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ollama/ollama: Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.

 

Get up and running with large language models.

Download

Download

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

Manual install instructions

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

To run and chat with Gemma 3:

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

Here are some example models that can be downloaded:

Model Parameters Size Download Gemma 3 1B 815MB ollama run gemma3:1b Gemma 3 4B 3.3GB ollama run gemma3 Gemma 3 12B 8.1GB ollama run gemma3:12b Gemma 3 27B 17GB ollama run gemma3:27b QwQ 32B 20GB ollama run qwq DeepSeek-R1 7B 4.7GB ollama run deepseek-r1 DeepSeek-R1 671B 404GB ollama run deepseek-r1:671b Llama 4 109B 67GB ollama run llama4:scout Llama 4 400B 245GB ollama run llama4:maverick 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 4 Mini 3.8B 2.5GB ollama run phi4-mini 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 Granite-3.3 8B 4.9GB ollama run granite3.3

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.

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

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 llama3.2 model:

Create a Modelfile:

FROM llama3.2

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

# set the system message
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 information on working with a Modelfile, see the Modelfile documentation.

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

ollama create mymodel -f ./Modelfile

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

ollama cp llama3.2 my-model

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 llava "What's in this image? /Users/jmorgan/Desktop/smile.png"

Output: 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)"

Output: 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.

List models on your computer List which models are currently loaded Stop a model which is currently running

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

See the developer guide

Next, start the server:

Finally, in a separate shell, run a model:

Ollama has a REST API for running and managing models.

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

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