[R] The Post-Transformer Era: State Space Models, Mamba, and What Comes After Attention
A new architecture is challenging the transformer's dominance. Here's what you need to know.
Deep Dive
A new viral guide details the rise of State Space Models (SSMs) like Mamba, which use selective state spaces to achieve linear scaling in sequence length, unlike transformers' quadratic scaling. The post compares when to use pure SSMs, transformers, or hybrid models, and highlights production-ready options. This signals a potential paradigm shift as the community explores what comes after the attention mechanism that has powered AI for years.
Why It Matters
This could lead to faster, cheaper, and more efficient models that handle long contexts with ease.