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New distillation method transfers Transformer knowledge to Mamba models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for distilling knowledge from Transformer models into State Space Models (SSMs) like Mamba. This two-stage approach involves first distilling knowledge into a linearized Attention adaptation and then into an adapted Mamba model. The distilled Mamba model, using this recipe, successfully retained most of the performance of the original Pythia-1B Transformer, achieving a comparable perplexity score. AI

IMPACT Enables leveraging existing Transformer knowledge for training more efficient State Space Models.

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

Read on arXiv cs.CL →

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

New distillation method transfers Transformer knowledge to Mamba models

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The cluster contains an academic 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. arXiv cs.CL TIER_1 English(EN) · Abhinav Moudgil, Ningyuan Huang, Eeshan Gunesh Dhekane, Pau Rodr\'iguez, Luca Zappella, Federico Danieli ·

    Attention to Mamba: A Recipe for Cross-Architecture Distillation

    arXiv:2604.14191v2 Announce Type: replace Abstract: 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 …

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