---
title: OneGenomeRice
canonical_url: "https://www.modelscope.cn/models/zhejianglab/OneGenomeRice"
md_url: "https://www.modelscope.cn/models/zhejianglab/OneGenomeRice.md"
repository: zhejianglab/OneGenomeRice
chinese_name: OneGenome-Rice
last_updated: 2026-04-23
license: "Apache License 2.0"
model_type:
  - mixtral
architectures:
  - MixtralForCausalLM
base_model_relation: finetune
parameters: 1.2B
tensor_type:
  - F32
library_name:
  - safetensors
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 42
stars: 1
tags:
  - biology
---

# OneGenomeRice

> OneGenomeRice - zhejianglab 在 ModelScope 开源的模型。OneGenome-Rice (OGR)

zhejianglab/OneGenomeRice 是 ModelScope 魔搭社区上的 1.2B 参数机器学习模型，采用 Apache License 2.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: zhejianglab/OneGenomeRice
- **License**: Apache License 2.0
- **Parameters**: 1.2B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: biology
- **Downloads**: 42
- **Stars**: 1
- **Last updated**: 2026-04-23

Source: https://www.modelscope.cn/models/zhejianglab/OneGenomeRice

---

<div align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/65a9e8563b9e1f0f308378b7/H2qI2OOSl-KqOlg01fRGR.png" width="50%" />
</div>


# OneGenome-Rice (OGR)

OGR 是一个专门为水稻 AI 精准育种和功能基因组学设计的**基础模型**。作为一个生成式基因组大模型，它能够处理长度达 **100 万**个碱基对的 DNA 序列，拥有 **12.5 亿**参数量，并采用了 **混合专家网络 (MoE)** 架构。该模型在涵盖栽培稻及野生稻多样性的 **422** 个精选水稻基因组语料库上进行了预训练。

有关指令、详细信息和示例，请参阅[OGR GitHub](https://github.com/zhejianglab/OneGenome-Rice)仓库。

下表总结了模型的训练规模和关键超参数。


<div align="center">

<table>
  <thead>
    <tr>
      <th align="center"><strong>Model Specification</strong></th>
      <th align="center"><strong>OneGenomeRice (OGR)</strong></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td align="center" colspan="2"><strong>Model Scale</strong></td>
    </tr>
    <tr>
      <td align="center">Total Parameters</td>
      <td align="center">1.25B</td>
    </tr>
    <tr>
      <td align="center">Activated Parameters</td>
      <td align="center">0.33B</td>
    </tr>
    <tr>
      <td align="center" colspan="2"><strong>Architecture</strong></td>
    </tr>
    <tr>
      <td align="center">Architecture</td>
      <td align="center">MoE</td>
    </tr>
    <tr>
      <td align="center">Number of Experts</td>
      <td align="center">8</td>
    </tr>
    <tr>
      <td align="center">Selected Experts per Token</td>
      <td align="center">2</td>
    </tr>
    <tr>
      <td align="center">Number of Layers</td>
      <td align="center">12</td>
    </tr>
    <tr>
      <td align="center">Attention Hidden Dimension</td>
      <td align="center">1024</td>
    </tr>
    <tr>
      <td align="center">Number of Attention Heads</td>
      <td align="center">16 (GQA, 8 KV groups)</td>
    </tr>
    <tr>
      <td align="center">MoE Hidden Dimension (per Expert)</td>
      <td align="center">4096</td>
    </tr>
    <tr>
      <td align="center">Vocabulary Size</td>
      <td align="center">128 (padded)</td>
    </tr>
    <tr>
      <td align="center">Context Length</td>
      <td align="center">up to 1Mb</td>
    </tr>
  </tbody>
</table>

</div>