The release of Mamba 6 months ago has sparked significant interest within the machine learning community. The focus on efficient sequence models has led to exciting advancements in various fields. The introduction of Mamba-2 aims to address some limitations of the first version, particularly in terms of efficiency and training speed. The new state space model variant, SSD, offers a novel approach to sequence modeling by incorporating elements of both structured state space models and attention mechanisms. By leveraging matrix multiplications and introducing scalar structure to the recurrent matrices, Mamba-2 aims to strike a balance between efficiency and performance, offering a fresh perspective on sequence modeling.
https://tridao.me/blog/2024/mamba2-part1-model/