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README.md
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---
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pipeline_tag: image-text-to-text
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inference: false
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license: apache-2.0
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---
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<br>
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<br>
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# LLaVA-Reasoner Model Card
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## Model details
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**Model type:**
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LLaVA-Reasoner is an open-source image vision language model, fine-tuned from GPT4-o distilled chain-of-thought (CoT) reasoning data.
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This model is the **DPO-preview** version, trained from LLaVA-Reasoner-SFT-preview based on model generated CoT evaluated by outcome reward.
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Base LLM: [Lin-Chen/open-llava-next-llama3-8b](https://huggingface.co/Lin-Chen/open-llava-next-llama3-8b)
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**Model date:**
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Trained on Sep, 2024.
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**Paper or resources for more information:**
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Paper: https://arxiv.org/abs/2410.16198
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Code: https://github.com/RifleZhang/LLaVA-Reasoner-DPO/tree/main
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## License
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[Lin-Chen/open-llava-next-llama3-8b](https://huggingface.co/Lin-Chen/open-llava-next-llama3-8b) license.
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**Where to send questions or comments about the model:**
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https://github.com/RifleZhang/LLaVA-Reasoner-DPO/issues
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## Intended use
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**Primary intended uses:**
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Image CoT reasoning
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**Primary intended users:**
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Researchers in artificial intelligence, large multimodal model, etc.
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## Training dataset
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[ShareGPT4o-Reasoning](https://huggingface.co/datasets/Share4oReasoning/sft_data) dataset.
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## Evaluation
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Follow https://github.com/RifleZhang/LLaVA-Reasoner-DPO/blob/main/README.md
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## citation
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```
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@article{zhang2024improve,
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title={Improve vision language model chain-of-thought reasoning},
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author={Zhang, Ruohong and Zhang, Bowen and Li, Yanghao and Zhang, Haotian and Sun, Zhiqing and Gan, Zhe and Yang, Yinfei and Pang, Ruoming and Yang, Yiming},
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journal={arXiv preprint arXiv:2410.16198},
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year={2024}
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}
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```
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