Multimodal SLMs: Qwen 2.5 and Air-Gapped Document Intelligence for Confidential Data

I’m helping Branches of Hope (a charity in Hong Kong dedicated to assisting refugees) effectively digitise their confidential refugee records into structured data. As a result, I’ve built a solution that can be air-gapped, on-prem utilising the open-source Qwen2.5-VL-7B model. Through this, I found that even SLMs are far more effective at OCR than traditional deep-learning based approaches (e.g. Tesseract). In fact they are so good that the guidance you provide in the prompt is vital.

I’ve now updated to using the latest Qwen3-VL-8B-Instruct model, and I will also highlight the changes in power that the latest open-source models provide.