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# 九格大模型使用文档
目录
- [环境配置](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md?tab=readme-ov-file#环境配置)
- [开源模型](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md?tab=readme-ov-file#开源模型)
- [数据](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md?tab=readme-ov-file#数据)
- [模型训练](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md?tab=readme-ov-file#模型训练)
- [模型推理](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md?tab=readme-ov-file#模型推理)
- [开源模型](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md?tab=readme-ov-file#开源模型)
- [分布式多机训练](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md?tab=readme-ov-file#分布式多机训练)
- [FAQs](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md?tab=readme-ov-file#FAQs)
## 环境配置：
[环境配置、硬件信息](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ENV.md)
@ -107,7 +115,12 @@ def transform(data, num_sample: int, r: random.Random):
- 我们在此文件中指定了数据文件的路径、转换脚本路径等信息，后续训练仅需要系统该文件的路径即可。
## 模型训练
模型训练列举了三种训练
- [pretrain 训练](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md?tab=readme-ov-file#pretrain 训练)
- [SFT全参数微调训练](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md?tab=readme-ov-file#SFT全参数微调训练)
- [LoRA微调训练](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md?tab=readme-ov-file#LoRA微调训练)
### pretrain 训练：
模型训练代码的位置：9G-Train/apps/cpm9g/pretrain_cpm9g.py
需要将代码中环境变量设置为您的代码路径：
``` python
@ -115,7 +128,6 @@ def transform(data, num_sample: int, r: random.Random):
sys.path.insert(0, "/data/public/CPM-9G/9G-Train")
```
### pretrain shell脚本：
```shell
#! /bin/bash
@ -172,9 +184,8 @@ echo "${CMD}"
$CMD
```
### sft 训练shell 脚本
### SFT全参数微调训练
``` shell
export MASTER_ADDR=`hostname`
export MASTER_PORT=12345
@ -220,11 +231,9 @@ OPTS+=" $@"
CMD="torchrun --nnodes=1 --nproc_per_node=8 --rdzv_id=1 --rdzv_backend=c10d --rdzv_endpoint=${MASTER_ADDR}:${MASTER_PORT} ${CPM_PATH}/apps/cpm9g/sft_cpm9g.py ${OPTS}"
echo "${CMD}"
$CMD
```
### lora 训练
[lora 训练](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_LORA.md)
### [LoRA微调训练](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_LORA.md)
## 模型推理
```python
@ -286,7 +295,7 @@ if __name__ == "__main__":
```
5 微调训练中，train_iters如何计算？
```
回答：因为模型上下文是4096的token数目，通常情况存在训练数据不足4096的长度，所以会对多条数据进行merge，送入模型的数据量会少于1000条
回答：因为模型上下文是4096的token数目，通常情况存在训练数据不足4096的长度，所以会对多条数据进行merge，因此送入模型条数要少于实际的数据条数
```
6 打印出来的Iter信息有缺失
```
@ -310,5 +319,4 @@ datas = [
## TODO
1 发布最新训练的80B SFT模型
2 Lora相关的代码更新
1 发布最新训练的80B SFT模型