metadata
license: apache-2.0
tags:
- biology
OneGenome-Rice (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 OGR GitHub.
The table below summarizes training scale and key hyperparameters.
| Model Specification | OneGenomeRice (OGR) |
|---|---|
| Model Scale | |
| Total Parameters | 1.25B |
| Activated Parameters | 0.33B |
| Architecture | |
| Architecture | MoE |
| Number of Experts | 8 |
| Selected Experts per Token | 2 |
| Number of Layers | 12 |
| Attention Hidden Dimension | 1024 |
| Number of Attention Heads | 16 (GQA, 8 KV groups) |
| MoE Hidden Dimension (per Expert) | 4096 |
| Vocabulary Size | 128 (padded) |
| Context Length | up to 1Mb |