Hymba Hybrid-Head Architecture Boosts Small Language Model Performance – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-07-11T15:00:00Z http://www.open-lab.net/blog/feed/ Xin Dong <![CDATA[Hymba Hybrid-Head Architecture Boosts Small Language Model Performance]]> http://www.open-lab.net/blog/?p=92595 2024-12-12T19:38:36Z 2024-11-22T17:31:14Z Transformers, with their attention-based architecture, have become the dominant choice for language models (LMs) due to their strong performance,...]]> Transformers, with their attention-based architecture, have become the dominant choice for language models (LMs) due to their strong performance,...

Transformers, with their attention-based architecture, have become the dominant choice for language models (LMs) due to their strong performance, parallelization capabilities, and long-term recall through key-value (KV) caches. However, their quadratic computational cost and high memory demands pose efficiency challenges. In contrast, state space models (SSMs) like Mamba and Mamba-2 offer constant��

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