FlexiGPT introduces a principled methodology for creating memory-efficient large language models that can operate on resource-constrained devices.

• Selectively prunes model blocks based on calculated importance scores
• Replaces pruned blocks with innovative low-parameter alternatives
• Implements weight-sharing mechanisms to maintain performance while reducing size
• Enables deployment of powerful language models on memory-limited hardware

This research addresses a critical engineering challenge: how to make increasingly complex AI systems accessible across a broader range of devices without compromising their capabilities. By enabling more efficient deployment, FlexiGPT helps bridge the gap between cutting-edge AI research and practical applications.

FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing