Hybrid Attention
Hybrid Attention is an attention mechanism design approach in deep learning that combines global attention with different strategies such as local attention and sparse attention. It preserves the ability to model long-range dependencies while reducing computational complexity. This approach is commonly used in large language models to balance model performance and computational efficiency when processing long-sequence contexts, representing an important technical direction in sequence modeling.
Core Facts
Timeline (last 90 days)
The model adopts a hybrid attention scheme in which approximately 75% of the attention layers use linear complexity, thereby saving approximately 75% of computational overhead in long-context scenarios.
Muse Glimmer采用混合注意力机制：大部分层只关注2048 token的局部窗口，每隔四层做一次全局全注意力
All Facts (3)
Qwen3.5 uses a hybrid attention architecture that mixes Gated Delta Net (a linear attention variant) with Gated Attention (standard attention).
65%UnverifiedThe model adopts a hybrid attention scheme in which approximately 75% of the attention layers use linear complexity, thereby saving approximately 75% of computational overhead in long-context scenarios.
50%UnverifiedMuse Glimmer采用混合注意力机制：大部分层只关注2048 token的局部窗口，每隔四层做一次全局全注意力
50%