jina-vlm: Small Multilingual Vision Language Model

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 this https URL.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2512.04032 [cs.CL]

(or arXiv:2512.04032v4 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2512.04032 Focus to learn more arXiv-issued DOI via DataCite

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