Mamba Models a possible replacement for Transformers?

The quest for more efficient and faster deep learning models has led to the development of various alternatives to Transformers, one of which is the Mamba model. This paper provides a comprehensive comparison between Mamba models and Transformers, focusing on their architectural differences, performance metrics, and underlying mechanisms. It analyzes and synthesizes findings from extensive research conducted by various authors on these models. The synergy between Mamba models and the SciPy ecosystem enhances their integration into science. By providing an in-depth comparison using Python and its scientific ecosystem, this paper aims to clarify the strengths and weaknesses of Mamba models relative to Transformers. It offers the results obtained along with some thoughts on the possible ramifications for future research and applications in a range of academic and professional fields.