mirror of
https://github.com/RYDE-WORK/MiniCPM.git
synced 2026-01-29 02:23:42 +08:00
78 lines
2.6 KiB
Markdown
78 lines
2.6 KiB
Markdown
# MiniCPM_llama_factory 微调
|
||
MiniCPM已经支持llama_factory微调，llama_factory支持continue_pretrain,sft,ppo,dpo,kto,orpo等等微调方式。
|
||
由于llama_factory功能强大，但初学者较难上手，我们录制了微调教程
|
||
|
||
**我们提供了 llama_factory_example文件夹，用来微调minicpm1b，minicpm2b模型。**
|
||
1.首先安装llama_factory依赖。
|
||
```bash
|
||
git clone https://github.com/hiyouga/LLaMA-Factory
|
||
cd LLaMA-Factory
|
||
pip install -r requirements.txt
|
||
```
|
||
2.将数据集处理成Minicpm/finetune/llama_factory_example/llama_factory_data文件夹中的格式,示例包括dpo,kto,sft三种微调方式并放置到llama_factory/data目录下.
|
||
|
||
3.在llama_factory/data/dataset_info.json中添加数据集信息,保证dataset_info.json中能找到你的数据集，如下例：
|
||
``` json
|
||
{"identity": {
|
||
"file_name": "identity.json"
|
||
},
|
||
"alpaca_zh_demo": {
|
||
"file_name": "alpaca_zh_demo.json"
|
||
},
|
||
"kto_en_demo": {
|
||
"file_name": "kto_en_demo.json",
|
||
"formatting": "sharegpt",
|
||
"columns": {
|
||
"messages": "messages",
|
||
"kto_tag": "label"
|
||
},
|
||
"tags": {
|
||
"role_tag": "role",
|
||
"content_tag": "content",
|
||
"user_tag": "user",
|
||
"assistant_tag": "assistant"
|
||
}
|
||
},
|
||
"dpo_en_demo": {
|
||
"file_name": "dpo_en_demo.json",
|
||
"ranking": true,
|
||
"formatting": "sharegpt",
|
||
"columns": {
|
||
"messages": "conversations",
|
||
"chosen": "chosen",
|
||
"rejected": "rejected"
|
||
}
|
||
}
|
||
}
|
||
```
|
||
4.将MiniCPM/finetune/llama_factory_example中文件复制到LLaMA-Factory/examples目录下。
|
||
|
||
5.以dpo为例，首先修改minicpm_dpo.yaml,需要修改的：
|
||
```yaml
|
||
model_name_or_path: openbmb/MiniCPM-2B-sft-bf16 #或者你本地保存的地址
|
||
dataset: dpo_en_demo #这里写dataset_info.json中的键名
|
||
output_dir: your/finetune_minicpm/save/path
|
||
bf16: true #如果你的设备支持bf16，否则false
|
||
deepspeed: examples/deepspeed/ds_z2_config.json #如果显存不够可以改成ds_z3_config.json
|
||
```
|
||
6.修改single_node.sh文件中：
|
||
|
||
- 1.如果是a100以及更高端服务器，删除以下两行
|
||
```bash
|
||
export NCCL_P2P_DISABLE=1
|
||
export NCCL_IB_DISABLE=1
|
||
```
|
||
- 2.设置你希望参与微调的卡，以下示例为第1张到第8张卡都参与微调
|
||
```bash
|
||
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
|
||
```
|
||
- 3.将以下代码src/train.py空格后方参数改为llama_facoty中minicpm_dpo.yaml的绝对路径
|
||
```bash
|
||
src/train.py /root/ld/ld_project/LLaMA-Factory/examples/minicpm/minicpm_sft.yaml
|
||
```
|
||
7.执行：
|
||
```bash
|
||
cd LLaMA-Factory
|
||
bash single_node.sh
|
||
```
|