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  # InternViT-6B-448px-V1-2
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- [\[πŸ†• Blog\]](https://internvl.github.io/blog/) [\[πŸ“œ InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238) [\[πŸ“œ InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821) [\[πŸ—¨οΈ Chat Demo\]](https://internvl.opengvlab.com/)
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- [\[πŸ€— HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[πŸš€ Quick Start\]](#model-usage) [\[🌐 Community-hosted API\]](https://rapidapi.com/adushar1320/api/internvl-chat) [\[πŸ“– 中文解读\]](https://zhuanlan.zhihu.com/p/675877376)
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  We release our new InternViT weights as InternViT-6B-448px-V1-2. The continuous pre-training of the InternViT-6B model is involved in the [InternVL 1.2](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2) update. Specifically, we increased the resolution of InternViT-6B from 224 to 448 and integrated it with [Nous-Hermes-2-Yi-34B]((https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B).
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  To equip the model with high-resolution processing and OCR capabilities, both the vision encoder and the MLP were activated for training, utilizing a mix of image captioning and OCR-specific datasets.
 
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  # InternViT-6B-448px-V1-2
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+ [\[πŸ“‚ GitHub\]](https://github.com/OpenGVLab/InternVL) [\[πŸ†• Blog\]](https://internvl.github.io/blog/) [\[πŸ“œ InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238) [\[πŸ“œ InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821)
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+ [\[πŸ—¨οΈ Chat Demo\]](https://internvl.opengvlab.com/) [\[πŸ€— HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[πŸš€ Quick Start\]](#quick-start) [\[πŸ“– 中文解读\]](https://zhuanlan.zhihu.com/p/706547971) [\[πŸ“– Documents\]](https://internvl.readthedocs.io/en/latest/)
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  We release our new InternViT weights as InternViT-6B-448px-V1-2. The continuous pre-training of the InternViT-6B model is involved in the [InternVL 1.2](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2) update. Specifically, we increased the resolution of InternViT-6B from 224 to 448 and integrated it with [Nous-Hermes-2-Yi-34B]((https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B).
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  To equip the model with high-resolution processing and OCR capabilities, both the vision encoder and the MLP were activated for training, utilizing a mix of image captioning and OCR-specific datasets.