Magpie-Air-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 [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-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.

This is the filtered data. Please see below for the filter design. Please do not use **Magpie-Air-300K-Filtered** and **Magpie-Air-MT-300K** to fine-tune the model simultaneously as they are largely the same for the first turn!

You can find the model fine-tuned using this dataset [here](https://huggingface.co/Magpie-Align/Llama-3-8B-Magpie-Air-SFT-v0.1).

## Filter Setups
- **Input Quality**: >= good
- **Input Difficulty**: >= medium
- **Reward difference**: >= 0
- Remove repetition and incomplete instructions (e.g., end with :)
- Choose 300K data with the longest responses

## Dataset Navigation 🧭
|Model Name | Dataset | Type | Description |
|-------------|:-------|:-------|:-------|
| [Llama 3 70B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) | [Magpie-Pro-1M](https://huggingface.co/datasets/Magpie-Align/Llama-3-Magpie-Pro-1M-v0.1) | SFT | 1M Raw conversations built with Meta Llama 3 70B.
| [Llama 3 70B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) | [Magpie-Pro-300K-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered) | SFT | Apply a filter and select 300K high quality conversations.
| [Llama 3 70B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) | [Magpie-Pro-MT-300K](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-MT-300K-v0.1) | SFT | Select 300K difficult questions and extend to multi-turn conversations.
| [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) | [Magpie-Air-3M](https://huggingface.co/datasets/Magpie-Align/Llama-3-Magpie-Air-3M-v0.1) | SFT | 3M Raw conversations built with Meta Llama 3 8B.
| [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) | [Magpie-Air-300K-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Air-300K-Filtered) | SFT | Apply a filter and select 300K high quality data.
| [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) | [Magpie-Air-MT-300K](https://huggingface.co/datasets/Magpie-Align/Magpie-Air-MT-300K-v0.1) | SFT | Select 300K difficult questions and extend to multi-turn 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>
高质量指令数据对于对齐大语言模型（Large Language Model，LLM）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且预定义的提示词范围，使得现有开源数据构建方法难以有效规模化，进而可能制约了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并规模化生成高质量指令数据？

为此我们提出了一种可生成大规模对齐数据的自合成方法，命名为Magpie。我们的核心观察在于：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响应内容。我们对提取得到的数据进行了全面分析，并从中筛选出30万条高质量样本。

为了将Magpie数据集与其他公开指令数据集进行对比，我们分别使用各数据集对Llama-3-8B-Base进行微调，并评估微调后模型的性能。我们的结果显示，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版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）与后续反馈学习得到了增强。我们还证明，仅使用Magpie数据集进行监督微调，其效果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的直接偏好优化方法。该优势在AlpacaEval、ArenaHard与WildBench等对齐基准测试集上均有体现。
</details>

## 数据集详情
本数据集由[Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)基于[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[代码仓库](https://github.com/magpie-align/magpie)。

本数据集为经过筛选后的版本，筛选规则详见下文。请勿同时使用**Magpie-Air-300K-Filtered**与**Magpie-Air-MT-300K**进行模型微调，二者的第一轮对话内容高度重合！

使用本数据集微调得到的模型可于[此处](https://huggingface.co/Magpie-Align/Llama-3-8B-Magpie-Air-SFT-v0.1)获取。

## 筛选规则
- **输入质量**：≥ 良好
- **输入难度**：≥ 中等
- **奖励分差**：≥ 0
- 移除重复与不完整的指令（例如以冒号结尾的内容）
- 选取回复最长的30万条数据

## 数据集导航 🧭
|模型名称 | 数据集 | 类型 | 描述 |
|-------------|:-------|:-------|:-------|
| [Llama 3 70B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) | [Magpie-Pro-1M](https://huggingface.co/datasets/Magpie-Align/Llama-3-Magpie-Pro-1M-v0.1) | SFT | 基于Meta Llama 3 70B构建的100万条原始对话。
| [Llama 3 70B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) | [Magpie-Pro-300K-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered) | SFT | 经过筛选后选取的30万条高质量对话。
| [Llama 3 70B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) | [Magpie-Pro-MT-300K](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-MT-300K-v0.1) | SFT | 选取30万条高难度问题并扩展至多轮对话。
| [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) | [Magpie-Air-3M](https://huggingface.co/datasets/Magpie-Align/Llama-3-Magpie-Air-3M-v0.1) | SFT | 基于Meta Llama 3 8B构建的300万条原始对话。
| [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) | [Magpie-Air-300K-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Air-300K-Filtered) | SFT | 经过筛选后选取的30万条高质量数据。
| [Llama 3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) | [Magpie-Air-MT-300K](https://huggingface.co/datasets/Magpie-Align/Magpie-Air-MT-300K-v0.1) | SFT | 选取30万条高难度问题并扩展至多轮对话。