This research presents a novel automated compression approach that reduces the size of large language models while maintaining or improving their performance.

• Frames compression as a neural architecture search problem that automatically prunes less important model components
• Achieves significant size reduction while maintaining or improving downstream task performance
• Reduces inference costs which accumulate substantially over a model's lifecycle
• Makes powerful AI more accessible by lowering computational requirements

This engineering breakthrough addresses one of AI's critical challenges: making sophisticated models practical for wider deployment without sacrificing capabilities.

Compressing Large Language Models with Automated Sub-Network Search