Efficient Hybrid Attention: KL-Guided Layer Selection for Model Distillation

This research explores a method to optimize hybrid attention models through knowledge distillation, focusing on layer selection guided by the Kullback-Leibler divergence. The approach potentially leads to more efficient models while preserving performance, which is valuable for resource-constrained applications.