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Use HF papers (#38184)

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Quentin Gallouédec
2025-06-13 13:07:09 +02:00
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docs/source/en/model_doc/llava_onevision.md
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## Overview
The LLaVA-OneVision model was proposed in [LLaVA-OneVision: Easy Visual Task Transfer](https://arxiv.org/abs/2408.03326) by <Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li
The LLaVA-OneVision model was proposed in [LLaVA-OneVision: Easy Visual Task Transfer](https://huggingface.co/papers/2408.03326) by <Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li
LLaVA-OneVision is a Vision-Language Model that can generate text conditioned on one or several images/videos. The model consists of SigLIP vision encoder and a Qwen2 language backbone. The images are processed with anyres-9 technique where the image is split into 9 patches to better process high resolution images and capture as much details as possible. However, videos are pooled to a total sequence length of 196 tokens each frame for more memory efficient computation. LLaVA-OneVision is available in three sizes: 0.5B, 7B and 72B and achieves remarkable performance on benchmark evaluations.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/llava-ov-acrhitecture.png"
alt="drawing" width="600"/>
<small> LLaVA-OneVision architecture. Taken from the <a href="https://arxiv.org/abs/2408.03326">original paper.</a> </small>
<small> LLaVA-OneVision architecture. Taken from the <a href="https://huggingface.co/papers/2408.03326">original paper.</a> </small>
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