Summary of Mixture Of Attentions For Speculative Decoding, by Matthieu Zimmer et al.
Mixture of Attentions For Speculative Decoding
by Matthieu Zimmer, Milan Gritta, Gerasimos Lampouras, Haitham Bou Ammar, Jun Wang
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The abstract presents a solution to address the computational requirements of Large Language Models (LLMs) by proposing a more grounded architecture for small models using Mixture of Attentions. This approach enables faster decoding speeds and state-of-the-art speedups, improving EAGLE-2’s acceptance length by 25%. The novel client-server deployment scenario leverages this architecture to achieve state-of-the-art latencies and maintain higher accuracy in the event of a complete disconnection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes an innovative way to make Large Language Models more efficient. Right now, these models are too big and need too much computer power to work well. The researchers found a way to use smaller models that can work quickly and correctly. They also came up with a new way for devices to talk to each other so the language model can keep working even if there’s no internet. |
Keywords
» Artificial intelligence » Language model
