Jina-VLM: Small Multilingual Vision Language Model
We present Jina-VLM, a 2.4B parameter vision-language model that achieves state-of-the-art multilingual visual question answering among open 2B-scale VLMs. The model couples a SigLIP2 vision encoder with a Qwen3 language backbone through an attention-pooling connector that enables token-efficient processing of arbitrary-resolution images. Across standard VQA benchmarks and multilingual evaluations, Jina-VLM outperforms comparable models while preserving competitive text-only performance.
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Andreas Koukounas
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2025-12-04T13:48:20.346Z
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Andreas Koukounas
koukandre
user
koukandre
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Georgios Mastrapas
admin_assigned
2025-12-04T13:50:03.276Z
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Georgios Mastrapas
gmastrapas
user
gmastrapas
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Florian Hönicke
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Sedigheh Eslami
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Guillaume Roncari
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Scott Martens
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Han Xiao
admin_assigned
2025-12-04T13:48:28.867Z
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Han Xiao
hanxiao
user
hanxiao
Jina-VLM: Small Multilingual Vision Language Model
We present Jina-VLM, a 2.4B parameter vision-language model that achieves state-of-the-art multilingual visual question answering among open 2B-scale VLMs. The model couples a SigLIP2 vision encoder with a Qwen3 language backbone through an attention-pooling connector that enables token-efficient processing of arbitrary-resolution images. Across standard VQA benchmarks and multilingual evaluations, Jina-VLM outperforms comparable models while preserving competitive text-only performance.
Jina-VLM, a 2.4B parameter vision-language model, achieves top performance in multilingual visual question answering using a SigLIP2 vision encoder and Qwen3 language backbone with an attention-pooling connector.
SigLIP2 Qwen3 attention-pooling connector token-efficient processing arbitrary-resolution images multilingual visual question answering VQA benchmarks text-only performance