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  VCoder-DS LLaVA-1.5-7b was trained on COST training dataset in December 2023. It uses the pretrained [LLaVA-1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b) model weights. It was introduced by Jain et al. in [this repository](https://github.com/SHI-Labs/VCoder).
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- VCoder is an adapter for improving existing Vision LLMs at object-level perception tasks with the use of perception modalities as control inputs while retaining performance on other tasks.
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  ![img](https://praeclarumjj3.github.io/vcoder/vcoder.svg)
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  ```bibtex
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  @article{jain2023vcoder,
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- title={{VCoder: Versatile Visual Encoder for Accurate Object-Level Perception with Large Language Models}},
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  author={Jitesh Jain and Jianwei Yang and Humphrey Shi},
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  journal={arXiv},
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  year={2023}
 
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  VCoder-DS LLaVA-1.5-7b was trained on COST training dataset in December 2023. It uses the pretrained [LLaVA-1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b) model weights. It was introduced by Jain et al. in [this repository](https://github.com/SHI-Labs/VCoder).
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+ VCoder is an adapter for improving existing Multimodal LLMs at object-level perception tasks with the use of perception modalities as control inputs while retaining performance on other tasks.
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  ![img](https://praeclarumjj3.github.io/vcoder/vcoder.svg)
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  ```bibtex
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  @article{jain2023vcoder,
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+ title={{VCoder: Versatile Vision Encoders for Multimodal Large Language Models}},
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  author={Jitesh Jain and Jianwei Yang and Humphrey Shi},
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  journal={arXiv},
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  year={2023}