OMP-MoE: Efficient Expert Pruning for Mixture-of-Experts LLMs via Orthogonal Matching Pursuit
Leveraging Orthogonal Matching Pursuit to streamline large language model architecture efficiency.
Mixture-of-Experts (MoE) models enable efficient scaling of large language models but face critical deployment challenges due to massive memory requirements. Existing pruning methods either incur prohibitive search costs or neglect the dynamic interdependencies between experts. T...
ArXiv CS.LG
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OMP-MoE introduces a mathematically rigorous approach to expert pruning that significantly lowers the memory footprint of massive models without sacrificing performance. By addressing the complex interdependencies between experts, this method paves the way for deploying high-performance LLMs on resource-constrained hardware.
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