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.
Customize a model
Import from GGUF
Ollama supports importing GGUF models in the Modelfile:
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
Create the model in Ollama
ollama create example -f Modelfile
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 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.
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"
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.
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.
Local Multimodal AI Chat (Ollama-based LLM Chat with support for multiple features, including PDF RAG, voice chat, image-based interactions, and integration with OpenAI.)
ARGO (Locally download and run Ollama and Huggingface models with RAG and deep research on Mac/Windows/Linux)
OrionChat - OrionChat is a web interface for chatting with different AI providers
G1 (Prototype of using prompting strategies to improve the LLM's reasoning through o1-like reasoning chains.)
AI Toolkit for Visual Studio Code (Microsoft-official VSCode extension to chat, test, evaluate models with Ollama support, and use them in your AI applications.)
AstrBot (User-friendly LLM-based multi-platform chatbot with a WebUI, supporting RAG, LLM agents, and plugins integration)
Reins (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
Flufy (A beautiful chat interface for interacting with Ollama's API. Built with React, TypeScript, and Material-UI.)
Ellama (Friendly native app to chat with an Ollama instance)
screenpipe Build agents powered by your screen history
Ollamb (Simple yet rich in features, cross-platform built with Flutter and designed for Ollama. Try the web demo.)
Writeopia (Text editor with integration with Ollama)
AppFlowy (AI collaborative workspace with Ollama, cross-platform and self-hostable)
Lumina (A lightweight, minimal React.js frontend for interacting with Ollama servers)
Tiny Notepad (A lightweight, notepad-like interface to chat with ollama available on PyPI)
macLlama (macOS native) (A native macOS GUI application for interacting with Ollama models, featuring a chat interface.)
GPTranslate (A fast and lightweight, AI powered desktop translation application written with Rust and Tauri. Features real-time translation with OpenAI/Azure/Ollama.)
ollama launcher (A launcher for Ollama, aiming to provide users with convenient functions such as ollama server launching, management, or configuration.)
ai-hub (AI Hub supports multiple models via API keys and Chat support via Ollama API.)
aichat All-in-one LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI tools & agents, with access to OpenAI, Claude, Gemini, Ollama, Groq, and more.
PowershAI PowerShell module that brings AI to terminal on Windows, including support for Ollama
DeepShell Your self-hosted AI assistant. Interactive Shell, Files and Folders analysis.
orbiton Configuration-free text editor and IDE with support for tab completion with Ollama.
orca-cli Ollama Registry CLI Application - Browse, pull, and download models from Ollama Registry in your terminal.
GGUF-to-Ollama - Importing GGUF to Ollama made easy (multiplatform)
ollama-multirun - A bash shell script to run a single prompt against any or all of your locally installed ollama models, saving the output and performance statistics as easily navigable web pages. (Demo)
ollama-bash-toolshed - Bash scripts to chat with tool using models. Add new tools to your shed with ease. Runs on Ollama.
Apple Vision Pro
SwiftChat (Cross-platform AI chat app supporting Apple Vision Pro via "Designed for iPad")
Local AI Helper (Chrome and Firefox extensions that enable interactions with the active tab and customisable API endpoints. Includes secure storage for user prompts.)
vnc-lm (Discord bot for messaging with LLMs through Ollama and LiteLLM. Seamlessly move between local and flagship models.)
LSP-AI (Open-source language server for AI-powered functionality)
QodeAssist (AI-powered coding assistant plugin for Qt Creator)
Opik is an open-source platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. Opik supports native intergration to Ollama.
Lunary is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing.
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.
MLflow Tracing is an open source LLM observability tool with a convenient API to log and visualize traces, making it easy to debug and evaluate GenAI applications.