7/ 🧩This Is Not Pruning
TL;DR · AI Summary
ZEDA is a novel MoE technique that uses self-distillation to dynamically skip experts, improving inference efficiency and giving models compute budget awareness.
Key Takeaways
- ZEDA skips half of the experts using self-distillation, enhancing inference effi
- The method gives models 'compute budget awareness', determining whether each tok
- Published on arXiv, the paper proposes a Post-Trained MoE architecture optimizat
Outline
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Introduces how ZEDA gives MoE models compute budget awareness.
ZEDA skips half of the experts using self-distillation to reduce computational overhead.
This approach significantly improves inference speed for large-scale language models.
The paper introduces Post-Trained MoE to address static activation issues in traditional MoE.
Dynamic activation allows models to flexibly adjust activated experts based on input.
Mindmap
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- ZEDA 动态专家跳过技术
- 核心机制
- 自蒸馏
- 跳过一半专家
- 优势
- 提升推理效率
- 算力预算意识
Highlights
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ZEDA gives MoE models 'compute budget awareness', deciding not only what to answer but also whether each token deserves serious consideration.
Post-Trained MoE Can Skip Half Experts via Self-Distillation proposes skipping half of the experts using self-distillation.
This method enhances inference efficiency while maintaining performance and reducing resource consumption.
AI Will on X: "7/ 🧩 This is Not Pruning. ZEDA Makes MoE Have 'Computational Budget Awareness'. Future models will not only decide what to answer, but also determine whether each token is worth serious consideration. Paper: Post-Trained MoE Can Skip Half Experts via Self-Distillation https://t.co/KYdgJUIr9o" / X
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7/ 
This is Not Pruning. ZEDA Makes MoE Have 'Computational Budget Awareness'. Future models will not only decide what to answer, but also determine whether each token is worth serious consideration. Paper: Post-Trained MoE Can Skip Half Experts via Self-Distillation
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