Summary of Transformers to Ssms: Distilling Quadratic Knowledge to Subquadratic Models, by Aviv Bick et al.
Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models
by Aviv Bick, Kevin Y. Li, Eric P. Xing, J. Zico Kolter, Albert Gu
First submitted to arxiv on: 19 Aug 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 This paper proposes a method called MOHAWK that distills a pre-trained transformer architecture into alternative state space models (SSMs), enabling the use of less computational resources. The authors demonstrate the effectiveness of their approach by distilling a Mamba-2 variant based on the Phi-1.5 architecture (Phi-Mamba) and a hybrid version using only 3B tokens, achieving stronger performance than past open-source non-transformer models. This method allows SSMs to leverage computational resources invested in training transformer-based architectures, opening up new avenues for building such models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to use less computer power when doing tasks like language modeling. Right now, the best way to do this is with something called Transformers, but they can be slow and use a lot of energy. The authors came up with an idea that lets them take a big, powerful Transformer model and turn it into a smaller, faster one using something called state space models (SSMs). They tested their method and found that the new models were really good at doing tasks like language modeling, even when they didn’t use as much computer power. This is important because it means we might be able to do more things with less energy in the future. |
Keywords
* Artificial intelligence * Transformer
