ollama 本地部署

ollama 本地模型部署

下载说明

支持windows 、 linux 、 macos 多个系统。本文使用windows安装

下载OllamaSetup.exe 根据指导完成安装。

部署使用

在终端查看ollama是否安装完成

bash 复制代码
ollama -v
ollama version is 0.3.9

终端查看ollama 命令说明

bash 复制代码
ollama help
Large language model runner

Usage:
  ollama [flags]
  ollama [command]

Available Commands:
  serve       Start ollama
  create      Create a model from a Modelfile
  show        Show information for a model
  run         Run a model
  pull        Pull a model from a registry
  push        Push a model to a registry
  list        List models
  ps          List running models
  cp          Copy a model
  rm          Remove a model
  help        Help about any command

Flags:
  -h, --help      help for ollama
  -v, --version   Show version information

Use "ollama [command] --help" for more information about a command.

查看当前支持下载的模型

bash 复制代码
ollama list
NAME            ID              SIZE    MODIFIED
llama3.1:latest f66fc8dc39ea    4.7 GB  4 days ago
qwen2:latest    e0d4e1163c58    4.4 GB  2 months ago
llama3:latest   365c0bd3c000    4.7 GB  2 months ago

启动对话模式

bash 复制代码
 ollama run llama3.1
>>> who are you
I'm an artificial intelligence model known as Llama. Llama stands for "Large Language Model Meta AI."

>>> /help
Available Commands:
  /set            Set session variables
  /show           Show model information
  /load <model>   Load a session or model
  /save <model>   Save your current session
  /clear          Clear session context
  /bye            Exit
  /?, /help       Help for a command
  /? shortcuts    Help for keyboard shortcuts

Use """ to begin a multi-line message.

>>> /show
Available Commands:
  /show info         Show details for this model
  /show license      Show model license
  /show modelfile    Show Modelfile for this model
  /show parameters   Show parameters for this model
  /show system       Show system message
  /show template     Show prompt template

>>> /show info
  Model
        arch                    llama
        parameters              8.0B
        quantization            Q4_0
        context length          131072
        embedding length        4096

  Parameters
        stop    "<|start_header_id|>"
        stop    "<|end_header_id|>"
        stop    "<|eot_id|>"

  License
        LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
        Llama 3.1 Version Release Date: July 23, 2024

>>> /show template
{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
{{- if .System }}

{{ .System }}
{{- end }}
{{- if .Tools }}

Cutting Knowledge Date: December 2023

When you receive a tool call response, use the output to format an answer to the orginal user question.

You are a helpful assistant with tool calling capabilities.
{{- end }}
{{- end }}<|eot_id|>
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 }}
{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>
{{- if and $.Tools $last }}

Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.

Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.

{{ range $.Tools }}
{{- . }}
{{ end }}
{{- end }}

{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>

{{ end }}
{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
{{- if .ToolCalls }}

{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
{{- else }}

{{ .Content }}{{ if not $last }}<|eot_id|>{{ end }}
{{- end }}
{{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>

{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>

{{ end }}
{{- end }}
{{- end }}
>>> what yuo want for me
I don't have personal desires or expectations from you. I exist to assist and provide information to the best of
my abilities.

However, if you'd like to engage in conversation or ask questions on a particular topic, I'm here to help!

Here are some ways we can interact:

1. **Ask me anything**: You can ask me any question on any topic.
2. **Learn something new**: I can provide information on various subjects, from science and history to
entertainment and culture.
3. **Chat about a hobby**: If you have a particular interest or hobby, feel free to discuss it with me.
4. **Get help with a problem**: If you're stuck with a problem or need advice, I'll do my best to assist you.

What sounds interesting to you?

>>> Send a message (/? for help)

默认情况下,ollama启动了server 的api访问功能

外部 api访问

使用postman网页版本

需要自己下载下代理。

fork-ollama的配置:ollama-postman

至此,完成本地部署和api访问。 enjoy

相关推荐
白狐_7985 小时前
深度解析:大语言模型(LLM)联网搜索与实时数据获取的底层原理
人工智能·语言模型·自然语言处理
Omics Pro6 小时前
空间组学下一代机器学习与深度学习
大数据·人工智能·深度学习·算法·机器学习·语言模型·自然语言处理
ai_coder_ai8 小时前
在自动化脚本中如何使用AI大语言模型?
语言模型·autojs·自动化脚本·冰狐智能辅助·easyclick
活跃的煤矿打工人8 小时前
【星海出品】智能科普(一)
ai·语言模型
Learn Beyond Limits9 小时前
RNN的多样化用途|The diverse applications of RNN
人工智能·深度学习·神经网络·机器学习·ai·语言模型·自然语言处理
智算菩萨9 小时前
GPT-5.4 进阶思考模式全面解析:从推理等级到实战提示词,代码、论文、数据处理一站通
人工智能·gpt·深度学习·机器学习·语言模型·自然语言处理·chatgpt
FelixZhang02810 小时前
从 PDF 到 AI 知识库:RAG 数据预处理的六步标准流水线 (SOP)
人工智能·python·目标检测·计算机视觉·语言模型·ocr·numpy
前端摸鱼匠10 小时前
【AI大模型春招面试题15】自回归(Autoregressive)与自编码(Autoencoding)语言模型的区别?
人工智能·语言模型·面试·回归·大模型·求职招聘
姚青&10 小时前
OpenAI ChatGPT 大语言模型
人工智能·语言模型·chatgpt
Jump 不二11 小时前
Meta 提出 HyperAgents:让智能体实现自主进化
人工智能·深度学习·语言模型