Magpie-Qwen2-Pro-200K-English


Project Web: [https://magpie-align.github.io/](https://magpie-align.github.io/)

Arxiv Technical Report: [https://arxiv.org/abs/2406.08464](https://arxiv.org/abs/2406.08464)

Codes: [https://github.com/magpie-align/magpie](https://github.com/magpie-align/magpie)

## Abstract
<details><summary>Click Here</summary>
High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent existing open-source data creation methods from scaling effectively, potentially limiting the diversity and quality of public alignment datasets. Is it possible to synthesize high-quality instruction data at scale by extracting it directly from an aligned LLM? We present a self-synthesis method for generating large-scale alignment data named Magpie. Our key observation is that aligned LLMs like Llama-3-Instruct can generate a user query when we input only the left-side templates up to the position reserved for user messages, thanks to their auto-regressive nature. We use this method to prompt Llama-3-Instruct and generate 4 million instructions along with their corresponding responses. We perform a comprehensive analysis of the extracted data and select 300K high-quality instances. To compare Magpie data with other public instruction datasets, we fine-tune Llama-3-8B-Base with each dataset and evaluate the performance of the fine-tuned models. Our results indicate that in some tasks, models fine-tuned with Magpie perform comparably to the official Llama-3-8B-Instruct, despite the latter being enhanced with 10 million data points through supervised fine-tuning (SFT) and subsequent feedback learning. We also show that using Magpie solely for SFT can surpass the performance of previous public datasets utilized for both SFT and preference optimization, such as direct preference optimization with UltraFeedback. This advantage is evident on alignment benchmarks such as AlpacaEval, ArenaHard, and WildBench.
</details><be>

## Dataset Details
This dataset is generated by [Qwen/Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) using [Magpie](https://huggingface.co/Magpie-Align). Please refer to our [paper](https://arxiv.org/abs/2406.08464) and [codebase](https://github.com/magpie-align/magpie) for implementation details.

### Available Labels
- **Input Length**: The total number of characters in the instructions.
- **Output Length**: The total number of characters in the responses.
- **Task Category**: The specific category of the instructions.
- **Input Quality**: The clarity, specificity, and coherence of the instructions, rated as 'very poor', 'poor', 'average', 'good', and 'excellent'.
- **Input Difficulty**: The level of knowledge required to address the task described in the instruction, rated as 'very easy', 'easy', 'medium', 'hard', or 'very hard'.
- **Minimum Neighbor Distance**: The embedding distance to the nearest neighbor within the dataset. It can be used for filtering out repetitive or similar instances.
- **Safety**: Safety tags marked by [meta-llama/Meta-Llama-Guard-2-8B](https://huggingface.co/meta-llama/Meta-Llama-Guard-2-8B)
- **Reward**: The output of the reward model given the specific instruction-response pair.
- **Language**: The language of the instruction.

## Filter Setups
- **Input Quality**: >= good
- **Instruction Reward**: >=-10
- **Language**: English
- Remove repetition and incomplete instructions (e.g., end with :)
- Choose 200K data with the longest responses

## Dataset Navigation 🧭
|Model Name | Dataset | Type | Description |
|-------------|:-------|:-------|:-------|
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) | [Magpie-Qwen2-Pro-1M](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-1M-v0.1) | SFT | 1M Raw conversations built with Qwen2 72B Instruct.
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) | [Magpie-Qwen2-Pro-300K-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-300K-Filtered) | SFT | Apply a filter and select 300K high quality conversations.
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) | [Magpie-Qwen2-Pro-200K-Chinese](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese) | SFT | Apply a filter and select 200K high quality Chinese conversations.
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) | [Magpie-Qwen2-Pro-200K-English](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-200K-English) | SFT | Apply a filter and select 200K high quality English conversations.


项目主页：[https://magpie-align.github.io/](https://magpie-align.github.io/)

arXiv技术报告：[https://arxiv.org/abs/2406.08464](https://arxiv.org/abs/2406.08464)

代码仓库：[https://github.com/magpie-align/magpie](https://github.com/magpie-align/magpie)

## 摘要
<details><summary>点击展开</summary>
高质量的指令数据对于对齐大语言模型（Large Language Model，LLM）至关重要。尽管部分模型如Llama-3-Instruct已开放权重，但其对齐数据仍处于私有状态，这阻碍了人工智能的民主化进程。高昂的人力成本与受限的预定义提示范围，使得现有开源数据构建方法难以有效规模化，进而可能限制了公开对齐数据集的多样性与质量。能否通过直接从已对齐的大语言模型中提取数据，规模化合成高质量的指令数据？我们提出了一种用于生成大规模对齐数据的自合成方法，名为Magpie。我们的核心观察是：得益于自回归特性，当仅输入用户消息预留位置之前的左侧模板时，像Llama-3-Instruct这类已对齐的大语言模型能够生成用户查询。我们利用该方法对Llama-3-Instruct进行提示，生成了400万条指令及其对应的响应。我们对提取的数据进行了全面分析，并筛选出30万个高质量实例。为了将Magpie数据集与其他公开指令数据集进行对比，我们使用每个数据集分别微调Llama-3-8B-Base，并评估微调后模型的性能。我们的结果表明，在部分任务中，使用Magpie数据微调的模型性能可与官方的Llama-3-8B-Instruct相媲美——尽管后者通过监督微调（Supervised Fine-Tuning, SFT）与后续反馈学习，使用了1000万条数据进行增强。我们还证明，仅使用Magpie数据进行SFT，其性能可超越此前用于SFT与偏好优化（如结合UltraFeedback的直接偏好优化）的公开数据集。这一优势在AlpacaEval、ArenaHard与WildBench等对齐基准测试中表现显著。
</details><be>

## 数据集详情
本数据集由[Qwen/Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct)通过[Magpie](https://huggingface.co/Magpie-Align)生成。有关实现细节，请参阅我们的[论文](https://arxiv.org/abs/2406.08464)与[代码库](https://github.com/magpie-align/magpie)。

### 可用标签
- **输入长度**：指令中的总字符数。
- **输出长度**：响应中的总字符数。
- **任务类别**：指令所属的具体类别。
- **输入质量**：指令的清晰度、特异性与连贯性，评级分为「极差」「差」「一般」「良好」「优秀」。
- **输入难度**：完成指令描述任务所需的知识水平，评级分为「极简单」「简单」「中等」「困难」「极困难」。
- **最小近邻距离**：数据集中与当前实例最近邻的嵌入距离，可用于过滤重复或相似实例。
- **安全性**：由[meta-llama/Meta-Llama-Guard-2-8B](https://huggingface.co/meta-llama/Meta-Llama-Guard-2-8B)标记的安全标签。
- **奖励得分**：针对特定指令-响应对的奖励模型输出值。
- **语言**：指令所使用的语言。

## 筛选设置
- **输入质量**：≥ 良好
- **指令奖励得分**：≥ -10
- **语言**：英语
- 移除重复与不完整的指令（例如以冒号结尾的指令）
- 选取20万个响应最长的数据

## 数据集导航 🧭
|模型名称 | 数据集 | 类型 | 描述 |
|-------------|:-------|:-------|:-------|
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) | [Magpie-Qwen2-Pro-1M](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-1M-v0.1) | SFT | 基于Qwen2 72B Instruct构建的100万条原始会话数据。
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) | [Magpie-Qwen2-Pro-300K-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-300K-Filtered) | SFT | 经过筛选后选取的30万个高质量会话数据。
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) | [Magpie-Qwen2-Pro-200K-Chinese](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese) | SFT | 经过筛选后选取的20万个高质量中文会话数据。
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) | [Magpie-Qwen2-Pro-200K-English](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-200K-English) | SFT | 经过筛选后选取的20万个高质量英文会话数据。