Summary of Hymba: a Hybrid-head Architecture For Small Language Models, by Xin Dong et al.
Hymba: A Hybrid-head Architecture for Small Language Models
by Xin Dong, Yonggan Fu, Shizhe Diao, Wonmin Byeon, Zijia Chen, Ameya Sunil Mahabaleshwarkar, Shih-Yang Liu, Matthijs Van Keirsbilck, Min-Hung Chen, Yoshi Suhara, Yingyan Lin, Jan Kautz, Pavlo Molchanov
First submitted to arxiv on: 20 Nov 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 proposed Hymba family of small language models leverages a hybrid-head parallel architecture that combines transformer attention mechanisms with state space models (SSMs) to boost efficiency. This design integrates high-resolution recall from attention heads with efficient context summarization from SSM heads. Additionally, learnable meta tokens are introduced to store critical information and alleviate the “forced-to-attend” burden associated with attention mechanisms. The model is further optimized through cross-layer key-value (KV) sharing and partial sliding window attention, resulting in a compact cache size. In controlled experiments comparing various architectures under identical settings, significant advantages of the proposed architecture were observed. Notably, Hymba achieves state-of-the-art results for small LMs: the Hymba-1.5B-Base model surpasses all sub-2B public models in performance and outperforms Llama-3.2-3B with 1.32% higher average accuracy, an 11.67x cache size reduction, and 3.49x throughput. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hymba is a new way to make language models better. It uses two special techniques: transformer attention mechanisms and state space models (SSMs). These help the model remember important details and understand context more efficiently. The team also added something called learnable meta tokens, which store important information and reduce the burden of remembering everything. To make it even faster, they used cross-layer key-value sharing and partial sliding window attention. This made the model’s cache smaller and faster. In tests, Hymba did much better than other models, especially in small language models. |
Keywords
* Artificial intelligence * Attention * Llama * Recall * Summarization * Transformer
