【LLaMA-Factory】使用LoRa微调训练DeepSeek-R1-Distill-Qwen-7B

本地环境说明

依赖	版本
Linux	BigCloud Enterprise Linux 8.6
GPU	NVIDIA Tesla T4 16G * 8

禁用开源驱动nouveau

如果不禁用开源驱动，直接安装nvidia-smi，会安装失败，在日志文件/var/log/nvidia-installer.log中会出现以下错误信息
ERROR: Unable to load the kernel module 'nvidia.ko'

• 查看nouveau是否在运行，先输入指令

lsmod | grep nouveau

如果不出现一下的情况则已经禁用

nouveau              2334720  0
video                  57344  1 nouveau
mxm_wmi                16384  1 nouveau
drm_kms_helper        262144  5 drm_vram_helper,ast,nouveau
ttm                   114688  3 drm_vram_helper,drm_ttm_helper,nouveau
i2c_algo_bit           16384  3 igb,ast,nouveau
drm                   614400  7 drm_kms_helper,drm_vram_helper,ast,drm_ttm_helper,ttm,nouveau
i2c_core               98304  9 drm_kms_helper,i2c_algo_bit,igb,ast,i2c_smbus,i2c_i801,ipmi_ssif,nouveau,drm
wmi                    32768  2 mxm_wmi,nouveau

• 禁用nouveau

# 如果文件不存在，就创建
sudo sh -c 'cat > /etc/modprobe.d/blacklist.conf << EOF
blacklist nouveau
options nouveau modeset=0
EOF'# 重新生成 initramfs
sudo dracut --force
# 重启机器
sudo reboot

安装nvidia-smi

• 浏览器访问: https://www.nvidia.com/en-us/drivers/

  需要梯子(科学上网)，才能加载Manual Driver Search

• 填写驱动信息
  
• 驱动查询结果
  
• 驱动下载页面
  
• 下载驱动文件，得到NVIDIA-Linux-x86_64-570.133.20.run，上传到Linux机器上

  不能复制下载地址，然后在机器上使用wget命令直接下载，这样请求会返回403

• 安装驱动

• 必须使用root权限安装
• -no-x-check #安装驱动时关闭X服务
• -no-nouveau-check #安装驱动时禁用nouveau
• -no-opengl-files #只安装驱动文件，不安装OpenGL文件

• 安装的时候，出现内核模块类型选择，根据提示选择NVIDIA Proprietary，使用左右键控制选择，然后回车

Multiple kernel module types are available for this system. Which would you like to use?NVIDIA Proprietary        MIT/GPL   

• 安装完运行命令确认驱动安装成功

nvidia-smi

显卡信息如下

+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 570.133.20             Driver Version: 570.133.20     CUDA Version: 12.8     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  Tesla T4                       Off |   00000000:3D:00.0 Off |                    0 |
| N/A   51C    P0             27W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   1  Tesla T4                       Off |   00000000:3E:00.0 Off |                    0 |
| N/A   54C    P0             28W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   2  Tesla T4                       Off |   00000000:40:00.0 Off |                    0 |
| N/A   51C    P0             27W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   3  Tesla T4                       Off |   00000000:41:00.0 Off |                    0 |
| N/A   48C    P0             26W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   4  Tesla T4                       Off |   00000000:B1:00.0 Off |                    0 |
| N/A   47C    P0             26W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   5  Tesla T4                       Off |   00000000:B2:00.0 Off |                    0 |
| N/A   53C    P0             30W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   6  Tesla T4                       Off |   00000000:B4:00.0 Off |                    0 |
| N/A   56C    P0             30W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   7  Tesla T4                       Off |   00000000:B5:00.0 Off |                    0 |
| N/A   55C    P0             30W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------++-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

安装Git环境

安装git和git-lfs

sudo dnf install git git-lfs

安装Anaconda(conda)环境

• 下载页面: https://www.anaconda.com/download/success
  
• 64-Bit (x86) Installer下载

wget https://repo.anaconda.com/archive/Anaconda3-2024.10-1-Linux-x86_64.sh

• 安装

sudo sh Anaconda3-2024.10-1-Linux-x86_64.sh

• 会出现很多信息，一路yes下去，观看文档用q跳过

Do you accept the license terms? [yes|no]
>>> yesAnaconda3 will now be installed into this location:
/root/anaconda3- Press ENTER to confirm the location- Press CTRL-C to abort the installation- Or specify a different location below
[/root/anaconda3] >>> /data/ProgramFiles/anaconda3You can undo this by running `conda init --reverse $SHELL`? [yes|no]
[no] >>> yes

• 设置环境变量

cat >> ~/.bash_profile << EOF
export ANACONDA3_HOME=/data/ProgramFiles/anaconda3
export CONDA_ENVS_PATH=\$ANACONDA3_HOME/envs
export PATH="\$ANACONDA3_HOME/bin:$PATH"
EOF
source ~/.bash_profile# 目录是使用root权限安装的，对目录进行授权
sudo chown -R tkyj.tkyj /data

• 查看conda版本以验证是否安装成功

conda -V

• 配置镜像源

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --set show_channel_urls yes

下载DeepSeek-R1-Distill-Qwen-7B模型

• 魔搭社区: https://modelscope.cn/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

mkdir -pv /data/llm/models
cd /data/llm/models# 如果您希望跳过 lfs 大文件下载，可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B.git
# 请确保lfs已经被正确安装
cd DeepSeek-R1-Distill-Qwen-7B
git lfs install
# 下载大文件
git lfs pull

安装LLaMA-Factory

• 软件要求

Mandatory	Minimum	Recommend
python	3.9	3.10
torch	2.0.0	2.6.0
transformers	4.45.0	4.50.0
datasets	2.16.0	3.2.0
accelerate	0.34.0	1.2.1
peft	0.14.0	0.15.1
trl	0.8.6	0.9.6

Optional	Minimum	Recommend
CUDA	11.6	12.2
deepspeed	0.10.0	0.16.4
bitsandbytes	0.39.0	0.43.1
vllm	0.4.3	0.8.2
flash-attn	2.5.6	2.7.2

• 硬件要求

Method	Bits	7B	14B	30B	70B	xB
Full (bf16 or fp16)	32	120GB	240GB	600GB	1200GB	18xGB
Full (pure_bf16)	16	60GB	120GB	300GB	600GB	8xGB
Freeze/LoRA/GaLore/APOLLO/BAdam	16	16GB	32GB	64GB	160GB	2xGB
QLoRA	8	10GB	20GB	40GB	80GB	xGB
QLoRA	4	6GB	12GB	24GB	48GB	x/2GB
QLoRA	2	4GB	8GB	16GB	24GB	x/4GB

下载LLaMA-Factory

• 使用git克隆项目

cd /data/ProgramFiles
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git

安装LLaMA-Factory依赖

cd LLaMA-Factory
conda create --name llama_factory  python=3.10
conda activate llama_factory
# 从torch官网匹配cuda 12.8版本
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
pip install -e ".[torch,metrics]" -i https://pypi.tuna.tsinghua.edu.cn/simple# 验证安装
llamafactory-cli version

修改环境变量

方便每次登录终端自动切换到对应的Python环境

cat >> ~/.bash_profile << EOF
conda activate llama_factory
EOF
source ~/.bash_profile

安装deepspeed

单卡16G显存微调7B模型有点吃紧，可以使用deepspeed解决

# 查询cuda版本
conda search cuda -c nvidia
# 安装与nvidia-smi对应的版本(安装cuda是安装deepspeed的前置条件)
conda install cuda=12.8.1 -c nvidia
DS_BUILD_CPU_ADAM=1 pip3 install deepspeed==0.16.7

Alpaca数据集准备

• alpaca示例格式

[{"instruction": "Invent a new word by combining two existing words.","input": "","output": "\"Chillax\" - a combination of the words \"chill\" and \"relax\". It means to calm down and unwind, enjoying oneself in a laidback manner."},{"instruction": "Transform the following sentence using a synonym: The car sped quickly.","input": "","output": "The car accelerated rapidly."}

• 修改LLaMA-Factory/data/dataset_info.json，增加如下信息

"cmic_financial_apaca": {"file_name": "/data/llm/dataset/cmic_financial_apaca.json","columns": {"prompt": "instruction","query": "input","response": "output","system": "system"}
}

lora配置文件准备

• 备份原始文件

cd LLaMA-Factory/examples/train_lora
# 从原始文件复制一份新的出来
cp llama3_lora_sft.yaml ds_qwen7b_lora_sft.yaml
vi examples/train_lora/ds_qwen7b_lora_sft.yaml

• 修改ds_qwen7b_lora_sft.yaml，主要修改如下字段
  • model_name_or_path
  • dataset
  • template
  • cutoff_len
  • max_samples
  • output_dir
• 需要关注以下参数
  • model_name_or_path: 模型路径
  • dataset: 数据集名称，对应上面声明的cmic_financial_apaca
  • template: 模版
  • cutoff_len: 控制输入序列的最大长度
  • output_dir: 微调后权重保存路径
  • gradient_accumulation_steps: 梯度累积的步数，GPU资源不足时需要减少该值
  • num_train_epochs: 训练的轮数
• ds_qwen7b_lora_sft.yaml完整内容如下

### model
model_name_or_path: /data/llm/models/DeepSeek-R1-Distill-Qwen-7B
trust_remote_code: true### method
stage: sft
do_train: true
finetuning_type: lora
lora_rank: 8
lora_target: all### dataset
dataset: cmic_financial_apaca
template: deepseek3
cutoff_len: 4096
max_samples: 4019
overwrite_cache: true
preprocessing_num_workers: 16
dataloader_num_workers: 4### output
output_dir: /data/llm/models/sft/DeepSeek-R1-Distill-Qwen-7B
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
save_only_model: false
report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 1.0e-4
num_train_epochs: 1.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
resume_from_checkpoint: null### eval
#eval_dataset: alpaca_en_demo
val_size: 0.1
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 500

设置GPU个数

多卡并行时，设置GPU个数，修改: LLaMA-Factory/examples/accelerate/fsdp_config.yaml

num_processes: 8  # the number of GPUs in all nodes

启动微调

conda activate llama_factory# 后台运行
nohup llamafactory-cli train /data/ProgramFiles/LLaMA-Factory/examples/train_lora/ds_qwen7b_lora_sft.yaml > nohup.log 2>&1 &# 查看日志
tail -fn200 nohup.log

查看微调时GPU情况

运行命令: watch -n 0.5 nvidia-smi

Every 0.5s: nvidia-smi                                                                                                                                             localhost.localdomain: Mon Apr 28 15:33:10 2025Mon Apr 28 15:33:14 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 570.133.20             Driver Version: 570.133.20     CUDA Version:      |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  Tesla T4                       Off |   00000000:3D:00.0 Off |                    0 |
| N/A   39C    P8             19W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   1  Tesla T4                       Off |   00000000:3E:00.0 Off |                    0 |
| N/A   40C    P0             26W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   2  Tesla T4                       Off |   00000000:40:00.0 Off |                    0 |
| N/A   37C    P0             26W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   3  Tesla T4                       Off |   00000000:41:00.0 Off |                    0 |
| N/A   37C    P0             25W /   70W |       0MiB /  15360MiB |      4%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   4  Tesla T4                       Off |   00000000:B1:00.0 Off |                    0 |
| N/A   36C    P8             13W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   5  Tesla T4                       Off |   00000000:B2:00.0 Off |                    0 |
| N/A   40C    P8             15W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   6  Tesla T4                       Off |   00000000:B4:00.0 Off |                    0 |
| N/A   42C    P8             15W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   7  Tesla T4                       Off |   00000000:B5:00.0 Off |                    0 |
| N/A   41C    P8             11W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------++-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

启动webui服务

# 关闭防火墙，方便访问web服务端口
sudo systemctl stop firewalldGRADIO_SHARE=1
nohup llamafactory-cli webui > webui.log 2>&1 &
tail -fn200 webui.log

对话

llamafactory-cli chat /data/ProgramFiles/LLaMA-Factory/examples/inference/ds_qwen7b_lora_sft.yaml

损失函数曲线

参考资料

• 开源模型应用落地-DeepSeek-R1-Distill-Qwen-7B-LoRA微调-LLaMA-Factory-单机单卡-V100（一）
• 保姆级零基础微调大模型（LLaMa-Factory，多卡版）
• LLaMA-Factory：手把手教你从零微调大模型！
• 大语言模型训练“参数”到底改怎么调？？？