Summary of Attamba: Attending to Multi-token States, by Yash Akhauri et al.
Attamba: Attending To Multi-Token States
by Yash Akhauri, Safeen Huda, Mohamed S. Abdelfattah
First submitted to arxiv on: 26 Nov 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 This paper introduces Attamba, a novel architecture that uses state-space models to compress chunks of tokens in sequence-to-sequence tasks. By replacing key-value projections with SSMs, Attamba achieves improved model quality and enables flexible token chunking, resulting in 24% improved perplexity with similar KV-Cache and attention footprint, or ~4 times smaller KV-Cache and Attention FLOPs for a 5% perplexity trade-off. This architecture can perform attention on chunked-sequences of variable length, offering adaptable efficiency gains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Attamba is a new way to predict what comes next in a sequence of tokens. Instead of looking at all the previous tokens, Attamba uses a special kind of model that compresses the whole sequence into a smaller piece of information. This helps make the process more efficient and accurate. By doing things this way, Attamba can improve its ability to predict what’s next, while also being able to handle longer sequences without getting too slow. |
Keywords
» Artificial intelligence » Attention » Perplexity » Token
