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About this paper
Yash Akhauri, Safeen Huda, Mohamed S. Abdelfattah
This paper introduces a novel architecture called Attamba that uses state-space models to compress token sequences for more efficient transformer attention
Key insights
- SSMs can effectively compress multiple tokens into meaningful single-token states
- Model is robust to random chunk boundaries, enabling flexible computation-quality tradeoffs
- Cyclic chunking across layers helps mitigate position bias from fixed chunk boundaries
- Preserving full attention on recent tokens improves model quality
Summary by alphaXiv.
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