Summary of Mambapeft: Exploring Parameter-efficient Fine-tuning For Mamba, by Masakazu Yoshimura et al.
MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba
by Masakazu Yoshimura, Teruaki Hayashi, Yota Maeda
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 In this paper, researchers investigate parameter-efficient fine-tuning (PEFT) methods for deploying State Space Model (SSM)-based models, specifically Mamba, to downstream tasks while achieving effective performance. They examine existing PEFT methods for Transformers and adapt them for Mamba, proposing new Mamba-specific methods that leverage its distinctive structure. The experiments show that PEFT performs better for Mamba than Transformers. Additionally, the authors demonstrate how to combine multiple PEFT methods effectively and provide a framework that outperforms previous works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba is a type of model that’s different from others. Scientists have been using it to do things like understand speech or recognize pictures. To make this work on new tasks, they need to adjust the model a little bit. This paper looks at how to do that in an efficient way so the model can learn quickly and accurately. They tried some methods that worked well for other models and adapted them for Mamba. They also came up with some new ideas just for Mamba. The results show that this approach works better than others. The scientists also figured out a way to combine their methods to get even better results. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient
