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Apache-2.0

大模型(LLMs)微调训练 快速入门指南

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大语言模型快速入门(理论学习与微调实战)

拉取代码

你可以通过克隆此仓库到 GPU 服务器来开始学习:

git clone https://github.com/DjangoPeng/LLM-quickstart.git

搭建开发环境

本项目对于硬件有一定要求:GPU 显存不小于16GB,支持最低配置显卡型号为 NVIDIA Tesla T4。

建议使用 GPU 云服务器来进行模型训练和微调。

项目使用 Python 版本为 3.10,环境关键依赖的官方文档如下:

以下是详细的安装指导(以 Ubuntu 22.04 操作系统为例)

安装 CUDA Toolkit 和 GPU 驱动

根据你的实际情况,找到对应的 CUDA 12.04

下载并安装 CUDA 12.04 Toolkit(包含GPU驱动):

wget https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_550.54.14_linux.run
sudo sh cuda_12.4.0_550.54.14_linux.run

**注意使用runfile方式,可以连同版本匹配的 GPU 驱动一起安装好。

CUDA Toolkit

安装完成后,使用 nvidia-smi 指令查看版本:

nvidia-smi          
Mon Dec 18 12:10:47 2023       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.129.03             Driver Version: 535.129.03   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| 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 | 00000000:00:0D.0 Off |                    0 |
| N/A   44C    P0              26W /  70W |      2MiB / 15360MiB |      6%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+

安装操作系统级软件依赖

sudo apt update && sudo apt upgrade
sudo apt install ffmpeg
## 检查是否安装成功
ffmpeg -version

参考:音频工具包 ffmpeg 官方安装文档

安装 Python 环境管理工具 Miniconda

mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -rf ~/miniconda3/miniconda.sh

安装完成后,建议新建一个 Python 虚拟环境,命名为 peft

conda create -n peft python=3.10

# 激活环境
conda activate peft 

之后每次使用需要激活此环境。

安装 Python 依赖软件包

完整 Python 依赖软件包见requirements.txt

pip install -r requirements.txt

安装和配置 Jupyter Lab

上述开发环境安装完成后,使用 Miniconda 安装 Jupyter Lab:

conda install -c conda-forge jupyterlab

使用 Jupyter Lab 开发的最佳实践是后台常驻,下面是相关配置(以 root 用户为例):

# 生成 Jupyter Lab 配置文件,
jupyter lab --generate-config

打开上面执行输出的jupyter_lab_config.py配置文件后,修改以下配置项:

c.ServerApp.allow_root = True # 非 root 用户启动,无需修改
c.ServerApp.ip = '*'

使用 nohup 后台启动 Jupyter Lab

$ nohup jupyter lab --port=8000 --NotebookApp.token='替换为你的密码' --notebook-dir=./ &

Jupyter Lab 输出的日志将会保存在 nohup.out 文件(已在 .gitignore中过滤)。

关于 LangChain 调用 OpenAI GPT API 的配置

为了使用OpenAI API,你需要从OpenAI控制台获取一个API密钥。一旦你有了密钥,你可以将其设置为环境变量:

对于基于Unix的系统(如Ubuntu或MacOS),你可以在终端中运行以下命令:

export OPENAI_API_KEY='你的-api-key'

对于Windows,你可以在命令提示符中使用以下命令:

set OPENAI_API_KEY=你的-api-key

请确保将'你的-api-key'替换为你的实际OpenAI API密钥。

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