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Apple researchers distill Transformer knowledge into Mamba architecture

By PulseAugur Editorial · [1 sources] ·

Researchers from Apple Inc. have developed a new method for distilling knowledge from Transformer models into Mamba-like architectures. Their two-stage approach involves first distilling knowledge into a linearized Attention model using a kernel trick, and then distilling that into an adapted Mamba model. This technique allows the distilled Mamba model to retain most of the performance of the original Transformer, achieving a perplexity of 14.01 compared to the teacher's 13.86, though some gaps appear on tasks requiring strong in-context recall. AI

IMPACT This research offers a method to leverage existing Transformer knowledge for Mamba-like architectures, potentially improving efficiency and adoption of State Space Models.

RANK_REASON The cluster contains a research paper detailing a new method for cross-architecture distillation of AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Apple Machine Learning Research →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Apple researchers distill Transformer knowledge into Mamba architecture

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The cluster contains a research paper detailing a new method for cross-architecture distillation of AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Apple Machine Learning Research TIER_1 English(EN) ·

    Attention to Mamba: A Recipe for Cross-Architecture Distillation

    State Space Models (SSMs) such as Mamba have become a popular alternative to Transformer models, due to their reduced memory consumption and higher throughput at generation compared to their Attention-based counterparts. On the other hand, the community has built up a considerabl…

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