Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging

Tingfeng Hui, Zhenyu Zhang, Shuohuan Wang, Yu Sun, Hua Wu, Sen Su

Keywords: Mixture-of-Experts, Model upcyling, Upcycling instruction tuning

TL;DR: In this paper, we propose Upcycling Instruction Tuning (UpIT), a data-efficient approach for tuning a dense pre-trained model into an MoE instruct model.

Abstract:

Mixture-of-Experts (MoE) shines brightly in large language models (LLMs) and demonstrates outstanding performance in plentiful natural language processing tasks. However, existing methods that transform LLMs from dense to MoE face significant data requirements and typically rely on large-scale post-training.
In this paper, we propose Upcycling Instruction Tuning (UpIT), a data-efficient approach for tuning a dense pre-trained model into an MoE instruct model.
Specifically, we first point out that intermediate checkpoints during instruction tuning of the dense model are naturally suitable for specialized experts, and then propose an expert expansion stage to flexibly achieve models with different numbers of experts, where genetic algorithm and parameter merging are introduced to ensure sufficient diversity of new extended experts.
To ensure that each differentiated expert in the MoE model works as expected, we select a small amount of seed data that each expert excels to pre-optimize the router.
Extensive experiments with various data scales and upcycling settings demonstrate the outstanding performance and data efficiency of UpIT, as well as stable improvement in expert or data scaling. Further analysis reveals the importance of ensuring expert diversity in upcycling.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 5475