Summary of Retraining-free Merging Of Sparse Moe Via Hierarchical Clustering, by I-chun Chen et al.
Retraining-Free Merging of Sparse MoE via Hierarchical Clustering
by I-Chun Chen, Hsu-Shen Liu, Wei-Fang Sun, Chen-Hao Chao, Yen-Chang Hsu, Chun-Yi Lee
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Medium Difficulty summary: This paper introduces Hierarchical Clustering for Sparsely activated Mixture of Experts (HC-SMoE), a framework that reduces the memory requirements of expert components in sparse mixture-of-experts (SMoE) models without retraining. HC-SMoE uses a novel clustering approach based on expert outputs to merge experts effectively, enabling large-scale architectures to be deployed in resource-limited environments. The authors demonstrate the effectiveness of HC-SMoE through theoretical analysis and comprehensive evaluations across multiple zero-shot language tasks, including Qwen and Mixtral, achieving state-of-the-art performance. HC-SMoE’s superior performance and practical applicability make it a promising solution for real-world deployments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper helps make big language models work better in places with limited computer power. The problem is that these models take up too much space on computers, which can cause problems. The authors came up with a new way to combine parts of the model without retraining it from scratch. They tested this new method and showed it works well for tasks like recognizing text or understanding speech. This new approach could help make these powerful language models more useful in real-life situations. |
Keywords
» Artificial intelligence » Clustering » Hierarchical clustering » Mixture of experts » Zero shot
