现代大型语言模型中的注意力变体视觉指南

Modern large language models (LLMs) are increasingly utilized in natural language processing, with attention mechanisms being a core component. Common forms include Multi-Head Attention (MHA) and Global Query Attention (GQA), while new variants such as Multi-Layer Attention (MLA), sparse attention, and hybrid architectures are emerging. These variants enhance model performance and adaptability by optimizing computational efficiency and memory usage. Sparse attention reduces computational complexity by selectively focusing on certain parts of the input, while hybrid architectures combine different types of attention mechanisms for more flexible model design. A deeper understanding of these attention variants aids researchers and engineers in making informed choices when building and optimizing LLMs.