Saving 77% of the Parameters in Large Language Models Technical Report

원문 초록

This technical report demonstrates that large language models (LLMs) can maintain their learning capacity while reducing their non-embedding parameters by up to 77%. We achieve this by adapting a parameter reduction technique originally developed for computer vision, replacing dense layers with an optimized subnetwork that contains grouped pointwise convolutions. Using Microsoft's phi-3-mini-4k-instruct as our baseline, we show that our optimized model (kphi-3) achieves comparable validation loss while using only 15-23% of the original non-embedding parameters. All experiments were conducted on a single NVIDIA L2 GPU within a 3-day timeframe, supporting the democratization of AI research. Our findings suggest that current LLM architectures may be substantially overparameterized, opening possibilities for more efficient model training and deployment.