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cloud.google.com·7h ago
AI Research
arXiv CS.AI·25d ago·5 min read
Higher-order pruning of experts in mixture-of-experts language models
TL;DR
Higher-Order Pruning of Experts (HOPE) improves the efficiency of Mixture-of-Experts (MoE) language models by considering expert interactions, leading to better pruning decisions and reduced memory usage.
✦ Why It Matters
Implement HOPE in your MoE models to achieve better performance with reduced memory usage.
Key Takeaways
- Consider expert interactions for more effective pruning decisions.
- Use HOPE to reduce memory usage in large language models.
- Evaluate model performance on complex tasks after pruning.
- Aim for aggressive compression while maintaining accuracy.
How It Works
HOPE employs a second-order pruning objective that accounts for the interactions between experts, rather than treating their contributions as purely additive. This approach allows for more informed pruning decisions, preserving the cooperative structure of the experts.