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  ## R1-Onevision
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  [\[📂 GitHub\]](https://github.com/Fancy-MLLM/R1-Onevision)[\[📝 Report\]](https://yangyi-vai.notion.site/r1-onevision?pvs=4)
 
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  [\[🤗 HF Dataset\]](https://huggingface.co/datasets/Fancy-MLLM/R1-onevision) [\[🤗 Reasoning Benchmark\]](https://huggingface.co/datasets/Fancy-MLLM/R1-OneVision-Bench) [\[🤗 HF Demo\]](https://huggingface.co/spaces/Fancy-MLLM/R1-OneVision)
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  ## Model Overview
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  We are also focused on integrating Chinese multimodal reasoning CoT data into the training process. By adding this language-specific dataset, we aim to improve the model’s capability to perform reasoning tasks in Chinese, expanding its multilingual and multimodal reasoning proficiency.
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  4. **Release of the 3B Model**
 
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  We are working on the release of a smaller, more efficient 3B model, which is designed to provide a balance between performance and resource efficiency. This model aims to deliver strong multimodal reasoning capabilities while being more accessible and optimized for environments with limited computational resources, offering a more compact alternative to the current 7B model.
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  ## R1-Onevision
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  [\[📂 GitHub\]](https://github.com/Fancy-MLLM/R1-Onevision)[\[📝 Report\]](https://yangyi-vai.notion.site/r1-onevision?pvs=4)
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+ [\[🤗 HF Demo\]](https://huggingface.co/spaces/Fancy-MLLM/R1-Onevision)
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  [\[🤗 HF Dataset\]](https://huggingface.co/datasets/Fancy-MLLM/R1-onevision) [\[🤗 Reasoning Benchmark\]](https://huggingface.co/datasets/Fancy-MLLM/R1-OneVision-Bench) [\[🤗 HF Demo\]](https://huggingface.co/spaces/Fancy-MLLM/R1-OneVision)
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  ## Model Overview
 
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  We are also focused on integrating Chinese multimodal reasoning CoT data into the training process. By adding this language-specific dataset, we aim to improve the model’s capability to perform reasoning tasks in Chinese, expanding its multilingual and multimodal reasoning proficiency.
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  4. **Release of the 3B Model**
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  We are working on the release of a smaller, more efficient 3B model, which is designed to provide a balance between performance and resource efficiency. This model aims to deliver strong multimodal reasoning capabilities while being more accessible and optimized for environments with limited computational resources, offering a more compact alternative to the current 7B model.
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