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README.md
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@@ -15,7 +15,9 @@ pipeline_tag: image-feature-extraction
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/re658pVjHaJEnJerlmRco.webp" alt="Image Description" width="300" height="300">
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\[
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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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@@ -83,6 +85,12 @@ If you find this project useful in your research, please consider citing:
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journal={arXiv preprint arXiv:2312.14238},
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year={2023}
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}
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```
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/re658pVjHaJEnJerlmRco.webp" alt="Image Description" width="300" height="300">
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</p>
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[\[π Blog\]](https://internvl.github.io/blog/) [\[π InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238) [\[π InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821) [\[π¨οΈ Chat Demo\]](https://internvl.opengvlab.com/)
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[\[π€ HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[π Quick Start\]](#model-usage) [\[π Community-hosted API\]](https://rapidapi.com/adushar1320/api/internvl-chat) [\[π δΈζ解读\]](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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journal={arXiv preprint arXiv:2312.14238},
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year={2023}
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}
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@article{chen2024far,
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title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
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author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
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journal={arXiv preprint arXiv:2404.16821},
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year={2024}
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}
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```
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