Get up and running with large language models locally.

Download

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curl https://ollama.ai/install.sh | sh

Manual install instructions

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

To run and chat with Llama 2:

ollama run llama2

Ollama supports a list of open-source models available on ollama.ai/library

Here are some example open-source models that can be downloaded:

Note: You should have at least 8 GB of RAM to run the 3B models, 16 GB to run the 7B models, and 32 GB to run the 13B models.

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
   

See the guide on importing models for more information.

Models from the Ollama library can be customized with a prompt. For example, to customize the llama2 model:

ollama pull llama2

Create a Modelfile:

FROM llama2

# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1

# set the system prompt
SYSTEM """
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 examples, see the examples directory. For more information on working with a Modelfile, see the Modelfile documentation.

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

ollama pull llama2

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

ollama rm llama2

ollama cp llama2 my-llama2

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.

$ ollama run llama2 "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.

ollama list

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

Install cmake and go:

brew install cmake go

Then generate dependencies and build:

go generate ./...
go build .

Next, start the server:

./ollama serve

Finally, in a separate shell, run a model:

./ollama run llama2

Ollama has a REST API for running and managing models. For example, to generate text from a model:

curl http://localhost:11434/api/generate -d '{
  "model": "llama2",
  "prompt":"Why is the sky blue?"
}'

Or send a chat message:

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

See the API documentation for all endpoints.

• HTML UI
• Chatbot UI
• Typescript UI
• Minimalistic React UI for Ollama Models
• Web UI
• Ollamac
• big-AGI
• Cheshire Cat assistant framework
• Amica
• chatd

• oterm
• Ellama Emacs client
• Emacs client
• gen.nvim
• ollama.nvim
• ogpt.nvim
• gptel Emacs client
• Oatmeal

• Pacman

• LangChain and LangChain.js with example
• LangChainGo with example
• LlamaIndex
• LiteLLM
• OllamaSharp for .NET
• Ollama-rs for Rust
• Ollama4j for Java
• ModelFusion Typescript Library
• OllamaKit for Swift
• Ollama for Dart
• Ollama for Laravel

• Maid (Mobile Artificial Intelligence Distribution)

• Raycast extension
• Discollama (Discord bot inside the Ollama discord channel)
• Continue
• Obsidian Ollama plugin
• Logseq Ollama plugin
• Dagger Chatbot
• Discord AI Bot
• Ollama Telegram Bot
• Hass Ollama Conversation
• Rivet plugin
• Llama Coder (Copilot alternative using Ollama)
• Obsidian BMO Chatbot plugin