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  - biology
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  ---
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  <div align="center">
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- <img src="https://cdn-uploads.huggingface.co/production/uploads/65a9e8563b9e1f0f308378b7/H2qI2OOSl-KqOlg01fRGR.png" width="100%" />
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  </div>
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  # OneGenomeRice (OGR)
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  OGR is a foundational model for AI-driven precision breeding and functional genomics in rice. It is a generative genomic foundation model trained to process DNA sequences up to **1 million** base pairs in length, with **1.25B** total parameters and a **Mixture-of-Experts (MoE)** architecture. It was pre-trained on a curated corpus of **422** rice genomes spanning cultivated and wild *Oryza* diversity.
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- For instructions, details, and examples, see the project repository[OGR GitHub](https://github.com/zhejianglab/OneGenomeRice).
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  The table below summarizes training scale and key hyperparameters.
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  - biology
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  <div align="center">
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/65a9e8563b9e1f0f308378b7/H2qI2OOSl-KqOlg01fRGR.png" width="80%" />
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  </div>
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  # OneGenomeRice (OGR)
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  OGR is a foundational model for AI-driven precision breeding and functional genomics in rice. It is a generative genomic foundation model trained to process DNA sequences up to **1 million** base pairs in length, with **1.25B** total parameters and a **Mixture-of-Experts (MoE)** architecture. It was pre-trained on a curated corpus of **422** rice genomes spanning cultivated and wild *Oryza* diversity.
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+ For instructions, details, and examples, see the project repository [OGR GitHub](https://github.com/zhejianglab/OneGenomeRice).
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  The table below summarizes training scale and key hyperparameters.
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