Compression Strategies for Efficient Multimodal LLMs in Medical Contexts

TL;DR

This paper explores compression techniques like pruning and quantization to make multimodal large language models more efficient for medical use, achieving significant memory reduction with improved performance.

Contribution

It introduces a novel layer selection method for pruning and analyzes quantization techniques, optimizing the compression pipeline for medical multimodal LLMs.

Findings

Memory usage reduced by 70%

Model performance increased by 4%

Enabled 7B parameter models to run within 4 GB VRAM

Abstract

Multimodal Large Language Models (MLLMs) hold huge potential for usage in the medical domain, but their computational costs necessitate efficient compression techniques. This paper evaluates the impact of structural pruning and activation-aware quantization on a fine-tuned LLAVA model for medical applications. We propose a novel layer selection method for pruning, analyze different quantization techniques, and assess the performance trade-offs in a prune-SFT-quantize pipeline. Our proposed method enables MLLMs with 7B parameters to run within 4 GB of VRAM, reducing memory usage by 70% while achieving 4% higher model performance compared to traditional pruning and quantization techniques in the same compression ratio.