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@ -249,9 +249,15 @@ python demo/gradio_based_demo.py
<p id="6"></p>
## 高效参数微调
## 二次开发
* 高效参数微调
* 一张1080/2080可实现高效参数微调
* [高效参数微调代码](https://github.com/OpenBMB/MiniCPM/tree/main/finetune)
* 全参数微调 or 持续训练
* 使用[BMTrain](https://github.com/OpenBMB/BMTrain)，借助重计算和ZeRO-3，一张3090/4090可实现全参数微调，一台机器可实现持续训练
* 相关代码也将陆续推出
<p id="7"></p>
@ -274,11 +280,11 @@ python demo/gradio_based_demo.py
## 工作引用
* 如果觉得MiniCPM有助于您的工作，请考虑引用下列[技术报告](todo)
* 如果觉得MiniCPM有助于您的工作，请引用我们的[技术报告](todo)
```
@inproceedings{minicpm2024,
title={MiniCPM: todo},
title={MiniCPM：Unveiling the Potential of End-side Large Language Models},
booktitle={OpenBMB Blog},
year={2024}
}