CPM-9G-8B/README.md

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方案:
全参数微调,使用不同数据集训练多个模型和推理时增强进行融合。
训练代码:
LLaMA-Factory.zip 解压后使用可参照https://github.com/hiyouga/LLaMA-Factory配置环境或将代码映射到docker中使用。
训练train.sh。将数据集放到LLaMA-Factory/data文件夹下将train.sh放到LLaMA-Factory下使用。
推理: python inference.py(需在inference.py中修改好模型路径。) test_case.json是从题目中提取出来的测试用例。
百度网盘需要收费,使用阿里云盘
model_wight:通过百度网盘分享的文件:
链接https://pan.baidu.com/s/1paYNO7d5OYESuyw3BVo7Ew
提取码6666
https://www.alipan.com/s/FTPWUSBuz7s
docker:
链接https://pan.baidu.com/s/1paYNO7d5OYESuyw3BVo7Ew
提取码6666
https://www.alipan.com/s/FTPWUSBuz7s
train_data:
链接https://pan.baidu.com/s/1paYNO7d5OYESuyw3BVo7Ew
提取码6666
https://www.alipan.com/s/FTPWUSBuz7s