Recent years have witnessed an increasing interest in image-text contrastive modeling, exemplified by models such as Contrastive Language-Image Pretraining (CLIP). In this paper, we propose the TernaryCLIP, a lightweight computational framework that converts connection weights of both vision and text encoders of CLIP into the ternary format, instead of full-precision or floating ones. TernaryCLIP incorporates quantization-aware training and distillation modules, preventing precision degradation and enabling low-cost and high-efficiency computations. Comprehensive experiments demonstrate that TernaryCLIP can achieve up to 99\% ternarized weights with 1.58-bit representation, 16.98 × compression ratio, 2.3 × inference acceleration, 16 × storage reduction, 10 × memory optimization, and 60\% sparsity while maintaining promising performance on zero-shot image classification and image-text retrieval tasks across 41 commonly used datasets. Our work highlights the feasibility of extreme quantization for large multimodal models, supporting effective and efficient deployment on resource-constrained devices. The model and code can be accessed from Hugging Face and GitHub.
@article{arxiv.2510.21879,
title = {TernaryCLIP: Efficiently Compressing Vision-Language Models with Ternary Weights and Distilled Knowledge},
author = {Shu-Hao Zhang and Wei-Cheng Tang and Chen Wu and Peng Hu and Nan Li and Liang-Jie Zhang and Qi Zhang and Shao-Qun Zhang},
journal= {arXiv preprint arXiv:2510.21879},
year = {2025}
}