Summary of Mamba State-space Models Are Lyapunov-stable Learners, by John T. Halloran et al.
Mamba State-Space Models Are Lyapunov-Stable Learners
by John T. Halloran, Manbir Gulati, Paul F. Roysdon
First submitted to arxiv on: 31 May 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 Mamba state-space models outperform state-of-the-art Transformer large language models across various tasks. Despite their widespread adoption, there is a lack of research on fine-tuning frameworks for Mamba LLMs, such as mixed-precision fine-tuning (MPFT) and parameter-efficient fine-tuning (PEFT). The paper answers the question of whether Mamba’s recurrent dynamics are robust to small input changes during MPFT using dynamical systems theory. It empirically validates this result through several experiments, showing that Mamba SSMs are more stable than comparable Transformers when both MPFT and PEFT are combined. For PEFT, the paper shows how targeting specific memory buffers in Mamba’s CUDA kernels regularizes SSM parameters for low-rank adaptation and provides computational savings. Finally, it explores the impact of instruction tuning Mamba SSMs for in-context learning (ICL) on natural language tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba state-space models are a type of artificial intelligence that can learn from data. They were recently shown to be better than other types of AI at doing certain tasks. This paper looks at how well these Mamba models do when we make small changes to the way they process information. It uses special math called dynamical systems theory to figure this out and shows that Mamba models are more stable than others when we make these changes. The paper also talks about another way to improve Mamba models, called parameter-efficient fine-tuning, which helps them learn faster and use less computer power. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Low rank adaptation » Parameter efficient » Precision » Transformer
