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REAM: Merging Improves Pruning of Experts in LLMs
arXiv – CS AI|Saurav Jha, Maryam Hashemzadeh, Ali Saheb Pasand, Ali Parviz, Min-Joong Lee, Boris Knyazev|
🤖AI Summary
Researchers propose REAM (Router-weighted Expert Activation Merging), a new method for compressing large language models that groups and merges expert weights instead of pruning them. The technique preserves model performance better than existing pruning methods while reducing memory requirements for deployment.
Key Takeaways
- →REAM merges expert weights in Mixture-of-Experts models rather than removing them entirely like traditional pruning methods.
- →The approach better preserves original model performance compared to REAP and other baseline compression techniques.
- →Results show a trade-off between multiple-choice and generative task performance that depends on calibration data composition.
- →REAM often matches or outperforms uncompressed models while significantly reducing memory requirements.
- →The method addresses deployment challenges for models with hundreds of billions of parameters.
#large-language-models#model-compression#mixture-of-experts#machine-learning#ai-efficiency#memory-optimization#model-pruning
Read Original →via arXiv – CS AI
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