DeepSeek Sparse Attention Implementation Now Available

A new implementation of DeepSeek Sparse Attention has been added to the LLMs-from-scratch repository. The addition provides reference code, comprehensive documentation, and test suites for exploring this machine learning architecture concept. The core innovation behind DeepSeek Sparse Attention is replacing fixed sparse attention patterns with learned ones. Rather than relying solely on a local attention window, the approach uses a lightweight indexer and selector to identify which previous tokens warrant attention. Alongside the implementation, comparative materials are available that position learned sparse attention within the broader context of attention mechanisms, including standard causal attention and sliding-window approaches.