Mamba {NLP}

Description

Mamba is a next-generation sequence model that combines efficiency and state-of-the-art performance without relying on attention or MLP blocks. Designed to overcome the computational inefficiency of Transformers on long sequences, Mamba uses Selective Structured State Space Models (SSMs) with input-dependent parameters, enabling content-based reasoning and selective information propagation for discrete modalities like language.

• Fast and Scalable: Achieves 5x higher inference throughput than Transformers with linear memory and computational scaling for sequences up to a million tokens.
• Simplified Design: Optimized with a hardware-aware parallel algorithm in recurrent mode, eliminating attention mechanisms entirely.
• Top-Tier Performance: Delivers state-of-the-art results across diverse modalities such as language, audio, and genomics. In language modeling, the Mamba-3B model outperforms same-size Transformers and matches Transformers twice its size.