=True, + category=MetricCategory.MULTICHOICE, + use_case=MetricUseCase.NONE, + sample_level_fn=sample_level_fn, + corpus_level_fn=np.mean, +) + +################################################################################ +# Define the task based on the prompt function and the custom metric +# Based on the guide here: https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task +################################################################################ + +task = LightevalTaskConfig( + name="example", + prompt_function=prompt_fn, + suite=["community"], + hf_repo="burtenshaw/exam_questions", + hf_subset="default", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + metric=[custom_metric], +) + +# Add the task to TASKS_TABLE +TASKS_TABLE = [task] + +# MODULE LOGIC +if __name__ == "__main__": + print([t.name for t in TASKS_TABLE]) + print(len(TASKS_TABLE)) + +# lighteval accelerate \ +# "pretrained=HuggingFaceTB/SmolLM2-135M-Instruct" \ +# "community|example|0|0" \ +# --custom-tasks "submitted_tasks/example.py" \ +# --output-dir "results" diff --git a/vi/4_evaluation/project/generate_dataset.py b/vi/4_evaluation/project/generate_dataset.py new file mode 100644 index 00000000..cb531d3f --- /dev/null +++ b/vi/4_evaluation/project/generate_dataset.py @@ -0,0 +1,167 @@ +import argparse +import os +from pydantic import BaseModel, Field +from datasets import Dataset + +from distilabel.llms import InferenceEndpointsLLM +from distilabel.pipeline import Pipeline +from distilabel.steps.tasks import TextGeneration + + +################################################################################ +# Script Parameters +################################################################################ + +parser = argparse.ArgumentParser( + description="Generate exam questions from text files in a directory." +) +parser.add_argument( + "--model_id", + type=str, + default="Qwen/Qwen2.5-7B-Instruct", + help="Model ID for text generation", +) +parser.add_argument( + "--tokenizer_id", + type=str, + default="Qwen/Qwen2.5-7B-Instruct", + help="Tokenizer ID for text generation", +) +parser.add_argument( + "--input_dir", + type=str, + help="Directory containing input text files", + default="data", +) +parser.add_argument( + "--max_new_tokens", + type=int, + default=2048, + help="Maximum number of new tokens to generate", +) +parser.add_argument( + "--output_path", + type=str, + default="exam_questions_output", + help="Directory to save the generated datasets", +) + +args = parser.parse_args() + +################################################################################ +# Load the documents +# We assume that the documents are in the input directory, and that each file +# is a separate document about the same topic. +################################################################################ + +# Process all text files in the input directory +documents = [] +for filename in os.listdir(args.input_dir): + if filename.endswith(".txt"): + file_path = os.path.join(args.input_dir, filename) + with open(file=file_path, mode="r", encoding="utf-8") as file: + document_content = file.read() + documents.append(document_content) + +# Create a single dataset from all document contents +dataset = Dataset.from_dict({"document": documents}) + +################################################################################ +# Define the prompts +# We use a system prompt to guide the model to generate the correct output format. +# A template is used to insert the document into the prompt. +################################################################################ + +SYSTEM_PROMPT = """\ +You are an exam writer specialized in writing exams for students. +Your goal is to create questions and answers based on the document provided, +and a list of distractors, that are incorrect but viable answers to the question. +Your answer must adhere to the following format: +``` +[ + { + "question": "Your question", + "answer": "The correct answer to the question", + "distractors": ["wrong answer 1", "wrong answer 2", "wrong answer 3"] + }, + ... (more questions and answers as required) +] +``` +""".strip() + +INSTRUCTION_TEMPLATE = """\ + Generate a list of answers and questions about the document. + Document:\n\n{{ instruction }}""" + +################################################################################ +# Define the output structure +# We define a data model for the output of the pipeline, this is used to ensure +# that the output is in the correct format for the evaluation task. +################################################################################ + + +class ExamQuestion(BaseModel): + question: str = Field(..., description="The question to be answered") + answer: str = Field(..., description="The correct answer to the question") + distractors: List[str] = Field( + ..., description="A list of incorrect but viable answers to the question" + ) + + +class ExamQuestions(BaseModel): + exam: List[ExamQuestion] + + +################################################################################ +# Create the pipeline +# We create a pipeline with a single task that generates the exam questions +# based on the document and in the correct format. We will Hugging Face +# InferenceEndpoints and the model specified in the arguments. +################################################################################ + +with Pipeline( + name="Domain-Eval-Questions", + description="Generate exam questions based on given documents.", +) as pipeline: + # Set up the text generation task + text_generation = TextGeneration( + name="exam_generation", + llm=InferenceEndpointsLLM( + model_id=args.model_id, + tokenizer_id=args.model_id, + api_key=os.environ["HF_TOKEN"], + structured_output={ + "schema": ExamQuestions.model_json_schema(), + "format": "json", + }, + ), + input_batch_size=8, + output_mappings={"model_name": "generation_model"}, + input_mappings={"instruction": "document"}, + system_prompt=SYSTEM_PROMPT, + template=INSTRUCTION_TEMPLATE, + ) + + +################################################################################ +# Run the pipeline +# We run the pipeline for all documents and save the results to the output path. 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zQ~Hw&g6x>P&?;$EwzMDhx$Gv}2pz`#n4bM9QO8S{9qdt7fusYmCeQNbaxWg${}2Xz zs29@qV7;Z{ZMpSG+Y0kF4Rt@^3VZ{6wZLcK4aud$+l~;Le5=k@v1dxU%y_c9g;{WB zwKg4%%bcG?WvNkaz~=_HqH?-LJBB4*k78{>xF;sQyrQQA^KLuL_5C;qN9{-a(mFm{ zBh-`>>1u9Bqle0VF`WWsD9!jUjvCE6Vi5K-p}p#1SjD7|L~S8jux;9m4^LywzL!@6 z*e%5pbSS^mYO=En2B~|jN}0k$ko^NMi*sXk$ZBfT+mx(u(JF{XxtUh*9wH7v=$F#I zExviA^2BCzg~m55_)8a}_EsVYCgq&En~UyXQq%L0q8cU1xTUE7p}7Acy6<*u;AqA- z5PqjCCAojfw>}EKxr;o16{jEp8&za@4~PW>YoqoV4a{d-qIY5AuYxKGfX&S`cn@U< z1m{(NC7jpdOYZpgn+8!!ZVI`(F(&<4y%noMkUEPB3Ud>Dki&8;qorL1(C^+gd6`9o z==YyEpeADuyg(tkB}YU?TfJzR3KGtdg literal 0 HcmV?d00001 From 5d7fa1388b17fae5abd14b585900bababe78db86 Mon Sep 17 00:00:00 2001 From: hahuyhoang411 Date: Sun, 22 Dec 2024 00:07:33 +0100 Subject: [PATCH 3/7] typo --- vi/4_evaluation/README.md | 2 +- vi/README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/vi/4_evaluation/README.md b/vi/4_evaluation/README.md index 525723c6..04aedd6a 100644 --- a/vi/4_evaluation/README.md +++ b/vi/4_evaluation/README.md @@ -26,7 +26,7 @@ Theo dõi một ví dụ hoàn chỉnh về việc xây dựng quy trình đánh |---------|-------|---------|------|-------| | Đánh giá và phân tích mô hình LLM của bạn | Học cách sử dụng LightEval để đánh giá và so sánh các mô hình trên các lĩnh vực cụ thể | 🐢 Sử dụng các tác vụ trong lĩnh vực y tế để đánh giá mô hình
🐕 Tạo đánh giá lĩnh vực mới với các tác vụ MMLU khác nhau
🦁 Tạo tác vụ đánh giá tùy chỉnh cho lĩnh vực của bạn | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | Open In Colab | -## Tài nguyên +## Tài liệu tham khảo - [Hướng dẫn đánh giá](https://github.com/huggingface/evaluation-guidebook) - Hướng dẫn toàn diện về đánh giá LLM - [Tài liệu LightEval](https://github.com/huggingface/lighteval) - Tài liệu chính thức cho thư viện LightEval diff --git a/vi/README.md b/vi/README.md index ce109d5e..77913375 100644 --- a/vi/README.md +++ b/vi/README.md @@ -88,7 +88,7 @@ Nếu dùng **Google Colab** bạn sẽ cần cài đặt dependencies một cá pip install -r transformers trl datasets huggingface_hub ``` -## Tham gia +## Tham gia và chia sẻ Hãy chia sẻ khoá học này, để cùng nhau phát triển cộng đồng AI Việt Nam. From a933c4dc95c27fdbb958e5b3767e62bb73078114 Mon Sep 17 00:00:00 2001 From: hahuyhoang411 Date: Sun, 22 Dec 2024 00:09:26 +0100 Subject: [PATCH 4/7] init --- vi/4_evaluation/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/vi/4_evaluation/README.md b/vi/4_evaluation/README.md index 04aedd6a..27b796da 100644 --- a/vi/4_evaluation/README.md +++ b/vi/4_evaluation/README.md @@ -24,7 +24,7 @@ Theo dõi một ví dụ hoàn chỉnh về việc xây dựng quy trình đánh | Tiêu đề | Mô tả | Bài tập | Link | Colab | |---------|-------|---------|------|-------| -| Đánh giá và phân tích mô hình LLM của bạn | Học cách sử dụng LightEval để đánh giá và so sánh các mô hình trên các lĩnh vực cụ thể | 🐢 Sử dụng các tác vụ trong lĩnh vực y tế để đánh giá mô hình
🐕 Tạo đánh giá lĩnh vực mới với các tác vụ MMLU khác nhau
🦁 Tạo tác vụ đánh giá tùy chỉnh cho lĩnh vực của bạn | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | Open In Colab | +| Đánh giá và phân tích mô hình LLM của bạn | Học cách sử dụng `LightEval` để đánh giá và so sánh các mô hình trên các lĩnh vực cụ thể | 🐢 Sử dụng các tác vụ trong **lĩnh vực y tế** để đánh giá mô hình
🐕 Tạo đánh giá lĩnh vực mới với các tác vụ MMLU khác nhau
🦁 Tạo tác vụ đánh giá cho lĩnh vực của bạn | [Notebook](./notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb) | Open In Colab | ## Tài liệu tham khảo @@ -32,6 +32,6 @@ Theo dõi một ví dụ hoàn chỉnh về việc xây dựng quy trình đánh - [Tài liệu LightEval](https://github.com/huggingface/lighteval) - Tài liệu chính thức cho thư viện LightEval - [Tài liệu Argilla](https://docs.argilla.io) - Tìm hiểu về nền tảng gán nhãn Argilla - [Paper MMLU](https://arxiv.org/abs/2009.03300) - Paper mô tả benchmark MMLU -- [Tạo tác vụ tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task) -- [Tạo số liệu tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric) +- [Tạo tác vụ theo yêu cầu](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task) +- [Tạo tiêu chuẩn đánh giá theo yêu cầu](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric) - [Sử dụng số liệu có sẵn](https://github.com/huggingface/lighteval/wiki/Metric-List) \ No newline at end of file From 0ea6dcf15ee63ef62e9a9fd40411ee9f9cd307d1 Mon Sep 17 00:00:00 2001 From: hahuyhoang411 Date: Sun, 22 Dec 2024 01:21:00 +0100 Subject: [PATCH 5/7] init --- vi/4_evaluation/automatic_benchmarks.md | 112 ++++++++++++------------ 1 file changed, 56 insertions(+), 56 deletions(-) diff --git a/vi/4_evaluation/automatic_benchmarks.md b/vi/4_evaluation/automatic_benchmarks.md index 6a980fa4..b5852e9c 100644 --- a/vi/4_evaluation/automatic_benchmarks.md +++ b/vi/4_evaluation/automatic_benchmarks.md @@ -1,82 +1,82 @@ -# Automatic Benchmarks +# Đánh giá tự động -Automatic benchmarks serve as standardized tools for evaluating language models across different tasks and capabilities. While they provide a useful starting point for understanding model performance, it's important to recognize that they represent only one piece of a comprehensive evaluation strategy. +Đánh giá tự động là các công cụ chuẩn hoá để đánh giá các mô hình ngôn ngữ qua các tác vụ và khả năng khác nhau. Mặc dù chúng cung cấp điểm khởi đầu hữu ích để hiểu hiệu năng của mô hình, điều quan trọng là phải nhận ra rằng chúng chỉ là một phần trong toàn bộ khả năng của mô hình. -## Understanding Automatic Benchmarks +## Hiểu về đánh giá tự động -Automatic benchmarks typically consist of curated datasets with predefined tasks and evaluation metrics. These benchmarks aim to assess various aspects of model capability, from basic language understanding to complex reasoning. The key advantage of using automatic benchmarks is their standardization - they allow for consistent comparison across different models and provide reproducible results. +Đánh giá tự động thường bao gồm các tập dữ liệu được tuyển chọn với các tác vụ và tiêu chí đánh giá được định nghĩa trước. Những đánh giá này nhằm đánh giá nhiều khía cạnh về khả năng của mô hình, từ hiểu ngôn ngữ cơ bản đến suy luận phức tạp. Lợi thế chính của việc sử dụng đánh giá tự động là tính chuẩn hoá - chúng cho phép so sánh nhất quán giữa các mô hình khác nhau và cung cấp kết quả có thể tái tạo lại. -However, it's crucial to understand that benchmark performance doesn't always translate directly to real-world effectiveness. A model that excels at academic benchmarks may still struggle with specific domain applications or practical use cases. +Tuy nhiên, điều quan trọng là phải hiểu rằng điểm số trên các bài đánh giá không phải lúc nào cũng thể hiện chính xác hiệu quả của mô hình trong thực tế. Một mô hình xuất sắc trong các bài đánh giá học thuật (academic benchmark) vẫn có thể gặp khó khăn với các ứng dụng vào lĩnh vực cụ thể hoặc các trường hợp sử dụng thực tế. -## Benchmarks and Their Limitations +## Các bài đánh giá và hạn chế của chúng -### General Knowledge Benchmarks +### Đánh giá kiến thức tổng quát -MMLU (Massive Multitask Language Understanding) tests knowledge across 57 subjects, from science to humanities. While comprehensive, it may not reflect the depth of expertise needed for specific domains. TruthfulQA evaluates a model's tendency to reproduce common misconceptions, though it can't capture all forms of misinformation. +`MMLU` (Massive Multitask Language Understanding) là bài đánh giá dùng để kiểm tra kiến thức trên *57 môn học*, từ khoa học tự nhiên đến khoa học xã hội. Mặc dù toàn diện, nó có thể không phản ánh độ sâu của chuyên môn cần thiết cho các lĩnh vực cụ thể. `TruthfulQA` đánh giá xu hướng mô hình tái tạo các quan niệm sai lầm phổ biến, mặc dù nó không thể nắm bắt tất cả các hình thức thông tin sai lệch. -### Reasoning Benchmarks -BBH (Big Bench Hard) and GSM8K focus on complex reasoning tasks. BBH tests logical thinking and planning, while GSM8K specifically targets mathematical problem-solving. These benchmarks help assess analytical capabilities but may not capture the nuanced reasoning required in real-world scenarios. +### Đánh giá khả năng suy luận +`BBH` (Big Bench Hard) và `GSM8K` tập trung vào các tác vụ suy luận phức tạp. `BBH` kiểm tra tư duy logic và lập kế hoạch, trong khi `GSM8K` nhắm vào giải quyết vấn đề toán học. Những bài đánh giá này giúp kiểm tra khả năng phân tích nhưng có thể không nắm bắt được khả năng suy luận tinh tế cần thiết trong các tình huống thực tế. -### Language Understanding -HELM provides a holistic evaluation framework, while WinoGrande tests common sense through pronoun disambiguation. These benchmarks offer insights into language processing capabilities but may not fully represent the complexity of natural conversation or domain-specific terminology. +### Đánh giá khả năng ngôn ngữ +`HELM` cung cấp một đánh giá toàn diện, trong khi `WinoGrande` kiểm tra hiểu biết thông thường thông qua việc giải nghĩa các đại từ. Những bài đánh giá này cung cấp cái nhìn sâu sắc về khả năng xử lý ngôn ngữ nhưng có thể không hoàn toàn đại diện cho độ phức tạp của giao tiếp tự nhiên hoặc thuật ngữ theo lĩnh vực cụ thể. -## Alternative Evaluation Approaches +## Các phương pháp đánh giá thay thế -Many organizations have developed alternative evaluation methods to address the limitations of standard benchmarks: +Nhiều tổ chức đã phát triển các phương pháp đánh giá thay thế để khắc phục những hạn chế của bài đánh giá tiêu chuẩn: -### LLM-as-Judge -Using one language model to evaluate another's outputs has become increasingly popular. This approach can provide more nuanced feedback than traditional metrics, though it comes with its own biases and limitations. +### LLM trong vài trò người đánh giá +Sử dụng một mô hình ngôn ngữ để đánh giá đầu ra của mô hình khác ngày càng phổ biến. Phương pháp này có thể cung cấp phản hồi chi tiết hơn các bài đánh giá truyền thống, mặc dù nó đi kèm với những định kiến (bias) của mô hình đánh giá và hạn chế riêng. -### Evaluation Arenas -Platforms like Anthropic's Constitutional AI Arena allow models to interact and evaluate each other in controlled environments. This can reveal strengths and weaknesses that might not be apparent in traditional benchmarks. +### Môi trường đánh giá +Các nền tảng như Constitutional AI Arena của Anthropic cho phép các mô hình tương tác và đánh giá lẫn nhau trong môi trường được kiểm soát. Điều này có thể cho thấy những điểm mạnh và điểm yếu có thể không rõ ràng trong các benchmark truyền thống. -### Custom Benchmark Suites -Organizations often develop internal benchmark suites tailored to their specific needs and use cases. These might include domain-specific knowledge tests or evaluation scenarios that mirror actual deployment conditions. +### Bộ đánh giá tuỳ chỉnh +Các tổ chức thường phát triển bộ benchmark nội bộ được điều chỉnh cho nhu cầu và trường hợp sử dụng cụ thể của họ. Những bộ này có thể bao gồm kiểm tra kiến thức theo lĩnh vực cụ thể hoặc các kịch bản đánh giá phản ánh điều kiện triển khai thực tế. -## Creating Your Own Evaluation Strategy +## Tạo chiến lược đánh giá riêng -Remember that while LightEval makes it easy to run standard benchmarks, you should also invest time in developing evaluation methods specific to your use case. +Hãy nhớ rằng mặc dù LightEval giúp dễ dàng chạy các benchmark tiêu chuẩn, bạn cũng nên dành thời gian phát triển phương pháp đánh giá phù hợp với trường hợp sử dụng của mình. -While standard benchmarks provide a useful baseline, they shouldn't be your only evaluation method. Here's how to develop a more comprehensive approach: +Mặc dù benchmark tiêu chuẩn cung cấp một đường cơ sở hữu ích, chúng không nên là phương pháp đánh giá duy nhất của bạn. Dưới đây là cách phát triển một phương pháp toàn diện hơn: -1. Start with relevant standard benchmarks to establish a baseline and enable comparison with other models. +1. Bắt đầu với các benchmark tiêu chuẩn liên quan để thiết lập đường cơ sở và cho phép so sánh với các mô hình khác. -2. Identify the specific requirements and challenges of your use case. What tasks will your model actually perform? What kinds of errors would be most problematic? +2. Xác định các yêu cầu và thách thức cụ thể của trường hợp sử dụng của bạn. Mô hình của bạn sẽ thực hiện những tác vụ gì? Những loại lỗi nào sẽ gây rắc rối nhất? -3. Develop custom evaluation datasets that reflect your actual use case. This might include: - - Real user queries from your domain - - Common edge cases you've encountered - - Examples of particularly challenging scenarios +3. Phát triển tập dữ liệu đánh giá tuỳ chỉnh phản ánh trường hợp sử dụng thực tế của bạn. Điều này có thể bao gồm: + - Các câu hỏi thực tế từ người dùng trong lĩnh vực của bạn + - Các trường hợp ngoại lệ phổ biến bạn đã gặp + - Các ví dụ về tình huống đặc biệt khó khăn -4. Consider implementing a multi-layered evaluation strategy: - - Automated metrics for quick feedback - - Human evaluation for nuanced understanding - - Domain expert review for specialized applications - - A/B testing in controlled environments +4. Xem xét triển khai chiến lược đánh giá nhiều lớp: + - Số liệu tự động để nhận phản hồi nhanh + - Đánh giá bởi con người để hiểu sâu sắc hơn + - Đánh giá của chuyên gia lĩnh vực cho các ứng dụng chuyên biệt + - Kiểm tra A/B trong môi trường được kiểm soát -## Using LightEval for Benchmarking +## Sử dụng LightEval để đánh giá -LightEval tasks are defined using a specific format: +Các tác vụ LightEval được định nghĩa theo định dạng cụ thể: ``` {suite}|{task}|{num_few_shot}|{auto_reduce} ``` -- **suite**: The benchmark suite (e.g., 'mmlu', 'truthfulqa') -- **task**: Specific task within the suite (e.g., 'abstract_algebra') -- **num_few_shot**: Number of examples to include in prompt (0 for zero-shot) -- **auto_reduce**: Whether to automatically reduce few-shot examples if prompt is too long (0 or 1) +- **suite**: Bộ benchmark (ví dụ: 'mmlu', 'truthfulqa') +- **task**: Tác vụ cụ thể trong bộ (ví dụ: 'abstract_algebra') +- **num_few_shot**: Số lượng ví dụ để đưa vào prompt (0 cho zero-shot) +- **auto_reduce**: Có tự động giảm ví dụ few-shot nếu prompt quá dài hay không (0 hoặc 1) -Example: `"mmlu|abstract_algebra|0|0"` evaluates on MMLU's abstract algebra task with zero-shot inference. +Ví dụ: `"mmlu|abstract_algebra|0|0"` đánh giá tác vụ đại số trừu tượng của MMLU với suy luận zero-shot. -### Example Evaluation Pipeline +### Ví dụ về Pipeline đánh giá -Here's a complete example of setting up and running an evaluation on automatic benchmarks relevant to one specific domain: +Đây là một ví dụ hoàn chỉnh về việc thiết lập và chạy đánh giá trên các benchmark tự động liên quan đến một lĩnh vực cụ thể: ```python from lighteval.tasks import Task, Pipeline from transformers import AutoModelForCausalLM -# Define tasks to evaluate +# Định nghĩa các tác vụ để đánh giá domain_tasks = [ "mmlu|anatomy|0|0", "mmlu|high_school_biology|0|0", @@ -84,20 +84,20 @@ domain_tasks = [ "mmlu|professional_medicine|0|0" ] -# Configure pipeline parameters +# Cấu hình tham số pipeline pipeline_params = { - "max_samples": 40, # Number of samples to evaluate - "batch_size": 1, # Batch size for inference - "num_workers": 4 # Number of worker processes + "max_samples": 40, # Số lượng mẫu để đánh giá + "batch_size": 1, # Kích thước batch cho inference + "num_workers": 4 # Số lượng worker process } -# Create evaluation tracker +# Tạo evaluation tracker evaluation_tracker = EvaluationTracker( output_path="./results", save_generations=True ) -# Load model and create pipeline +# Tải mô hình và tạo pipeline model = AutoModelForCausalLM.from_pretrained("your-model-name") pipeline = Pipeline( tasks=domain_tasks, @@ -106,15 +106,15 @@ pipeline = Pipeline( model=model ) -# Run evaluation +# Chạy đánh giá pipeline.evaluate() -# Get and display results +# Lấy và hiển thị kết quả results = pipeline.get_results() pipeline.show_results() ``` -Results are displayed in a tabular format showing: +Kết quả được hiển thị dưới dạng bảng thể hiện: ``` | Task |Version|Metric|Value | |Stderr| |----------------------------------------|------:|------|-----:|---|-----:| @@ -124,8 +124,8 @@ Results are displayed in a tabular format showing: |leaderboard:mmlu:high_school_biology:5 | 0|acc |0.1500|± |0.0819| ``` -You can also handle the results in a pandas DataFrame and visualise or represent them as you want. +Bạn cũng có thể xử lý kết quả trong pandas DataFrame và trực quan hoá hoặc biểu diễn chúng theo cách bạn muốn. -# Next Steps +# Bước tiếp theo -⏩ Explore [Custom Domain Evaluation](./custom_evaluation.md) to learn how to create evaluation pipelines tailored to your specific needs +⏩ Khám phá [Đánh giá theo lĩnh vực tuỳ chỉnh](./custom_evaluation.md) để học cách tạo pipeline đánh giá phù hợp với nhu cầu cụ thể của bạn \ No newline at end of file From 30a81a08baa3160dd072e59da32c56f2cf538ef3 Mon Sep 17 00:00:00 2001 From: hahuyhoang411 Date: Wed, 1 Jan 2025 23:10:59 +0100 Subject: [PATCH 6/7] init --- vi/4_evaluation/custom_evaluation.md | 97 ++++++++++++++-------------- 1 file changed, 49 insertions(+), 48 deletions(-) diff --git a/vi/4_evaluation/custom_evaluation.md b/vi/4_evaluation/custom_evaluation.md index c82868a7..3461cb48 100644 --- a/vi/4_evaluation/custom_evaluation.md +++ b/vi/4_evaluation/custom_evaluation.md @@ -1,16 +1,16 @@ -# Custom Domain Evaluation +# Đánh Giá Theo Lĩnh Vực Cụ Thể -While standard benchmarks provide valuable insights, many applications require specialized evaluation approaches tailored to specific domains or use cases. This guide will help you create custom evaluation pipelines that accurately assess your model's performance in your target domain. +Mặc dù các phương pháp đánh giá tiêu chuẩn (`benchmark`) cung cấp những thông tin chuyên sâu có giá trị, nhiều ứng dụng đòi hỏi các phương pháp đánh giá chuyên biệt phù hợp với các lĩnh vực hoặc trường hợp sử dụng cụ thể. Bài học này sẽ giúp bạn tạo các quy trình (`pipeline`) tùy chỉnh các phương pháp đánh giá để có cái nhìn chính xác hơn về hiệu suất của mô hình trong lĩnh vực cụ thể của bạn. -## Designing Your Evaluation Strategy +## Thiết kế chiến lược đánh giá của bạn -A successful custom evaluation strategy starts with clear objectives. Consider what specific capabilities your model needs to demonstrate in your domain. This might include technical knowledge, reasoning patterns, or domain-specific formats. Document these requirements carefully - they'll guide both your task design and metric selection. +Một chiến lược đánh giá tùy chỉnh đầy đủ bắt đầu với các mục tiêu rõ ràng. Hãy xem xét những khả năng cụ thể mà mô hình của bạn cần thể hiện trong lĩnh vực của bạn. Điều này có thể bao gồm kiến thức kỹ thuật, các mẫu suy luận hoặc các định dạng đặc thù cho lĩnh vực đó. Ghi lại cẩn thận các yêu cầu này - chúng sẽ hướng dẫn cả việc thiết kế tác vụ (`task`) và lựa chọn các chỉ số (`metric`). -Your evaluation should test both standard use cases and edge cases. For example, in a medical domain, you might evaluate both common diagnostic scenarios and rare conditions. In financial applications, you might test both routine transactions and complex edge cases involving multiple currencies or special conditions. +Việc đánh giá của bạn nên kiểm tra cả các trường hợp sử dụng tiêu chuẩn và các trường hợp biên (`edge case`). Ví dụ, trong lĩnh vực y tế, bạn có thể đánh giá cả các kịch bản chẩn đoán phổ biến và các tình trạng hiếm gặp. Trong các ứng dụng tài chính, bạn có thể kiểm tra cả các giao dịch thông thường và các trường hợp biên phức tạp liên quan đến nhiều loại tiền tệ hoặc các điều kiện đặc biệt. -## Implementation with LightEval +## Triển khai với LightEval -LightEval provides a flexible framework for implementing custom evaluations. Here's how to create a custom task: +`LightEval` cung cấp một khung (`framework`) linh hoạt để triển khai các đánh giá tùy chỉnh. Đây là cách để tạo một tác vụ (`task`) tùy chỉnh: ```python from lighteval.tasks import Task, Doc @@ -21,62 +21,63 @@ class CustomEvalTask(Task): super().__init__( name="custom_task", version="0.0.1", - metrics=["accuracy", "f1"], # Your chosen metrics - description="Description of your custom evaluation task" + metrics=["accuracy", "f1"], # Các chỉ số bạn chọn + description="Mô tả tác vụ đánh giá tùy chỉnh của bạn" ) def get_prompt(self, sample): - # Format your input into a prompt + # Định dạng đầu vào của bạn thành một lời nhắc (prompt) return f"Question: {sample['question']}\nAnswer:" def process_response(self, response, ref): - # Process model output and compare to reference + # Xử lý đầu ra của mô hình và so sánh với tham chiếu return response.strip() == ref.strip() ``` -## Custom Metrics -Domain-specific tasks often require specialized metrics. LightEval provides a flexible framework for creating custom metrics that capture domain-relevant aspects of performance: +## Các chỉ số (`metric`) tùy chỉnh + +Các tác vụ theo lĩnh vực cụ thể thường yêu cầu các chỉ số chuyên biệt. `LightEval` cung cấp một khung linh hoạt để tạo các chỉ số tùy chỉnh nắm bắt các khía cạnh liên quan đến lĩnh vực của hiệu suất: ```python from aenum import extend_enum from lighteval.metrics import Metrics, SampleLevelMetric, SampleLevelMetricGrouping import numpy as np -# Define a sample-level metric function +# Định nghĩa một hàm chỉ số mức mẫu (sample-level metric) def custom_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> dict: - """Example metric that returns multiple scores per sample""" + """Chỉ số ví dụ trả về nhiều điểm số cho mỗi mẫu""" response = predictions[0] return { "accuracy": response == formatted_doc.choices[formatted_doc.gold_index], "length_match": len(response) == len(formatted_doc.reference) } -# Create a metric that returns multiple values per sample +# Tạo một chỉ số trả về nhiều giá trị cho mỗi mẫu custom_metric_group = SampleLevelMetricGrouping( - metric_name=["accuracy", "length_match"], # Names of sub-metrics - higher_is_better={ # Whether higher values are better for each metric + metric_name=["accuracy", "length_match"], # Tên của các chỉ số + higher_is_better={ # Liệu giá trị cao hơn có tốt hơn cho mỗi chỉ số không "accuracy": True, "length_match": True }, category=MetricCategory.CUSTOM, use_case=MetricUseCase.SCORING, sample_level_fn=custom_metric, - corpus_level_fn={ # How to aggregate each metric + corpus_level_fn={ # Cách tổng hợp từng chỉ số "accuracy": np.mean, "length_match": np.mean } ) -# Register the metric with LightEval +# Đăng ký chỉ số vào LightEval extend_enum(Metrics, "custom_metric_name", custom_metric_group) ``` -For simpler cases where you only need one metric value per sample: +Đối với các trường hợp đơn giản hơn, nơi bạn chỉ cần một giá trị chỉ số cho mỗi mẫu: ```python def simple_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool: - """Example metric that returns a single score per sample""" + """Chỉ số ví dụ trả về một điểm duy nhất cho mỗi mẫu""" response = predictions[0] return response == formatted_doc.choices[formatted_doc.gold_index] @@ -86,47 +87,47 @@ simple_metric_obj = SampleLevelMetric( category=MetricCategory.CUSTOM, use_case=MetricUseCase.SCORING, sample_level_fn=simple_metric, - corpus_level_fn=np.mean # How to aggregate across samples + corpus_level_fn=np.mean # Cách tổng hợp trên các mẫu ) extend_enum(Metrics, "simple_metric", simple_metric_obj) ``` -You can then use your custom metrics in your evaluation tasks by referencing them in the task configuration. The metrics will be automatically computed across all samples and aggregated according to your specified functions. +Sau đó, bạn có thể sử dụng các chỉ số tùy chỉnh của mình trong các tác vụ đánh giá bằng cách tham chiếu chúng trong cấu hình tác vụ. Các chỉ số sẽ được tự động tính toán trên tất cả các mẫu và tổng hợp theo các hàm bạn đã chỉ định. -For more complex metrics, consider: -- Using metadata in your formatted documents to weight or adjust scores -- Implementing custom aggregation functions for corpus-level statistics -- Adding validation checks for your metric inputs -- Documenting edge cases and expected behavior +Đối với các chỉ số phức tạp hơn, hãy xem xét: +- Sử dụng siêu dữ liệu (`metadata`) trong các tài liệu được định dạng của bạn để đánh trọng số hoặc điều chỉnh điểm số +- Triển khai các hàm tổng hợp tùy chỉnh cho các thống kê cấp độ kho ngữ liệu (`corpus-level`) +- Thêm kiểm tra xác thực cho đầu vào chỉ số của bạn +- Ghi lại các trường hợp biên và hành vi mong đợi -For a complete example of custom metrics in action, see our [domain evaluation project](./project/README.md). +Để có một ví dụ đầy đủ về các chỉ số tùy chỉnh trong thực tế, hãy xem [dự án đánh giá theo lĩnh vực](./project/README.md) của chúng tôi. -## Dataset Creation +## Tạo tập dữ liệu -High-quality evaluation requires carefully curated datasets. Consider these approaches for dataset creation: +Đánh giá chất lượng cao đòi hỏi các tập dữ liệu được sắp xếp cẩn thận. Hãy xem xét các phương pháp sau để tạo tập dữ liệu: -1. Expert Annotation: Work with domain experts to create and validate evaluation examples. Tools like [Argilla](https://github.com/argilla-io/argilla) make this process more efficient. +1. Chú thích của chuyên gia: Làm việc với các chuyên gia trong lĩnh vực để tạo và xác thực các ví dụ đánh giá. Các công cụ như [Argilla](https://github.com/argilla-io/argilla) làm cho quá trình này hiệu quả hơn. -2. Real-World Data: Collect and anonymize real usage data, ensuring it represents actual deployment scenarios. +2. Dữ liệu thế giới thực: Thu thập và ẩn danh hóa dữ liệu sử dụng thực tế, đảm bảo nó đại diện cho các kịch bản triển khai thực tế. -3. Synthetic Generation: Use LLMs to generate initial examples, then have experts validate and refine them. +3. Tạo tổng hợp: Sử dụng `LLM` để tạo các ví dụ ban đầu, sau đó để các chuyên gia xác thực và tinh chỉnh chúng. -## Best Practices +## Các phương pháp tốt nhất -- Document your evaluation methodology thoroughly, including any assumptions or limitations -- Include diverse test cases that cover different aspects of your domain -- Consider both automated metrics and human evaluation where appropriate -- Version control your evaluation datasets and code -- Regularly update your evaluation suite as you discover new edge cases or requirements +- Ghi lại phương pháp đánh giá của bạn một cách kỹ lưỡng, bao gồm mọi giả định hoặc hạn chế +- Bao gồm các trường hợp kiểm thử đa dạng bao gồm các khía cạnh khác nhau trong lĩnh vực của bạn +- Xem xét cả các chỉ số tự động và đánh giá thủ công khi thích hợp +- Kiểm soát phiên bản (`Version control`) các tập dữ liệu và mã đánh giá của bạn +- Thường xuyên cập nhật bộ đánh giá của bạn khi bạn phát hiện ra các trường hợp biên hoặc yêu cầu mới -## References +## Tài liệu tham khảo -- [LightEval Custom Task Guide](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task) -- [LightEval Custom Metrics](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric) -- [Argilla Documentation](https://docs.argilla.io) for dataset annotation -- [Evaluation Guidebook](https://github.com/huggingface/evaluation-guidebook) for general evaluation principles +- [Hướng dẫn tác vụ tùy chỉnh LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task) +- [Chỉ số tùy chỉnh LightEval](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric) +- [Tài liệu Argilla](https://docs.argilla.io) để chú thích tập dữ liệu +- [Hướng dẫn đánh giá](https://github.com/huggingface/evaluation-guidebook) cho các nguyên tắc đánh giá chung -# Next Steps +# Các bước tiếp theo -⏩ For a complete example of implementing these concepts, see our [domain evaluation project](./project/README.md). \ No newline at end of file +⏩ Để có một ví dụ đầy đủ về việc triển khai các khái niệm này, hãy xem [dự án đánh giá theo lĩnh vực](./project/README.md) của chúng tôi. \ No newline at end of file From ca3eaf0b58d218420d17b708059a17636e0f7c52 Mon Sep 17 00:00:00 2001 From: hahuyhoang411 Date: Wed, 1 Jan 2025 23:38:12 +0100 Subject: [PATCH 7/7] init --- ...hteval_evaluate_and_analyse_your_LLM.ipynb | 66 +++++++++---------- vi/4_evaluation/project/README.md | 66 +++++++++---------- 2 files changed, 65 insertions(+), 67 deletions(-) diff --git a/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb b/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb index 9597634b..92d67ae1 100644 --- a/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb +++ b/vi/4_evaluation/notebooks/lighteval_evaluate_and_analyse_your_LLM.ipynb @@ -6,24 +6,24 @@ "id": "TZVw9f5QYWPL" }, "source": [ - "# lighteval is your AI evaluation library\n", + "# `lighteval` là thư viện đánh giá AI của bạn\n", "\n", - "This notebook explores how you can use lighteval to evaluate and compare LLMs.\n", + "Notebook này khám phá cách bạn có thể sử dụng `lighteval` để đánh giá và so sánh các `LLM`.\n", "\n", - "`lighteval` has been around a while and it's a great tool for getting eval score on major benchmarks. It's just been refactored to support being used like a library in Python, which makes it great for comparing models across benchmarks.\n", + "`lighteval` đã xuất hiện được một thời gian và nó là một công cụ tuyệt vời để đạt được điểm đánh giá trên các `benchmark` chính. Nó vừa được tái cấu trúc để hỗ trợ việc sử dụng như một thư viện trong Python, điều này làm cho nó trở nên tuyệt vời để so sánh các mô hình trên các `benchmark`.\n", "\n", - "So let's dig in to some eval scores.\n", + "Vì vậy, hãy đi sâu vào một số điểm đánh giá.\n", "\n", "

\n", - "

Exercise: Evaluate Your Own Model

\n", - "

Now that you've seen how to evaluate models on specific domains, try evaluating a model on a domain that interests you.

\n", - "

Difficulty Levels

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🐢 Use the existing medical domain tasks but evaluate a different model from the Hugging Face hub

\n", - "

🐕 Create a new domain evaluation by selecting different MMLU tasks (e.g., computer science, mathematics, physics)

\n", - "

🦁 Create a custom evaluation task using LightEval's task framework and evaluate models on your specific domain

\n", + "

Bài tập: Đánh giá mô hình của riêng bạn

\n", + "

Bây giờ bạn đã thấy cách đánh giá các mô hình trên các lĩnh vực cụ thể, hãy thử đánh giá một mô hình trên một lĩnh vực mà bạn quan tâm.

\n", + "

Các mức độ khó

\n", + "

🐢 Sử dụng các tác vụ lĩnh vực y tế hiện có nhưng đánh giá một mô hình khác từ Hugging Face Hub

\n", + "

🐕 Tạo một đánh giá lĩnh vực mới bằng cách chọn các tác vụ MMLU khác nhau (ví dụ: khoa học máy tính, toán học, vật lý)

\n", + "

🦁 Tạo một tác vụ đánh giá tùy chỉnh bằng cách sử dụng khung tác vụ của LightEval và đánh giá các mô hình trên lĩnh vực cụ thể của bạn

\n", "
\n", "\n", - "## Install dependencies" + "## Cài đặt các thư viện" ] }, { @@ -63,17 +63,17 @@ "id": "TDKs5ShvXw8K" }, "source": [ - "## Setup `lighteval` evaluation\n", + "## Thiết lập đánh giá `lighteval`\n", "\n", - "We need to setup the evaluation environment and pipeline. Much of this we will disable because we're keeping things in the notebook, but we could also use `push_to_hub` or `push_to_tensorboard`.\n", + "Chúng ta cần thiết lập môi trường và quy trình (`pipeline`) đánh giá. Bài tập này sẽ lưu mọi thứ trong notebook, nhưng bạn cũng có thể sử dụng `push_to_hub` hoặc `push_to_tensorboard` để lưu trữ các tập đánh giá để sử dụng về sau.\n", "\n", "### `push_to_hub`\n", "\n", - "This is useful if we're evaluating a model and want to persist its evaluation with weights and configuration on the Hugging Face hub.\n", + "Lệnh này rất hữu ích nếu chúng ta đang đánh giá một mô hình và muốn lưu trữ đánh giá của nó với trọng số và cấu hình (`configs`) trên Hugging Face Hub.\n", "\n", "### `push_to_tensorboard`\n", "\n", - "This would be useful if we were building an evaluation tool or script, where we wanted to view results within tensorboard." + "Lệnh này sẽ hữu ích nếu chúng ta đang xây dựng một công cụ hoặc tập lệnh đánh giá, nơi chúng ta muốn xem kết quả trong `tensorboard`." ] }, { @@ -206,30 +206,30 @@ "id": "nsNjwzCtltkA" }, "source": [ - "# Compares models with `lighteval`\n", + "# So sánh các mô hình với `lighteval`\n", "\n", - "We are going to compare two small LLMs on a domain. We will use `Qwen2.5-0.5B` and `SmolLM2-360M-Instruct` and we will evaluate them on a medical domain.\n", + "Chúng ta sẽ so sánh hai `LLM` nhỏ trên một lĩnh vực. Chúng ta sẽ sử dụng `Qwen2.5-0.5B` và `SmolLM2-360M-Instruct` và chúng ta sẽ đánh giá chúng trên lĩnh vực y tế.\n", "\n", - "We can create a domain evaluation from a subset of MMLU evaluations, by defining the evaluation tasks. In lighteval, tasks are described as strings.\n", + "Chúng ta có thể tạo một đánh giá lĩnh vực từ một tập hợp con các đánh giá `MMLU`, bằng cách xác định các tác vụ đánh giá. Trong `lighteval`, các tác vụ được mô tả dưới dạng chuỗi.\n", "\n", "`{suite}|{task}:{subtask}|{num_few_shot}|{0 or 1 to reduce num_few_shot if prompt is too long}`\n", "\n", - "Therefore, we will pass our list of medicine related tasks like this:\n", + "Do đó, chúng ta sẽ chuyển danh sách các tác vụ liên quan đến y học của mình như sau:\n", "\n", "```\n", "\"leaderboard|mmlu:anatomy|5|0,leaderboard|mmlu:professional_medicine|5|0,leaderboard|mmlu:high_school_biology|5|0,leaderboard|mmlu:high_school_chemistry|5|0\"\n", "```\n", "\n", - "Which can be translated to :\n", + "Có thể được hiểu như sau:\n", "\n", - "| Suite | Task | Num Fewshot Example | Limit Fewshots |\n", + "| Suite | Tác vụ | Số mẫu Fewshot | Giới hạn Fewshots |\n", "|---|---|---|---|\n", "| leaderboard | mmlu:anatomy | 5 | False |\n", "| leaderboard | mmlu:professional_medicine | 5 | False |\n", "| leaderboard | mmlu:high_school_biology | 5 | False |\n", "| leaderboard | mmlu:high_school_chemistry | 5 | False |\n", "\n", - "For a full list of lighteval supported tasks. Checkout this page in [the documentation](https://github.com/huggingface/lighteval/wiki/Available-Tasks)." + "Để có danh sách đầy đủ các tác vụ được `lighteval` hỗ trợ. Hãy xem trang này trong [tài liệu](https://github.com/huggingface/lighteval/wiki/Available-Tasks)." ] }, { @@ -249,7 +249,7 @@ "id": "XwcJklSFX4H6" }, "source": [ - "# Evaluate Qwen2.5 0.5B" + "# Đánh giá Qwen2.5 0.5B" ] }, { @@ -716,7 +716,7 @@ "id": "CIwCaCxJX_hA" }, "source": [ - "# Evaluate SmolLM 360M" + "# Đánh giá SmolLM 360M" ] }, { @@ -877,9 +877,9 @@ "id": "0HD8aFwSYGHu" }, "source": [ - "# Visualize Results\n", + "# Trực quan hóa kết quả\n", "\n", - "Now that we have results from the two models we can visualize them side-by-side. We'll keep visualisation simple here, but with this data structure you could represent scores in many ways." + "Bây giờ chúng ta đã có kết quả từ hai mô hình, chúng ta có thể trực quan hóa chúng cạnh nhau. Chúng ta sẽ giữ cho việc trực quan hóa đơn giản ở đây, nhưng với cấu trúc dữ liệu này, bạn có thể biểu diễn điểm số theo nhiều cách." ] }, { @@ -909,13 +909,13 @@ "id": "qJEbQeYDplKX" }, "source": [ - "# 💐 That's it!\n", + "# 💐 Chúc mừng bạn đã hoàn thành!\n", "\n", - "You have a handy notebook to view model evals. You could use this to:\n", + "Bạn đã có một notebook tiện dụng để đánh giá các mô hình. Bạn có thể sử dụng notebook này để:\n", "\n", - "- select the right model for your inference use case\n", - "- evaluate checkpoints during training\n", - "- share model scores" + "- chọn mô hình phù hợp cho trường hợp sử dụng suy luận của bạn\n", + "- đánh giá các điểm kiểm tra trong quá trình đào tạo\n", + "- chia sẻ điểm số mô hình" ] }, { @@ -924,9 +924,9 @@ "id": "jWdS38syaipm" }, "source": [ - "🏃Next Steps\n", + "🏃Các bước tiếp theo\n", "\n", - "- If you want to go deeper into your evaluation results check out this [notebook](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)\n" + "- Nếu bạn muốn đi sâu hơn vào kết quả đánh giá của mình, hãy xem [sổ tay này](https://github.com/huggingface/evaluation-guidebook/blob/main/contents/examples/comparing_task_formulations.ipynb)" ] } ], diff --git a/vi/4_evaluation/project/README.md b/vi/4_evaluation/project/README.md index 98d32eca..4e47e8e5 100644 --- a/vi/4_evaluation/project/README.md +++ b/vi/4_evaluation/project/README.md @@ -1,72 +1,72 @@ -# Domain Specific Evaluation with Argilla, Distilabel, and LightEval +# Đánh Giá Theo Lĩnh Vực Cụ Thể với Argilla, Distilabel, và LightEval -Most popular benchmarks look at very general capabilities (reasoning, math, code), but have you ever needed to study more specific capabilities? +Hầu hết các bộ tiêu chuẩn (`benchmark`) phổ biến đều xem xét các khả năng rất chung chung (lý luận, toán học, lập trình), nhưng bạn đã bao giờ cần nghiên cứu các khả năng cụ thể hơn chưa? -What should you do if you need to evaluate a model on a **custom domain** relevant to your use-cases? (For example, financial, legal, medical use cases) +Bạn nên làm gì nếu bạn cần đánh giá một mô hình trên một **lĩnh vực cụ thể** liên quan đến các trường hợp sử dụng của bạn? (Ví dụ: các trường hợp sử dụng tài chính, pháp lý, y tế) -This tutorial shows you the full pipeline you can follow, from creating relevant data and annotating your samples to evaluating your model on them, with the easy to use [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel), and [lighteval](https://github.com/huggingface/lighteval). For our example, we'll focus on generating exam questions from multiple documents. +Hướng dẫn này chỉ cho bạn toàn bộ quy trình (`pipeline`) mà bạn có thể làm theo, từ việc tạo dữ liệu liên quan và chú thích các mẫu của bạn đến việc đánh giá mô hình của bạn trên chúng, với các công cụ dễ sử dụng [Argilla](https://github.com/argilla-io/argilla), [distilabel](https://github.com/argilla-io/distilabel), và [lighteval](https://github.com/huggingface/lighteval). Trong ví dụ của chúng tôi, chúng tôi sẽ tập trung vào việc tạo các câu hỏi đánh giá từ nhiều tài liệu. -## Project Structure +## Cấu trúc dự án -For our process, we will follow 4 steps, with a script for each: generating a dataset, annotating it, extracting relevant samples for evaluation from it, and actually evaluating models. +Đối với quy trình của chúng tôi, chúng tôi sẽ làm theo 4 bước, với một tập lệnh cho mỗi bước: tạo tập dữ liệu, chú thích nó, trích xuất các mẫu liên quan để đánh giá từ nó và thực sự đánh giá các mô hình. -| Script Name | Description | +| Tên tập lệnh | Mô tả | |-------------|-------------| -| generate_dataset.py | Generates exam questions from multiple text documents using a specified language model. | -| annotate_dataset.py | Creates an Argilla dataset for manual annotation of the generated exam questions. | -| create_dataset.py | Processes annotated data from Argilla and creates a Hugging Face dataset. | -| evaluation_task.py | Defines a custom LightEval task for evaluating language models on the exam questions dataset. | +| generate_dataset.py | Tạo các câu hỏi đánh giá từ nhiều tài liệu văn bản bằng cách sử dụng một mô hình ngôn ngữ được chỉ định. | +| annotate_dataset.py | Tạo tập dữ liệu Argilla để chú thích thủ công các câu hỏi đánh giá được tạo. | +| create_dataset.py | Xử lý dữ liệu đã chú thích từ Argilla và tạo tập dữ liệu Hugging Face. | +| evaluation_task.py | Định nghĩa một tác vụ LightEval tùy chỉnh để đánh giá các mô hình ngôn ngữ trên tập dữ liệu câu hỏi đánh giá. | -## Steps +## Các bước thực hành -### 1. Generate Dataset +### 1. Tạo tập dữ liệu -The `generate_dataset.py` script uses the distilabel library to generate exam questions based on multiple text documents. It uses the specified model (default: Meta-Llama-3.1-8B-Instruct) to create questions, correct answers, and incrorect answers (known as distractors). You should add you own data samples and you might wish to use a different model. +Tập lệnh `generate_dataset.py` sử dụng thư viện `distilabel` để tạo các câu hỏi thi dựa trên nhiều tài liệu văn bản. Nó sử dụng mô hình được chỉ định (mặc định: Meta-Llama-3.1-8B-Instruct) để tạo các câu hỏi, câu trả lời đúng và câu trả lời sai (được gọi là câu gây nhiễu). Bạn nên thêm các mẫu dữ liệu của riêng bạn và bạn có thể muốn sử dụng một mô hình khác. -To run the generation: +Để chạy quá trình tạo: ```sh python generate_dataset.py --input_dir path/to/your/documents --model_id your_model_id --output_path output_directory ``` -This will create a [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) containing the generated exam questions for all documents in the input directory. +Thao tác này sẽ tạo một [Distiset](https://distilabel.argilla.io/dev/sections/how_to_guides/advanced/distiset/) chứa các câu hỏi thi được tạo cho tất cả các tài liệu trong thư mục đầu vào. -### 2. Annotate Dataset +### 2. Chú thích tập dữ liệu -The `annotate_dataset.py` script takes the generated questions and creates an Argilla dataset for annotation. It sets up the dataset structure and populates it with the generated questions and answers, randomizing the order of answers to avoid bias. Once in Argilla, you or a domain expert can validate the dataset with the correct answers. +Tập lệnh `annotate_dataset.py` lấy các câu hỏi đã tạo và tạo tập dữ liệu Argilla để chú thích. Nó thiết lập cấu trúc tập dữ liệu và điền vào đó các câu hỏi và câu trả lời đã tạo, sắp xếp ngẫu nhiên thứ tự các câu trả lời để tránh sai lệch. Khi ở trong Argilla, bạn hoặc một chuyên gia trong lĩnh vực có thể xác thực tập dữ liệu với các câu trả lời đúng. -You will see suggested correct answers from the LLM in random order and you can approve the correct answer or select a different one. The duration of this process will depend on the scale of your evaluation dataset, the complexity of your domain data, and the quality of your LLM. For example, we were able to create 150 samples within 1 hour on the domain of transfer learning, using Llama-3.1-70B-Instruct, mostly by approving the correct answer and discarding the incorrect ones. +Bạn sẽ thấy các câu trả lời đúng được đề xuất từ `LLM` theo thứ tự ngẫu nhiên và bạn có thể phê duyệt câu trả lời đúng hoặc chọn một câu trả lời khác. Thời gian của quá trình này sẽ phụ thuộc vào quy mô của tập dữ liệu đánh giá của bạn, độ phức tạp của dữ liệu lĩnh vực của bạn và chất lượng của `LLM` của bạn. Ví dụ: chúng tôi đã có thể tạo 150 mẫu trong vòng 1 giờ trên lĩnh vực chuyển giao học tập (`transfer learning`), sử dụng Llama-3.1-70B-Instruct, chủ yếu bằng cách phê duyệt câu trả lời đúng và loại bỏ những câu trả lời không chính xác. -To run the annotation process: +Để chạy quá trình chú thích: ```sh python annotate_dataset.py --dataset_path path/to/distiset --output_dataset_name argilla_dataset_name ``` -This will create an Argilla dataset that can be used for manual review and annotation. +Thao tác này sẽ tạo một tập dữ liệu Argilla có thể được sử dụng để xem xét và chú thích thủ công. ![argilla_dataset](./images/domain_eval_argilla_view.png) -If you're not using Argilla, deploy it locally or on spaces following this [quickstart guide](https://docs.argilla.io/latest/getting_started/quickstart/). +Nếu bạn không sử dụng Argilla, hãy triển khai cục bộ hoặc trên không gian (`spaces`) theo [hướng dẫn bắt đầu nhanh](https://docs.argilla.io/latest/getting_started/quickstart/) này. -### 3. Create Dataset +### 3. Tạo tập dữ liệu -The `create_dataset.py` script processes the annotated data from Argilla and creates a Hugging Face dataset. It handles both suggested and manually annotated answers. The script will create a dataset with the question, possible answers, and the column name for the correct answer. To create the final dataset: +Tập lệnh `create_dataset.py` xử lý dữ liệu đã chú thích từ Argilla và tạo tập dữ liệu Hugging Face. Nó xử lý cả các câu trả lời được đề xuất và được chú thích thủ công. Tập lệnh sẽ tạo một tập dữ liệu với câu hỏi, các câu trả lời có thể và tên cột cho câu trả lời đúng. Để tạo tập dữ liệu cuối cùng: ```sh huggingface_hub login python create_dataset.py --dataset_path argilla_dataset_name --dataset_repo_id your_hf_repo_id ``` -This will push the dataset to the Hugging Face Hub under the specified repository. You can view the sample dataset on the hub [here](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train), and a preview of the dataset looks like this: +Thao tác này sẽ đẩy tập dữ liệu lên Hugging Face Hub dưới kho lưu trữ được chỉ định. Bạn có thể xem tập dữ liệu mẫu trên `hub` [tại đây](https://huggingface.co/datasets/burtenshaw/exam_questions/viewer/default/train) và bản xem trước của tập dữ liệu trông như thế này: ![hf_dataset](./images/domain_eval_dataset_viewer.png) -### 4. Evaluation Task +### 4. Tác vụ đánh giá -The `evaluation_task.py` script defines a custom LightEval task for evaluating language models on the exam questions dataset. It includes a prompt function, a custom accuracy metric, and the task configuration. +Tập lệnh `evaluation_task.py` định nghĩa một tác vụ LightEval tùy chỉnh để đánh giá các mô hình ngôn ngữ trên tập dữ liệu câu hỏi thi. Nó bao gồm một hàm nhắc nhở (`prompt function`), một chỉ số chính xác tùy chỉnh và cấu hình tác vụ. -To evaluate a model using lighteval with the custom exam questions task: +Để đánh giá một mô hình bằng `lighteval` với tác vụ câu hỏi thi tùy chỉnh: ```sh lighteval accelerate \ @@ -76,10 +76,8 @@ lighteval accelerate \ --output_dir "./evals" ``` -You can find detailed guides in lighteval wiki about each of these steps: - -- [Creating a Custom Task](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task) -- [Creating a Custom Metric](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric) -- [Using existing metrics](https://github.com/huggingface/lighteval/wiki/Metric-List) - +Bạn có thể tìm thấy các hướng dẫn chi tiết trong `lighteval wiki` về từng bước sau: +- [Tạo tác vụ tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task) +- [Tạo chỉ số tùy chỉnh](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric) +- [Sử dụng các chỉ số hiện có](https://github.com/huggingface/lighteval/wiki/Metric-List) \ No newline at end of file

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