BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion
Sike Xiang, Shuang Chen, Amir Atapour–Abarghouei · 2025
As multimodal large language models (MLLMs) advance, their large-scale architectures pose challenges for deployment in resource-constrained environments.In the age of large models, where energy efficiency, computational scalability and environmental sustainability are paramount, the development of lightweight and high-performance models is critical for real-world applications.As such, we propose a lightweight MLLM framework for end-to-end visual question answering.Our proposed approach centres on BreezeCLIP, a compact yet powerful vision-language encoder optimised for efficient multimodal understanding.With only 1.2 billion parameters overall, our model significantly reduces computational cost while achieving performance comparable to standard-size MLLMs.Experiments conducted on multiple datasets further validate its effectiveness in balancing accuracy and efficiency.The modular and extensible design enables generalisation to broader multimodal tasks.The proposed lightweight vision-language framework is denoted as BcQLM (BreezeCLIPenhanced Q-Gated Multimodal Language Model).It offers a promising path toward deployable MLLMs under practical hardware constraints.The source code is available at https://github.com/thico0224/BcQLM.