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Ollama快速搭建与使用

#AI#Github

ollama

Ollama

Get up and running with large language models.

macOS

Download

Windows

Download

Linux

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

Manual install instructions

Docker

The official Ollama Docker image ollama/ollama is available on Docker Hub.

Libraries

Community

Quickstart

To run and chat with Llama 3.2:

ollama run llama3.2

Model library

Ollama supports a list of models available on ollama.com/library

Here are some example models that can be downloaded:

Model

Parameters

Size

Download

Llama 3.3

70B

43GB

ollama run llama3.3

Llama 3.2

3B

2.0GB

ollama run llama3.2

Llama 3.2

1B

1.3GB

ollama run llama3.2:1b

Llama 3.2 Vision

11B

7.9GB

ollama run llama3.2-vision

Llama 3.2 Vision

90B

55GB

ollama run llama3.2-vision:90b

Llama 3.1

8B

4.7GB

ollama run llama3.1

Llama 3.1

405B

231GB

ollama run llama3.1:405b

Phi 4

14B

9.1GB

ollama run phi4

Phi 3 Mini

3.8B

2.3GB

ollama run phi3

Gemma 2

2B

1.6GB

ollama run gemma2:2b

Gemma 2

9B

5.5GB

ollama run gemma2

Gemma 2

27B

16GB

ollama run gemma2:27b

Mistral

7B

4.1GB

ollama run mistral

Moondream 2

1.4B

829MB

ollama run moondream

Neural Chat

7B

4.1GB

ollama run neural-chat

Starling

7B

4.1GB

ollama run starling-lm

Code Llama

7B

3.8GB

ollama run codellama

Llama 2 Uncensored

7B

3.8GB

ollama run llama2-uncensored

LLaVA

7B

4.5GB

ollama run llava

Solar

10.7B

6.1GB

ollama run solar

[!NOTE]
You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.

Customize a model

Import from GGUF

Ollama supports importing GGUF models in the Modelfile:

  1. Create a file named Modelfile, with a FROM instruction with the local filepath to the model you want to import.

    FROM ./vicuna-33b.Q4_0.gguf

  2. Create the model in Ollama

    ollama create example -f Modelfile

  3. Run the model

    ollama run example

Import from Safetensors

See the guide on importing models for more information.

Customize a prompt

Models from the Ollama library can be customized with a prompt. For example, to customize the llama3.2 model:

ollama pull llama3.2

Create a Modelfile:

FROM llama3.2
set the temperature to 1 [higher is more creative, lower is more coherent]PARAMETER temperature 1set the system messageSYSTEM """

You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.

"""

Next, create and run the model:

ollama create mario -f ./Modelfile
ollama run mario
>>> hi
Hello! It's your friend Mario.

For more information on working with a Modelfile, see the Modelfile documentation.

CLI Reference

Create a model

ollama create is used to create a model from a Modelfile.

ollama create mymodel -f ./Modelfile

Pull a model

ollama pull llama3.2

This command can also be used to update a local model. Only the diff will be pulled.

Remove a model

ollama rm llama3.2

Copy a model

ollama cp llama3.2 my-model

Multiline input

For multiline input, you can wrap text with """:

>>> """Hello,
... world!
... """
I'm a basic program that prints the famous "Hello, world!" message to the console.

Multimodal models

ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"
The image features a yellow smiley face, which is likely the central focus of the picture.

Pass the prompt as an argument

$ ollama run llama3.2 "Summarize this file: $(cat README.md)"
 Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.

Show model information

ollama show llama3.2

List models on your computer

ollama list

List which models are currently loaded

ollama ps

Stop a model which is currently running

ollama stop llama3.2

Start Ollama

ollama serve is used when you want to start ollama without running the desktop application.

Building

See the developer guide

Running local builds

Next, start the server:

./ollama serve

Finally, in a separate shell, run a model:

./ollama run llama3.2

REST API

Ollama has a REST API for running and managing models.

Generate a response

curl http://localhost:11434/api/generate -d '{
  "model": "llama3.2",
  "prompt":"Why is the sky blue?"
}'

Chat with a model

curl http://localhost:11434/api/chat -d '{
  "model": "llama3.2",
  "messages": [
    { "role": "user", "content": "why is the sky blue?" }
  ]
}'

See the API documentation for all endpoints.

Community Integrations

Web & Desktop

Cloud

Terminal

Apple Vision Pro

Database

  • pgai - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)

  • MindsDB (Connects Ollama models with nearly 200 data platforms and apps)

  • chromem-go with example

  • Kangaroo (AI-powered SQL client and admin tool for popular databases)

Package managers

Libraries

Mobile

  • Enchanted

  • Maid

  • Ollama App (Modern and easy-to-use multi-platform client for Ollama)

  • ConfiChat (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)

Extensions & Plugins

Supported backends

  • llama.cpp project founded by Georgi Gerganov.

Observability

  • OpenLIT is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.

  • HoneyHive is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.

  • Langfuse is an open source LLM observability platform that enables teams to collaboratively monitor, evaluate and debug AI applications.

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