ubuntu24.0离线安装Ollama和纯cpu版本以及对接Spring AI

文章目录

- 一.官网下载 0.3.13版本
- 二.将文件包上传至ubuntu服务器
- 三.下载安装脚本
- 四.剔除GPU相关下载ROCM等，纯CPU运行脚本
- 五.ollama常用命令
- 六. 远程测试
七.对接spring AI

一.官网下载 0.3.13版本

ollama离线安装包下载地址
在这里插入图片描述

二.将文件包上传至ubuntu服务器

在这里插入图片描述

三.下载安装脚本

curl -fsSL https://ollama.com/install.sh

修改远程拉取ollama代码为本地解压
源需要修改的脚本代码如下

if curl -I --silent --fail --location "https://ollama.com/download/ollama-linux-${ARCH}.tgz${VER_PARAM}" >/dev/null ; thenstatus "Downloading Linux ${ARCH} bundle"curl --fail --show-error --location --progress-bar \"https://ollama.com/download/ollama-linux-${ARCH}.tgz${VER_PARAM}" | \$SUDO tar -xzf - -C "$OLLAMA_INSTALL_DIR"BUNDLE=1if [ "$OLLAMA_INSTALL_DIR/bin/ollama" != "$BINDIR/ollama" ] ; thenstatus "Making ollama accessible in the PATH in $BINDIR"$SUDO ln -sf "$OLLAMA_INSTALL_DIR/ollama" "$BINDIR/ollama"fi
elsestatus "Downloading Linux ${ARCH} CLI"curl --fail --show-error --location --progress-bar -o "$TEMP_DIR/ollama"\"https://ollama.com/download/ollama-linux-${ARCH}${VER_PARAM}"$SUDO install -o0 -g0 -m755 $TEMP_DIR/ollama $OLLAMA_INSTALL_DIR/ollamaBUNDLE=0if [ "$OLLAMA_INSTALL_DIR/ollama" != "$BINDIR/ollama" ] ; thenstatus "Making ollama accessible in the PATH in $BINDIR"$SUDO ln -sf "$OLLAMA_INSTALL_DIR/ollama" "$BINDIR/ollama"fi
fi

新改后代码

status "Downloading Linux ${ARCH} bundle"
#    curl --fail --show-error --location --progress-bar \
#        "https://ollama.com/download/ollama-linux-${ARCH}.tgz${VER_PARAM}" | \
$SUDO tar -xzf ./ollama-linux-amd64.tgz -C "$OLLAMA_INSTALL_DIR"
BUNDLE=1
if [ "$OLLAMA_INSTALL_DIR/bin/ollama" != "$BINDIR/ollama" ] ; thenstatus "Making ollama accessible in the PATH in $BINDIR"$SUDO ln -sf "$OLLAMA_INSTALL_DIR/ollama" "$BINDIR/ollama"
fi

四.剔除GPU相关下载ROCM等，纯CPU运行脚本

在题目3的基础上，又剔除了GPU部分，即从wls2注释将下面全部删除

完整版离线基于CPU的运行脚本

#!/bin/sh
# This script installs Ollama on Linux.
# It detects the current operating system architecture and installs the appropriate version of Ollama.set -eustatus() { echo ">>> $*" >&2; }
error() { echo "ERROR $*"; exit 1; }
warning() { echo "WARNING: $*"; }TEMP_DIR=$(mktemp -d)
cleanup() { rm -rf $TEMP_DIR; }
trap cleanup EXITavailable() { command -v $1 >/dev/null; }
require() {local MISSING=''for TOOL in $*; doif ! available $TOOL; thenMISSING="$MISSING $TOOL"fidoneecho $MISSING
}[ "$(uname -s)" = "Linux" ] || error 'This script is intended to run on Linux only.'ARCH=$(uname -m)
case "$ARCH" inx86_64) ARCH="amd64" ;;aarch64|arm64) ARCH="arm64" ;;*) error "Unsupported architecture: $ARCH" ;;
esacIS_WSL2=falseKERN=$(uname -r)
case "$KERN" in*icrosoft*WSL2 | *icrosoft*wsl2) IS_WSL2=true;;*icrosoft) error "Microsoft WSL1 is not currently supported. Please use WSL2 with 'wsl --set-version <distro> 2'" ;;*) ;;
esacVER_PARAM="${OLLAMA_VERSION:+?version=$OLLAMA_VERSION}"SUDO=
if [ "$(id -u)" -ne 0 ]; then# Running as root, no need for sudoif ! available sudo; thenerror "This script requires superuser permissions. Please re-run as root."fiSUDO="sudo"
fiNEEDS=$(require curl awk grep sed tee xargs)
if [ -n "$NEEDS" ]; thenstatus "ERROR: The following tools are required but missing:"for NEED in $NEEDS; doecho "  - $NEED"doneexit 1
fifor BINDIR in /usr/local/bin /usr/bin /bin; doecho $PATH | grep -q $BINDIR && break || continue
done
OLLAMA_INSTALL_DIR=$(dirname ${BINDIR})status "Installing ollama to $OLLAMA_INSTALL_DIR"
$SUDO install -o0 -g0 -m755 -d $BINDIR
$SUDO install -o0 -g0 -m755 -d "$OLLAMA_INSTALL_DIR"
status "Downloading Linux ${ARCH} bundle"
#    curl --fail --show-error --location --progress-bar \
#        "https://ollama.com/download/ollama-linux-${ARCH}.tgz${VER_PARAM}" | \
$SUDO tar -xzf ./ollama-linux-amd64.tgz -C "$OLLAMA_INSTALL_DIR"
BUNDLE=1
if [ "$OLLAMA_INSTALL_DIR/bin/ollama" != "$BINDIR/ollama" ] ; thenstatus "Making ollama accessible in the PATH in $BINDIR"$SUDO ln -sf "$OLLAMA_INSTALL_DIR/ollama" "$BINDIR/ollama"
fiinstall_success() {status 'The Ollama API is now available at 127.0.0.1:11434.'status 'Install complete. Run "ollama" from the command line.'
}
trap install_success EXIT# Everything from this point onwards is optional.configure_systemd() {if ! id ollama >/dev/null 2>&1; thenstatus "Creating ollama user..."$SUDO useradd -r -s /bin/false -U -m -d /usr/share/ollama ollamafiif getent group render >/dev/null 2>&1; thenstatus "Adding ollama user to render group..."$SUDO usermod -a -G render ollamafiif getent group video >/dev/null 2>&1; thenstatus "Adding ollama user to video group..."$SUDO usermod -a -G video ollamafistatus "Adding current user to ollama group..."$SUDO usermod -a -G ollama $(whoami)status "Creating ollama systemd service..."cat <<EOF | $SUDO tee /etc/systemd/system/ollama.service >/dev/null
[Unit]
Description=Ollama Service
After=network-online.target[Service]
ExecStart=$BINDIR/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=$PATH"[Install]
WantedBy=default.target
EOFSYSTEMCTL_RUNNING="$(systemctl is-system-running || true)"case $SYSTEMCTL_RUNNING inrunning|degraded)status "Enabling and starting ollama service..."$SUDO systemctl daemon-reload$SUDO systemctl enable ollamastart_service() { $SUDO systemctl restart ollama; }trap start_service EXIT;;esac
}if available systemctl; thenconfigure_systemd
fiinstall_success

在这里插入图片描述

五.ollama常用命令

# 关闭ollama服务
service ollama stopollama serve # 启动ollama
ollama create # 从模型文件创建模型
ollama show  # 显示模型信息
ollama run qwen2.5:3b-instruct-q4_K_M  # 运行模型，会先自动下载模型
ollama pull  # 从注册仓库中拉取模型
ollama push  # 将模型推送到注册仓库
ollama list  # 列出已下载模型
ollama ps  # 列出正在运行的模型
ollama cp  # 复制模型
ollama rm  # 删除模型

六. 远程测试

建议生产不开启，因为没有token等限制，必须注意接口调用安全
1.首先停止ollama服务：

systemctl stop ollama

2.修改ollama的service文件：

vim /etc/systemd/system/ollama.service

3.新增Environment="OLLAMA_HOST=0.0.0.0:11434"

[Unit]
Description=Ollama Service
After=network-online.target[Service]
ExecStart=/usr/local/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin"
Environment="OLLAMA_HOST=0.0.0.0:11434"[Install]
WantedBy=default.target

启动ollama

systemctl daemon-reload
systemctl start ollama
# 若启动失败可以使用 ollama serve测试

七.对接spring AI

    <dependencyManagement><dependencies><!--spring boot依赖--><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-dependencies</artifactId><version>${spring.boot.version}</version><type>pom</type><scope>runtime</scope></dependency><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-bom</artifactId><version>1.0.0-SNAPSHOT</version><type>pom</type><scope>import</scope></dependency></dependencies></dependencyManagement><dependencies><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><!-- https://mvnrepository.com/artifact/org.springframework.ai/spring-ai-ollama-spring-boot-starter --><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-ollama-spring-boot-starter</artifactId></dependency></dependencies><repositories><repository><id>spring-milestones</id><name>Spring Milestones</name><url>https://repo.spring.io/milestone</url><snapshots><enabled>false</enabled></snapshots></repository><repository><id>spring-snapshots</id><name>Spring Snapshots</name><url>https://repo.spring.io/snapshot</url><releases><enabled>false</enabled></releases></repository></repositories>

若以上代码无法拉取，可能被setting.xml全局拦截到镜像站。以上spring ai还未发布到maven中央仓库请参考maven多仓库私库模板配置

spring:application:name: spring-ai-ollamaai:ollama:base-url: http://192.168.200.94:11434chat:# 为了使模型输入内容拥有更多的多样性或随机性，应当增加temperature。#在 temperature 非零的情况下，从 0.95 左右的 top-p（或 250 左右的 top-k ）开始，根据需要降低 temperature。# 如果有太多无意义的内容、垃圾内容或产生幻觉，应当降低 temperature 和 降低top-p/top-k。# 如果 temperature 很高而模型输出内容的多样性却很低，应当增加top-p/top-k。# 为了获得更多样化的主题，应当增加存在惩罚值。# 为了获得更多样化且更少重复内容的模型输出，应当增加频率惩罚。options:# 配置文件指定时，现在程序中指定的模型，程序没有指定模型在对应查找配置中的模型#          model: qwen:0.5b-chatmodel: qwen2.5:3b-instruct-q4_K_M# 支持的最大字符数max_tokens: 2048# 温度值越高，准确率下降，温度值越低，准确率上升# 对于每个提示语只需要单个答案：零。#对于每个提示语需要多个答案：非零。temperature: 0.4# 随机采样 值越大，随机性越高# 在 temperature 为零的情况下：输出不受影响。# 在 temperature 不为零的情况下：非零。top_p: 0.2# 贪心解码 值越大，随机性越高top-k: 40# 频率惩罚 让token每次在文本中出现都受到惩罚。这可以阻止重复使用相同的token/单词/短语，同时也会使模型讨论的主题更加多样化，更频繁地更换主题# 当问题仅存在一个正确答案时：零。# 当问题存在多个正确答案时：可自由选择。frequency-penalty: 0# 存在惩罚 如果一个token已经在文本中出现过，就会受到惩罚 使其讨论的主题更加多样化，话题变化更加频繁，而不会明显抑制常用词的重复presence-penalty: 0

@RestController
public class QianWenController {@Resourceprivate OllamaChatModel ollamaChatModel;@RequestMapping(value = "/ai/ollama")public Object ollama(@RequestParam(value = "msg") String msg) {String called = ollamaChatModel.call(msg);System.out.println(called);return called;}@RequestMapping(value = "/ai/ollama2")public Map<String, Object> ollama2(@RequestParam(value = "msg") String msg) {Map<String, Object> map = new HashMap<String, Object>();long start = System.currentTimeMillis();ChatResponse chatResponse = ollamaChatModel.call(new Prompt(msg, OllamaOptions.create().withModel("qwen2.5:3b-instruct-q4_K_M")//使用哪个大模型.withTemperature(0.4D)));//温度，温度值越高，准确率下降，温度值越低，准确率上升String content = chatResponse.getResult().getOutput().getContent();long end = System.currentTimeMillis();map.put("content", content);map.put("time", (end - start) / 1000);return map;}@RequestMapping(value = "/ai/stream",produces = MediaType.TEXT_EVENT_STREAM_VALUE)public Flux<String>  stream(@RequestParam(value = "msg") String msg) {return ollamaChatModel.stream(new Prompt(msg)).flatMapSequential(chunk -> Flux.just(chunk.getResult().getOutput().getContent()));}
}