A comprehensive exploration of attention mechanisms in transformers and how they enable models to selectively focus on relevant information.
Understand how speculative decoding achieves 2-4x faster LLM inference without compromising output quality. This technique uses a smaller model to draft tokens that are verified in parallel by the main model, solving the memory bandwidth bottleneck.