Magpie-Qwen2.5-Pro-300K-Filtered


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/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-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**: >=-5
- Remove repetition and incomplete instructions (e.g., end with :)
- Remove instructions with "Alibaba"
- Choose 300K data with the longest responses

## Dataset Navigation 🧭
|Model Name | Dataset | Type | Description |
|-------------|:-------|:-------|:-------|
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) | [Magpie-Qwen2.5-Pro-1M](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2.5-Pro-1M-v0.1) | SFT | 1M Raw conversations built with Qwen2.5 72B Instruct.
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) | [Magpie-Qwen2.5-Pro-300K-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2.5-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-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.


项目官网：[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>
高质量指令数据对于大语言模型（Large Language Model, LLM）的对齐微调至关重要。尽管诸如Llama-3-Instruct等部分模型已开放权重，但其对齐训练数据仍处于保密状态，这阻碍了人工智能的民主化发展。现有开源数据构建方法面临人力成本高昂、提示预设范围受限的问题，难以实现有效规模化，进而可能制约公开对齐数据集的多样性与质量。能否直接从已对齐的大语言模型中提取数据，规模化生成高质量指令数据？为此我们提出了一种名为Magpie的规模化对齐数据自合成方法。我们的核心发现在于：得益于自回归特性，诸如Llama-3-Instruct这类已对齐的大语言模型，仅需输入至用户消息预留位置的左侧模板，即可生成用户查询。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回复。我们对提取得到的数据进行了全面分析，并从中筛选出30万个高质量样本。为了将Magpie数据集与其他公开指令数据集进行对比，我们使用各数据集分别对Llama-3-8B-Base进行微调，并评估微调后模型的性能。实验结果表明：在部分任务中，使用Magpie数据集微调得到的模型性能可与官方Llama-3-8B-Instruct相媲美——尽管后者通过1000万条数据的监督微调（Supervised Fine-Tuning, SFT）与后续反馈学习完成了性能优化。此外我们还证实，仅使用Magpie数据集进行监督微调，其性能便可超越此前同时用于监督微调与偏好优化的公开数据集，例如结合UltraFeedback的直接偏好优化（Direct Preference Optimization, DPO）方法所使用的数据集。这一优势在AlpacaEval、ArenaHard与WildBench等对齐评测基准中均有体现。
</details><br>

## 数据集详情
本数据集由[Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)借助[Magpie](https://huggingface.co/Magpie-Align)生成。如需了解实现细节，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**：回复中的总字符数。
- **任务类别**：指令所属的具体任务类型。
- **输入质量**：指令的清晰度、针对性与连贯性，评级分为「极差」「较差」「一般」「良好」「优秀」。
- **输入难度**：完成指令描述的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」。
- **最小邻域距离**：数据集中与当前样本最近邻的嵌入空间距离，可用于过滤重复或高度相似的样本。
- **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-回复对，奖励模型输出的评分。
- **语言**：指令所使用的语言。

## 筛选规则
- **输入质量**：≥ 良好
- **指令奖励得分**：≥ -5
- 移除重复与不完整的指令（例如以「:」结尾的样本）
- 移除包含「Alibaba」关键词的指令
- 选取回复长度最长的30万个样本

## 数据集导航 🧭
|模型名称 | 数据集 | 类型 | 描述 |
|-------------|:-------|:-------|:-------|
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) | [Magpie-Qwen2.5-Pro-1M](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2.5-Pro-1M-v0.1) | 监督微调（SFT） | 由Qwen2.5 72B Instruct构建的100万条原始对话数据。
| [Qwen2 72B Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) | [Magpie-Qwen2.5-Pro-300K-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2.5-Pro-300K-Filtered) | 监督微调（SFT） | 经过筛选后得到的30万个高质量对话数据。
| [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) | 监督微调（SFT） | 经过筛选后得到的30万个高质量对话数据。
| [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) | 监督微调（SFT） | 经过筛选后得到的20万个高质量中文对话数据。
| [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) | 监督微调（SFT） | 经过筛选后得到的20万个高质量英文对话数据。