ollama安装与使用

1. ollama安装：

1.1 ollama下载地址：

https://ollama.com/download

1.2 安装Ollama（windows）：

直接一键安装

若想进行个性化设置参见：

1.3 安装Ollama(linux)：

安装Ollama：
- 下载&解压安装包：
  curl -L https://ollama.com/download/ollama-linux-amd64.tgz -o ollama-linux-amd64.tgz
- 启动Ollama服务:
  ollama serve
- 查看Ollama版本:
  ollama -v
调整模型下载目录：
- 设置模型下载地址：
  vim ~/.bashrc
配置 OLLAMA_MODELS 环境变量自定义路径

ollama model dir 改为自定义的路径，默认路径 /usr/share/ollama/.ollama/models

export OLLAMA_MODELS=/home/Ollama/ollama_cache

复制/usr/share/ollama/.ollama/models目录中（blobs manifests）的文件夹到OLLAMA_MODELS环境变量目录

cp -r /usr/share/ollama/.ollama/models /home/Ollama/ollama_cache

1.4 下载模型：

在ollama官网搜索所需模型：https://ollama.com/
复制模型下载命令即可，如下，获取qwen2:0.5b模型
在cmd或者shell终端执行第2步命令即可，比如：ollama run qwen2:0.5b

1.5 启动ollama：

windows版直接双击启动，其他可参考如下命令
常见命令：

启动服务

ollama serve

启动服务（挂后台）

nohup ollama serve >/dev/null 2>&1 &

查看已经安装的模型

ollama list

查看版本

ollama v

2. ollama下模型如何使用？

2.1 LLM的使用：以qwen2:0.5b为例：

终端交互：

ollama run qwen2:0.5b
python版http请求：

import os
from openai import OpenAI

os.environ['OPENAI_API_KEY'] = 'none'
os.environ['OPENAI_BASE_URL'] = 'http://localhost:11434/v1'

client = OpenAI()
response = client.chat.completions.create(
model="qwen",
messages=[{"role": "user", "content": "你好，你能做什么？"}],
)

print(response.choices[0].message.content)

返回示例：

复制代码

我很乐意为您效劳。我可以帮助您解答关于健康、旅游、教育或其他任何话题的问题。您需要帮助什么？我会尽力提供相应内容。欢迎随时提问！

2.2 表征模型的使用：以bge-large为例

python版http请求：

#!/usr/bin/env python

import requests

if name == 'main':
print('')

复制代码

  url = "http://localhost:11434/api/embed"
  headers = {"Content-Type": "application/json"}
  data = {
      "model": "bge-large",
      "input": ["你好，bge 模型！"]
  }
  response = requests.post(url, json=data, headers=headers)
  print(response.json())

返回示例：

复制代码

{'model': 'bge-large', 'embeddings': [[-0.0070403893, 0.01906589, 0.002659078, 0.056569893, -0.023268774, 0.005505571, 0.056816433, 0.04278964, 0.00044504693, 0.023565972, 0.024955913, 0.019057797, 0.05871248, -0.03599396, -0.045997616, -0.030140555, -0.009543529, -0.053064633, -0.03361538, 0.02916276, 0.06290166, -0.019709377, -0.034709457, -0.011301448, 0.0117891235, 0.027437935, -3.430807e-07, 0.013488718, 0.076188676, 0.03509752, 0.00590025, -0.040265292, -0.027687589, -0.024458922, -0.014245673, -0.021977823, 0.029482568, 0.010672004, -0.008089688, -0.027499154, 0.0050347657, -0.019399296, 0.063457735, -0.020411367, -0.057074193, -0.034824282, 0.009907699, -0.015852498, 0.025248498, -0.045246854, 0.01432494, 0.0109622525, 0.037995335, -0.021219427, -0.027175924, -0.007347304, -0.024817608, 0.012287279, -0.034731314, 0.037794087, 0.037213646, -0.002320361, 0.030508645, -0.0465931, 0.048149984, 0.023971763, -0.027592681, -0.015870372, -0.021268606, 0.0076985643, -0.0006617156, 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