Mamba: Linear-Time Sequence Modeling with Selective State Spaces

This research paper describes a new approach to sequence modeling called Mamba, which is designed to be faster and more efficient than the commonly used Transformer models. Mamba is based on a different mathematical framework called selective state space models (SSMs), which allow the model to choose which parts of a sequence to focus on, similar to how people can ignore distractions and concentrate on important information. Mamba was tested on different tasks like predicting the next word in a sentence, analyzing DNA sequences, and generating realistic audio, and it outperformed existing models, especially on longer sequences. The key advantage of Mamba is that it can process sequences in linear time, meaning the time it takes to process a sequence increases proportionally to th ...