Project Web: https://magpie-align.github.io/

Arxiv Technical Report: https://arxiv.org/abs/2406.08464

Codes: https://github.com/magpie-align/magpie

Abstract

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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.

🤨 Also take a look at our V1 (150K data) with new response generators here:

• Magpie-Align/Magpie-Reasoning-V1-150K (Llama3-70B-Instruct)
• Magpie-Align/Magpie-Reasoning-V1-150K-CoT-QwQ (QwQ-32B-Preview)
• Magpie-Align/Magpie-Reasoning-V1-150K-CoT-Skywork-O1-Llama-3.1-8B (Skywork-O1-Llama-3.1-8B)
• Magpie-Align/Magpie-Reasoning-V1-150K-CoT-Deepseek-R1-Llama-70B (Deepseek-R1-Llama-70B)

🤨 Take a look on more diverse CoT styles here!

• Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Llama3
• Magpie-Align/Magpie-Reasoning-V2-250K-CoT-QwQ
• Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Skywork-O1-Llama-3.1-8B
• Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B [You're here!]

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Dataset Details

This dataset is generated by Meta's Llama 3.1 70B Instruct, Llama 3.3 70B Instruct and deepseek-ai/DeepSeek-R1-Distill-Llama-70B using Magpie framework. Specifically, the instructions are generated by Llama 3.1 70B Instruct and Llama 3.3 70B Instruct, and the responses are generated by DeepSeek-R1-Distill-Llama-70B. Please refer to our paper and codebase for implementation details.

The motivation for developing this dataset is to augment the reasoning capabilities of our models through the utilization of high-quality instruction-response pairs.

Instruction and Response Sources

The instructions come from Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Llama3. Please refer to the corresponding dataset card for details.

The responses are generated by deepseek-ai/DeepSeek-R1-Distill-Llama-70B. Please note that for this release, we do not apply any response filter. If you are going to train your LLMs using this dataset, we recommend performing dataset filtering before training.

License

We release this dataset for research purpose only. For other usage, please follow:

• Meta Llama 3.1 Community License,
• Meta Llama 3.3 Community License, and
• CC BY-NC 4.0.

📚 Citation

If you find the model, data, or code useful, please cite our paper:

@article{xu2024magpie,
	title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing}, 
	author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
	year={2024},
	eprint={2406.08464},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
}