scalable-dot-product-attention

Scalable dot-product attention optimizes the traditional attention mechanism used in models like transformers for better efficiency and scalability. By reducing computational complexity, it allows for processing of larger inputs while maintaining the quality of attention scores. This is particularly useful in applications with massive datasets, ensuring that models can still perform effectively without overwhelming computational resources.