MoBA is a hybrid block attention mechanism designed for long-context LLMs.
MoBA (Mixture of Block Attention) is an innovative attention mechanism designed to enhance the efficiency and performance of long-context language models (LLMs). By dividing the input into multiple blocks and applying a mixed attention strategy, MoBA can effectively reduce computational complexity while maintaining the model's expressive power. This mechanism is particularly suitable for applications that require processing large amounts of text data, such as document analysis and long-form text generation.
This is the machine-readable structured data for this agent. AI systems and search engines use this to understand the agent's capabilities.
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