Merge remote-tracking branch 'origin/dev_1.0.0' into dev_1.0.0
# Conflicts: # src/main/java/com/supervision/police/service/impl/ModelCaseServiceImpl.javatopo_dev
DESKTOP-DDTUS3E\yaxin
10 months ago
commit
c45b374f7a
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# mysql相关配置内容
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MYSQL_PASSWORD=12345678
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MYSQL_PORT=5415
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# minio相关配置内容
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MINIO_CONSOLE_PORT=9001
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MINIO_PORT=9000
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MINIO_USER=admin
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MINIO_PASSWORD=12345678
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# neo4j相关配置内容
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NEO4J_PORT_HTTP=7474
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NEO4J_PORT_BOLT=7687
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NEO4J_USERNAME=neo4j
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NEO4J_PASSWORD=123456
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# 伏羲后台服务相关配置内容
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WEB_HTTP_PORT=9380
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SPRING_PROFILES_ACTIVE=bridge
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OLLAMA_URL=http://192.168.10.70:11434
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OLLAMA_MODEL=llama3-chinese:8b
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LANGCHAIN_CHAT_URL=http://113.128.242.110:7861
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# nginx相关配置内容
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NGINX_HTTP_PORT=80
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FU_HSI_SERVERS=fu-hsi-web:8097
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TIMEZONE='Asia/Shanghai'
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services:
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mysql:
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image: mysql:5.7
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container_name: fu-hsi-mysql
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environment:
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- MYSQL_ROOT_PASSWORD=${MYSQL_PASSWORD}
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- TZ=${TIMEZONE}
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command:
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--max_connections=100
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--character-set-server=utf8mb4
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--collation-server=utf8mb4_general_ci
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--default-authentication-plugin=mysql_native_password
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--tls_version="TLSv1.2,TLSv1.3"
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ports:
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- ${MYSQL_PORT}:3306
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volumes:
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- ./mysql/data:/var/lib/mysql
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- ./init.sql:/docker-entrypoint-initdb.d/init.sql
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networks:
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- fu-hsi
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healthcheck:
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test: ["CMD", "mysqladmin" ,"ping", "-uroot", "-p${MYSQL_PASSWORD}"]
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interval: 10s
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timeout: 10s
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retries: 3
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start_period: 40s
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restart: always
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minio:
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image: quay.io/minio/minio:RELEASE.2023-12-20T01-00-02Z
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container_name: fu-hsi-minio
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command: server --console-address ":9001" /data
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ports:
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- ${MINIO_PORT}:9000
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- ${MINIO_CONSOLE_PORT}:9001
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environment:
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- MINIO_ROOT_USER=${MINIO_USER}
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- MINIO_ROOT_PASSWORD=${MINIO_PASSWORD}
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- TZ=${TIMEZONE}
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volumes:
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- ./minio/data:/data
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networks:
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- fu-hsi
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# healthcheck:
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# test: ["CMD","curl -fs http://localhost/ || exit 1"]
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# interval: 30s
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# timeout: 10s
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# retries: 3
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# start_period: 20s
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restart: always
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neo4j:
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image: neo4j:4.4
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container_name: fu-hsi-neo4j
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ports:
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- ${NEO4J_PORT_HTTP}:7474
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- ${NEO4J_PORT_BOLT}:7687
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environment:
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- NEO4J_AUTH=${NEO4J_USERNAME}/${NEO4J_PASSWORD}
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volumes:
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- ./neo4j/data:/data
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- ./neo4j/logs:/logs
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- ./neo4j/import:/var/lib/neo4j/import
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- ./neo4j/plugins:/var/lib/neo4j/plugins
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networks:
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- fu-hsi
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# healthcheck:
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# test: ["CMD", "curl", "-sS", "http://localhost:7474"]
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# interval: 30s
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# timeout: 10s
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# retries: 5
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# start_period: 40s
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# restart: always
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networks:
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fu-hsi:
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driver: bridge
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include:
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- path: ./docker-compose-base.yml
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env_file: ./.env
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services:
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fu-hsi-web:
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depends_on:
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mysql:
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condition: service_started
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neo4j:
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condition: service_started
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minio:
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condition: service_started
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image: fu-hsi-web:1.0.0
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container_name: fu-hsi-web
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ports:
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- ${WEB_HTTP_PORT}:8097
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volumes:
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- ./fu-hsi-web/:/data/fu-hsi/web/
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environment:
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- TZ=${TIMEZONE}
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- spring.profiles.active=${SPRING_PROFILES_ACTIVE}
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- ollama.url=${OLLAMA_URL}
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- ollama.model=${OLLAMA_MODEL}
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- langChain-chat.url=${LANGCHAIN_CHAT_URL}
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- datasource.username=${MYSQL_PASSWORD}
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- neo4j.username=${NEO4J_USERNAME}
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- neo4j.password=${NEO4J_PASSWORD}
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- minio.username=${MINIO_USER}
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- minio.password=${MINIO_PASSWORD}
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networks:
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- fu-hsi
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restart: always
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fu-hsi-nginx:
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depends_on:
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fu-hsi-web:
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condition: service_started
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image: fu-hsi-nginx:1.0.0
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container_name: fu-hsi-nginx
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ports:
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- ${NGINX_HTTP_PORT}:80
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volumes:
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- ./fu-hsi-nginx/html/:/usr/share/nginx/html/
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environment:
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- TZ=${TIMEZONE}
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- FU_HSI_SERVERS=${FU_HSI_SERVERS}
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networks:
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- fu-hsi
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restart: always
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# 文件目录说明
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- /data/ollama : 本项目根目录,本项目所有目录都在这个路径下面
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# 前置条件
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- 本机安装了Docker
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- 安装了Nvidia GPU驱动
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- 安装了容器工具包nvidia-container-toolkit
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# 下载gguf模型:
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- 网址: https://hf-mirror.com/
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- 搜要下载的模型,比如,想现在llama3 8b的模型,则搜索: llama3 8b gguf
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- 找到gguf字样的进去,下载,下载建议下载k4_0级别的,效果和效率最优,注意下载文件格式一定是.gguf的才行
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# Modelfile文件
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- 创建文件:touch Modlefile
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- 编辑Modlefile,修改并粘贴以下内容
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~~~
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FROM ./这里替换成需要部署的模型名称.gguf
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TEMPLATE """
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{{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}
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{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Prompt }}<|eot_id|>{{ end }}
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<|start_header_id|>assistant<|end_header_id|>\n\n{{ .Response }}<|eot_id|>
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"""
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~~~
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# 使用Docker构建步骤
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- 首先安装容器工具包(必须,如果已安装可以不安装)
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1. 首先配置源:curl -s -L https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo | sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo
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2. 安装: sudo yum install -y nvidia-container-toolkit
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3. 配置docker: sudo nvidia-ctk runtime configure --runtime=docker
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4. 重启docker: systemctl restart docker
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- 构建步骤
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1. 加载ollama的镜像,镜像版本0.2.8
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- 本地加载: docker load -i ollama.tar.gz
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2. 运行容器: sudo docker run -d --gpus=all -e NVIDIA_VISIBLE_DEVICES=all -v /data/ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
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3. 将模型文件复制本地目录/data/ollama/gguf下
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4. 在/data/ollama/gguf目录下创建Modlefile文件,并将FROM的模型的名称修改为对应的.gguf文件名称
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5. 进入容器: docker exec -it ollama bash
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6. 进入目录: cd /root/.ollama/gguf
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7. 执行命令ollama create llama3-chinese:8b -f Modelfile (将 llama3-chinese:8b修改成你想要命名的名称)
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