Apple publishes a recipe for distilling Transformer attention into Mamba architectures
zerop · hn · 2026-11-11
Apple's ML research team released Attention to Mamba, a recipe for cross-architecture distillation that transfers attention-model capabilities into Mamba-style state space models.
- Motivation: Mamba-like linear architectures are efficient at inference but typically lag same-size attention models when trained from scratch.
- Approach: The post details a reusable distillation pipeline so the Mamba student inherits knowledge and behavior from a Transformer teacher.
- Why it matters: If it works broadly, it points to combining attention-level quality with linear-architecture efficiency.
Full write-up on the Apple Machine Learning Research blog.
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