ProxSparse introduces a novel framework that intelligently prunes Large Language Models using regularized optimization to reduce size while preserving performance.

• Replaces heuristic-based pruning with a learning-based approach that considers global model feedback
• Implements semi-structured sparsity that maintains hardware compatibility
• Achieves significant model compression with minimal performance degradation
• Addresses critical deployment challenges for resource-constrained environments

This engineering breakthrough makes LLMs more accessible for real-world applications by reducing computational requirements and operational costs without sacrificing capabilities.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs