ollama

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Ollama

Get up and running with large language models.

macOS

Download

Windows preview

Download

Linux

Manual install instructions

Docker

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

Libraries

Quickstart

To run and chat with Llama 3.2:

Model library

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

Here are some example models that can be downloaded:

[!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.

  2. Create the model in Ollama

  3. Run the model

Import from PyTorch or 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:

Create a Modelfile:

Next, create and run the model:

For more examples, see the examples directory. 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.

Pull a model

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

Remove a model

Copy a model

Multiline input

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

Multimodal models

Pass the prompt as an argument

Show model information

List models on your computer

List which models are currently loaded

Stop a model which is currently running

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:

Finally, in a separate shell, run a model:

REST API

Ollama has a REST API for running and managing models.

Generate a response

Chat with a model

See the API documentation for all endpoints.

Community Integrations

Web & Desktop

Terminal

Apple Vision Pro

Database

Package managers

Libraries

Mobile

  • Enchanted
  • Maid
  • 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.

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