Summary of Sara: Singular-value Based Adaptive Low-rank Adaption, by Jihao Gu and Shuai Chen and Zelin Wang and Yibo Zhang and Ping Gong
SARA: Singular-Value Based Adaptive Low-Rank Adaption
by Jihao Gu, Shuai Chen, Zelin Wang, Yibo Zhang, Ping Gong
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 A novel approach to efficient fine-tuning of large pre-trained models, called SARA (Singular-Value Based Adaptive Low-Rank Adaption), is proposed. This method leverages Singular Value Decomposition (SVD) to adaptively determine the rank values for different layers in a model, replacing the need for manual verification. The paper also introduces Mo-SARA (Mixture-of-SARA), which further reduces parameters by fine-tuning multiple parallel sets of singular values controlled by a router. Extensive experiments demonstrate the simplicity and efficiency of SARA and Mo-SARA in various complex tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Singular-Value Based Adaptive Low-Rank Adaption is a new way to make big AI models smaller. It uses math to figure out which parts of the model are most important, so it can focus on those areas. This helps the model learn faster and be more efficient. The method also combines multiple sets of values to control what’s learned. Tests show this approach works well for many complex tasks. |
Keywords
» Artificial intelligence » Fine tuning
