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arXiv · 2512.04032

jina-vlm: Small Multilingual Vision Language Model

Andreas Koukounas·Georgios Mastrapas·Florian Hönicke·Sedigheh Eslami·Guillaume Roncari·Han Xiao
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Abstract

We present jina-vlm, a token-efficient 2.4B parameter vision-language model that achieves state-of-the-art multilingual VQA performance among open 2B-scale VLMs. The model couples a SigLIP2 vision encoder with a Qwen3 language decoder and makes use of image tiling and attention-pooling for token-efficient processing of arbitrary-resolution images. To understand the contribution of different training data categories, we conduct a leave-one-out data mixture ablation study-systematically removing task, domain, modality, and language categories-to diagnose which data types are necessary versus redundant and whether task benefits transfer across domains. Model weights and code are publicly released at https://huggingface.co/jinaai/jina-vlm.

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Andreas Koukounas, Georgios Mastrapas, Florian Hönicke, Sedigheh Eslami, Guillaume Roncari, Han Xiao. 2025-12-03. jina-vlm: Small Multilingual Vision Language Model. https://arxiv.org/abs/2512.04032

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