支持百川、chatglm、星火等AI大模型的容器化封装
已配好相关依赖，分为CPU和GPU版本，降低使用门槛，开箱即用。
拉取镜像(CPU版本)
bash
docker pull openeuler/llm-server:1.0.0-oe2203sp3拉取镜像(GPU版本)
bash
docker pull icewangds/llm-server:1.0.0下载模型, 并转换为gguf格式
bash
# 安装huggingface
pip install huggingface-hub
# 下载你想要部署的模型
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download --resume-download baichuan-inc/Baichuan2-13B-Chat --local-dir /root/models/Baichuan2-13B-Chat --local-dir-use-symlinks False
# gguf格式转换
cd /root/models/
git clone https://github.com/ggerganov/llama.cpp.git
python llama.cpp/convert-hf-to-gguf.py ./Baichuan2-13B-Chat
# 生成的gguf格式的模型路径 /root/models/Baichuan2-13B-Chat/ggml-model-f16.gguf启动方式
需要Docker v25.0.0及以上版本。
若使用GPU镜像，需要OS上安装nvidia-container-toolkit，安装方式见https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html。
docker-compose.yaml:
yaml
version: '3'
services:
model:
image: <image>:<tag> #镜像名称与tag
restart: on-failure:5
ports:
- 8001:8000 #监听端口号，修改“8001”以更换端口
volumes:
- /root/models:/models # 大模型挂载目录
environment:
- MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf # 容器内的模型文件路径
- MODEL_NAME=baichuan13b # 自定义模型名称
- KEY=sk-12345678 # 自定义API Key
- CONTEXT=8192 # 上下文大小
- THREADS=8 # CPU线程数，仅CPU部署时需要
deploy: # 指定GPU资源, 仅GPU部署时需要
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities: [gpu]bash
docker-compose -f docker-compose.yaml updocker run:
text
cpu部署: docker run -d --restart on-failure:5 -p 8001:8000 -v /root/models:/models -e MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf -e MODEL_NAME=baichuan13b -e KEY=sk-12345678 openeuler/llm-server:1.0.0-oe2203sp3
gpu部署: docker run -d --gpus all --restart on-failure:5 -p 8001:8000 -v /root/models:/models -e MODEL=/models/Baichuan2-13B-Chat/ggml-model-f16.gguf -e MODEL_NAME=baichuan13b -e KEY=sk-12345678 icewangds/llm-server:1.0.0调用大模型接口测试，成功返回则表示大模型服务已部署成功
bash
curl -X POST http://127.0.0.1:8001/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-12345678" \
-d '{
"model": "baichuan13b",
"messages": [
{"role": "system", "content": "你是一个社区助手，请回答以下问题。"},
{"role": "user", "content": "你是谁?"}
],
"stream": false,
"max_tokens": 1024
}'