This research introduces an adaptive feature-based low-rank compression technique for large language models that reduces computational burden while maintaining performance.

• Compresses LLMs by decomposing weight matrices into products of smaller low-rank matrices
• Uses Bayesian optimization to efficiently identify optimal compression configurations
• Achieves significant parameter reduction with minimal performance loss
• Addresses the critical challenge of balancing model scale and computational efficiency

This engineering innovation enables more efficient deployment of large language models across resource-constrained environments, making advanced AI more accessible and practical for business applications.

Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization