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by VictorSanh - opened
README.md
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# LVM
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This is the model implementation of the CVPR 2024 'Sequential Modeling Enables Scalable Learning for Large Vision Models'. (https://arxiv.org/abs/2312.00785)
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LVM is a vision pretraining model that converts various kinds of visual data into visual sentences and performs next-token prediction autoregressively. It is compatible with both GPU and TPU.
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LVM is built on top of [OpenLLaMA](https://github.com/openlm-research/open_llama) (an autoregressive model) and [OpenMuse](https://github.com/huggingface/open-muse) (a VQGAN that converts images into visual tokens).
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This was trained in collaboration with HuggingFace. Thanks [Victor Sanh](https://huggingface.co/VictorSanh) for the support in this project.
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journal={arXiv preprint arXiv:2312.00785},
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year={2023}
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}
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\`\`\`
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---
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license: apache-2.0
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tags:
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- image
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- video
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inference: false
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---
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# LVM
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This is the model implementation of the CVPR 2024 'Sequential Modeling Enables Scalable Learning for Large Vision Models'. (https://arxiv.org/abs/2312.00785)
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LVM is a vision pretraining model that converts various kinds of visual data into visual sentences and performs next-token prediction autoregressively. It is compatible with both GPU and TPU.
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You can try out the demo [here](https://huggingface.co/spaces/Emma02/LVM).
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LVM is built on top of [OpenLLaMA](https://github.com/openlm-research/open_llama) (an autoregressive model) and [OpenMuse](https://github.com/huggingface/open-muse) (a VQGAN that converts images into visual tokens).
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This was trained in collaboration with HuggingFace. Thanks [Victor Sanh](https://huggingface.co/VictorSanh) for the support in this project.
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journal={arXiv preprint arXiv:2312.00785},
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year={2023}
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}
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\`\`\`
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