Update README.md

README.md

@@ -3,29 +3,23 @@ license: mit
 tags:
 - biology
 ---
-
 <div align="center">
-  <!-- TODO: Uncomment and set YOUR_IMAGE_URL -->
-  <!-- <img src="YOUR_IMAGE_URL" width="100%" alt="OneGenome-Rice (OGR)" /> -->
-  *(Banner / architecture figure: add URL, then uncomment the line above.)*
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/65a9e8563b9e1f0f308378b7/H2qI2OOSl-KqOlg01fRGR.png" width="100%" />
 </div>
 
-# OneGenome-Rice (OGR)
+# OneGenomeRice (OGR)
 
 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.
 
-For instructions, details, and examples, see the project repository: *[TODO: GitHub or documentation URL](https://github.com/TODO/TODO)*.
-
-The table below summarizes training scale and key hyperparameters. **Trained Tokens** follows the **Training Process** section (sequence curriculum and CPT).
+For instructions, details, and examples, see the project repository[OGR GitHub](https://github.com/zhejianglab/OneGenomeRice).
 
-<!-- If you ship multiple sizes (e.g. Small / Large), duplicate the table and add columns. -->
+The table below summarizes training scale and key hyperparameters.
 
-| Model Specification | OneGenome-Rice (OGR) |
+| Model Specification | OGR |
 | --- | --- |
 | **Model Scale** | |
 | Total Parameters | 1.25B |
 | Activated Parameters | 0.33B |
-| Trained Tokens | ~490B (sequence curriculum) + ~104B (CPT) |
 | **Architecture** | |
 | Architecture | MoE |
 | Number of Experts | 8 |