The MAMBA (Multi-Array Memory-Based Architecture) architecture is an innovative approach in the field of large language models (LLMs). It introduces a new model block inspired by structured state space models (SSMs) and Transformer models, focusing on efficient hardware acceleration and fast inference speeds. Here's a detailed technical overview of how MAMBA works:
- Selective State Space Models (SSMs): MAMBA utilizes selective SSMs to compress and selectively remember information in long sequences. This approach contrasts with attention mechanisms in traditional models, which do not compress context and can be computationally expensive.
Selective State Space Models (SSMs) enhance traditional state space approaches by integrating a unique selective mechanism that dynamically filters and retains information throughout a sequence. This mechanism works by evaluating each element of the sequence (like words in a text) and deciding whether to incorporate it into the model's current state based on its relevance t