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Ollama：从入门到进阶 本文从开源工具Ollama的核心价值出发，系统介绍了其在大型语言模型（LLM）领域的应用优势。作为免费开源平台，Ollama显著降低了LLM的使用门槛，支持一键下载和管理Llama 3、Mistral等热门模型。文档详细解析了跨平台安装方法（macOS/Windows/Linux/Docker）、命令行操作流程（模型拉取、运行、交互），以及关键环境变量配置。重点阐述了基于HTTP的API设计，涵盖文本生成、多轮对话、模型管理等核心端点，并提供Python/JavaScript/curl的完整调用示例。此外，深入说明了Ollama与OpenAI API的兼容性方案，助力开发者无缝迁移现有项目，展现了其作为轻量级LLM部署方案的实用价值。 近年来，大型语言模型（LLM）以其强大的文本生成和理解能力，成为了人工智能领域的中坚力量。商业 LLM 的价格通常高昂且代码封闭，限制了研究者和开发者的探索空间。幸运的是，开源社区提供了像 Ollama 这样优秀的替代方案，让每个人都能够轻松体验 LLM 的魅力，并能结合 HPC 和 IDE 插件，打造更强大的个人助手。 什么是 Ollama？Ollama 是一个用于构建大型语言模型应用的工具，它提供了一个简洁易用的命令行界面和服务器，让你能够轻松下载、运行和管理各种开源 LLM。与需要复杂配置和强大硬件的传统 LLM 不同，Ollama 让你能够方便地像使用手机 App 一样体验 LLM 的强大功能。 Ollama 的优势Ollama 拥有以下显著优势： · 开源免费： Ollama 及其支持的模型完全开源免费，任何人都可以自由使用、修改和分发。
· 简单易用： 无需复杂的配置和安装过程，只需几条命令即可启动和运行 Ollama。
· 模型丰富： Ollama 支持 Llama 3、Mistral、Qwen2 等众多热门开源 LLM，并提供一键下载和切换功能。
· 资源占用低： 相比于商业 LLM，Ollama 对硬件要求更低，即使在普通笔记本电脑上也能流畅运行。
· 社区活跃： Ollama 拥有庞大且活跃的社区，用户可以轻松获取帮助、分享经验和参与模型开发。 如何使用 Ollama？使用 Ollama 非常简单，只需要按照以下步骤： · 安装 Ollama： 根据你的操作系统，从 Ollama 官网 下载并安装最新版本。
· 启动 Ollama： 打开终端或命令行，输入 ollama serve 命令启动 Ollama 服务器。
· 下载模型： 在模型仓库 找到想要的模型，然后使用 ollama pull 命令下载，例如 ollama pull llama3:70b 。
· 运行模型： 使用 ollama run 命令启动模型，例如 ollama run llama3:70b 。
· 开始聊天： 在终端中输入你的问题或指令，Ollama 会根据模型生成相应的回复。 安装 OllamamacOSWindowsLinuxcurl -fsSL https://ollama.com/install.sh | shDockerCPU 版本docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollamaGPU 版本docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama启动 Ollamaollama serve输出以下信息表示 Ollama 服务器已成功启动（V100 机器）： $ ollama serve### 省略的日志输出 ###Listening on [::]:11434 (version 0.1.42)下载模型ollama pull deepseek-r1:1.5b运行模型ollama run deepseek-r1:1.5b例如，运行如下命令后： $ ollama run deepseek-r1:1.5b>>> who are you<think></think>Greetings! I'm DeepSeek-R1, an artificial intelligence assistant created by DeepSeek. I'm at your service andwould be delighted to assist you with any inquiries or tasks you may have.>>> Send a message (/? for help)Docker 容器中运行模型docker exec -it ollama ollama run deepseek-r1:1.5b配置 OllamaOllama 提供了多种环境变量以供配置： 名称解释默认值
OLLAMA_DEBUG是否开启调试模式false
OLLAMA_FLASH_ATTENTION是否闪烁注意力true
OLLAMA_HOSTOllama 服务器的主机地址empty
OLLAMA_KEEP_ALIVE保持连接的时间5m
OLLAMA_LLM_LIBRARYLLM 库empty
OLLAMA_MAX_LOADED_MODELS最大加载模型数1
OLLAMA_MAX_QUEUE最大队列数empty
OLLAMA_MAX_VRAM最大虚拟内存empty
OLLAMA_MODELS模型目录empty
OLLAMA_NOHISTORY是否保存历史记录false
OLLAMA_NOPRUNE是否启用剪枝false
OLLAMA_NUM_PARALLEL并行数1
OLLAMA_ORIGINS允许的来源empty
OLLAMA_RUNNERS_DIR运行器目录empty
OLLAMA_SCHED_SPREAD调度分布empty
OLLAMA_TMPDIR临时文件目录emptyOllama API 交互Ollama 提供了基于 HTTP 的 API，允许开发者通过编程方式与模型进行交互。 本文将详细介绍 Ollama API 的详细使用方法，包括请求格式、响应格式以及示例代码。 1. 启动 Ollama 服务在使用 API 之前，需要确保 Ollama 服务正在运行。可以通过以下命令启动服务： ollama serve设置环境变量OLLAMA_HOST = 0.0.0.0// OLLAMA_MODELS = D:\ModelsOLLAMA_ORIGINS = *2. API 端点Ollama 提供了以下主要 API 端点： 生成文本（Generate Text）端点：POST /api/generate功能：向模型发送提示词（prompt），并获取生成的文本。请求格式：{ "model": "<model-name>", // 模型名称 "prompt": "<input-text>", // 输入的提示词 "stream": false, // 是否启用流式响应（默认 false） "options": { // 可选参数 "temperature": 0.7, // 温度参数 "max_tokens": 100 // 最大 token 数 }}响应格式：{ "response": "<generated-text>", // 生成的文本 "done": true // 是否完成}聊天（Chat）端点：POST /api/chat功能：支持多轮对话，模型会记住上下文。请求格式：{ "model": "<model-name>", // 模型名称 "messages": [ // 消息列表 { "role": "user", // 用户角色 "content": "<input-text>" // 用户输入 } ], "stream": false, // 是否启用流式响应 "options": { // 可选参数 "temperature": 0.7, "max_tokens": 100 }}响应格式：{ "message": { "role": "assistant", // 助手角色 "content": "<generated-text>" // 生成的文本 }, "done": true}列出本地模型（List Models）端点：GET /api/tags功能：列出本地已下载的模型。响应格式：{ "models": [ { "name": "<model-name>", // 模型名称 "size": "<model-size>", // 模型大小 "modified_at": "<timestamp>" // 修改时间 } ]}拉取模型（Pull Model）端点：POST /api/pull功能：从模型库中拉取模型。请求格式：{ "name": "<model-name>" // 模型名称}响应格式：{ "status": "downloading", // 下载状态 "digest": "<model-digest>" // 模型摘要}3. 使用示例生成文本使用 curl 发送请求： curl http://localhost:11434/api/generate -d '{ "model": "deepseek-r1:1.5b", "prompt": "你好，你能帮我写一段代码吗？", "stream": false}'多轮对话使用 curl 发送请求： curl http://localhost:11434/api/chat -d '{ "model": "deepseek-r1:1.5b", "messages": [ { "role": "user", "content": "你好，你能帮我写一段 Python 代码吗？" } ], "stream": false}'列出本地模型使用 curl 发送请求： curl http://localhost:11434/api/tags拉取模型使用 curl 发送请求： curl http://localhost:11434/api/pull -d '{ "name": "deepseek-r1:1.5b"}'4. 流式响应Ollama 支持流式响应（streaming response），适用于实时生成文本的场景。 ##启用流式响应
在请求中设置 "stream": true，API 会逐行返回生成的文本。 curl http://localhost:11434/api/generate -d '{ "model": "deepseek-r1:1.5b", "prompt": "你好，你能帮我写一段代码吗？", "stream": true}'响应格式每行返回一个 JSON 对象： { "response": "<partial-text>", // 部分生成的文本 "done": false // 是否完成}5. 编程语言示例Python 使用 requests 库与 Ollama API 交互： import requests# 生成文本response = requests.post( "http://localhost:11434/api/generate", json={ "model": "deepseek-r1:1.5b", "prompt": "你好，你能帮我写一段代码吗？", "stream": False })print(response.json())多轮对话:response = requests.post( "http://localhost:11434/api/chat", json={ "model": "deepseek-r1:1.5b", "messages": [ { "role": "user", "content": "你好，你能帮我写一段 Python 代码吗？" } ], "stream": False })print(response.json())JavaScript 使用 fetch API 与 Ollama 交互： // 生成文本fetch("http://localhost:11434/api/generate", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ model: "deepseek-r1:1.5b", prompt: "你好，你能帮我写一段代码吗？", stream: false })}) .then(response => response.json()) .then(data => console.log(data));多轮对话: fetch("http://localhost:11434/api/chat", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ model: "deepseek-r1:1.5b", messages: [ { role: "user", content: "你好，你能帮我写一段 Python 代码吗？" } ], stream: false })}) .then(response => response.json()) .then(data => console.log(data));Ollama OpenAI API 交互使用方法OpenAI Python 库 from openai import OpenAIclient = OpenAI( base_url='http://localhost:11434/v1/', # required but ignored api_key='ollama',)chat_completion = client.chat.completions.create( messages=[ { 'role': 'user', 'content': 'Say this is a test', } ], model='deepseek-r1:1.5b',)response = client.chat.completions.create( model="llava", messages=[ { "role": "user", "content": [ {"type": "text", "text": "What's in this image?"}, { "type": "image_url", "image_url": 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}, ], } ], max_tokens=300,)completion = client.completions.create( model="deepseek-r1:1.5b", prompt="Say this is a test",)list_completion = client.models.list()model = client.models.retrieve("deepseek-r1:1.5b")embeddings = client.embeddings.create( model="all-minilm", input=["why is the sky blue?", "why is the grass green?"],)OpenAI JavaScript 库import OpenAI from "openai";const openai = new OpenAI({ baseURL: "http://localhost:11434/v1/", // required but ignored apiKey: "ollama",});const chatCompletion = await openai.chat.completions.create({ messages: [{ role: "user", content: "Say this is a test" }], model: "deepseek-r1:1.5b",});const response = await openai.chat.completions.create({ model: "llava", messages: [ { role: "user", content: [ { type: "text", text: "What's in this image?" }, { type: "image_url", image_url: "data:image/png;base64,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", }, ], }, ],});const completion = await openai.completions.create({ model: "deepseek-r1:1.5b", prompt: "Say this is a test.",});const listCompletion = await openai.models.list();const model = await openai.models.retrieve("deepseek-r1:1.5b");const embedding = await openai.embeddings.create({ model: "all-minilm", input: ["why is the sky blue?", "why is the grass green?"],});curlcurl http://localhost:11434/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek-r1:1.5b", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Hello!" } ] }'curl http://localhost:11434/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "llava", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "What'\''s in this image?" }, { "type": "image_url", "image_url": { "url": "data:image/png;base64,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" } } ] } ], "max_tokens": 300 }'curl http://localhost:11434/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek-r1:1.5b", "prompt": "Say this is a test" }'curl http://localhost:11434/v1/modelscurl http://localhost:11434/v1/models/deepseek-r1:1.5bcurl http://localhost:11434/v1/embeddings \ -H "Content-Type: application/json" \ -d '{ "model": "all-minilm", "input": ["why is the sky blue?", "why is the grass green?"] }'端点/v1/chat/completions支持的功能[x] Chat completions
[x] 流式传输
[x] JSON 模式
[x] 可重复的输出
[x] 视觉
[x] 工具（流式传输支持即将推出）
[ ] 对数概率 支持的请求字段[x] model
[x] messages
[x] 文本 content
[x] 图像 content
[x] Base64 编码的图像
[ ] 图像 URL
[x] content 部分的数组
[x] frequency_penalty
[x] presence_penalty
[x] response_format
[x] seed
[x] stop
[x] stream
[x] temperature
[x] top_p
[x] max_tokens
[x] tools
[ ] tool_choice
[ ] logit_bias
[ ] user
[ ] n /v1/completions支持的功能[x] Completions
[x] 流式传输
[x] JSON 模式
[x] 可重复的输出
[ ] 对数概率 支持的请求字段[x] model
[x] prompt
[x] frequency_penalty
[x] presence_penalty
[x] seed
[x] stop
[x] stream
[x] temperature
[x] top_p
[x] max_tokens
[x] suffix
[ ] best_of
[ ] echo
[ ] logit_bias
[ ] user
[ ] n 注意事项prompt 目前仅接受字符串 /v1/models注意事项created 对应模型最后修改的时间
owned_by 对应 ollama 用户名，默认为 "library" /v1/models/{model}注意事项created 对应模型最后修改的时间
owned_by 对应 ollama 用户名，默认为 "library" /v1/embeddings支持的请求字段[x] model
[x] input
[x] 字符串
[x] 字符串数组
[ ] 令牌数组
[ ] 令牌数组的数组
[ ] encoding format
[ ] dimensions
[ ] user 模型在使用模型之前，请将其拉取到本地 ollama pull： ollama pull deepseek-r1:1.5b默认模型名称对于依赖默认 OpenAI 模型名称（如 gpt-3.5-turbo）的工具，使用 ollama cp 将现有模型名称复制到一个临时名称： ollama cp deepseek-r1:1.5b gpt-3.5-turbo之后，这个新的模型名称可以指定在 model 字段中： curl http://localhost:11434/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-3.5-turbo", "messages": [ { "role": "user", "content": "Hello!" } ] }'设置上下文大小OpenAI API 没有提供设置模型上下文大小的方法。如果你需要更改上下文大小，可以创建一个 Modelfile，其内容如下： FROM <some model>PARAMETER num_ctx <context size>使用 ollama create mymodel 命令创建一个具有更新上下文大小的新模型。使用更新后的模型名称调用 API： curl http://localhost:11434/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek-r1:1.5b", "messages": [ { "role": "user", "content": "Hello!" } ] }
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