Summary of Perft: Parameter-efficient Routed Fine-tuning For Mixture-of-expert Model, by Yilun Liu et al.
PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model
by Yilun Liu, Yunpu Ma, Shuo Chen, Zifeng Ding, Bailan He, Zhen Han, Volker Tresp
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Mixture-of-Experts (MoE) paradigm has been shown to be a powerful approach for scaling transformers with improved resource utilization. However, efficiently fine-tuning MoE models remains largely underexplored. This paper presents a unified framework for integrating Parameter-Efficient Fine-Tuning (PEFT) modules directly into the MoE mechanism. The framework includes various design dimensions, such as functional and composition strategies. By combining these design choices, the authors introduce Parameter-Efficient Routed Fine-Tuning (PERFT) as a flexible and scalable family of PEFT strategies tailored for MoE models. The effectiveness of PERFT is demonstrated through extensive experiments on adapting OLMoE-1B-7B and Mixtral-87B for commonsense and arithmetic reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE models are a type of transformer that can be fine-tuned to do many different tasks. But making these models work efficiently is still an open problem. This paper solves this problem by creating a way to combine two techniques: MoE and Parameter-Efficient Fine-Tuning (PEFT). The new technique, called PERFT, makes it possible to fine-tune MoE models in a way that’s both efficient and effective. The authors tested PERFT on several tasks, like answering questions about the world or doing math problems, and showed that it works really well. |
Keywords
» Artificial intelligence » Fine tuning » Mixture of experts » Parameter efficient » Transformer
