Co-authored-by: bo.sun <bo.sun@cotticoffee.com>
11 KiB
Ollama
Get up and running with large language models locally.
macOS
Windows preview
Linux
curl -fsSL https://ollama.com/install.sh | sh
Docker
The official Ollama Docker image ollama/ollama
is available on Docker Hub.
Libraries
Quickstart
To run and chat with Llama 2:
ollama run llama2
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 2 | 7B | 3.8GB | ollama run llama2 |
Mistral | 7B | 4.1GB | ollama run mistral |
Dolphin Phi | 2.7B | 1.6GB | ollama run dolphin-phi |
Phi-2 | 2.7B | 1.7GB | ollama run phi |
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 |
Llama 2 13B | 13B | 7.3GB | ollama run llama2:13b |
Llama 2 70B | 70B | 39GB | ollama run llama2:70b |
Orca Mini | 3B | 1.9GB | ollama run orca-mini |
Vicuna | 7B | 3.8GB | ollama run vicuna |
LLaVA | 7B | 4.5GB | ollama run llava |
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 aFROM
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 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 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 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 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.
ollama create mymodel -f ./Modelfile
Pull a model
ollama pull llama2
This command can also be used to update a local model. Only the diff will be pulled.
Remove a model
ollama rm llama2
Copy a model
ollama cp llama2 my-llama2
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
>>> 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 in prompt as arguments
$ 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.
List models on your computer
ollama list
Start Ollama
ollama serve
is used when you want to start ollama without running the desktop application.
Building
Install cmake
and go
:
brew install cmake go
Then generate dependencies:
go generate ./...
Then build the binary:
go build .
More detailed instructions can be found in the developer guide
Running local builds
Next, start the server:
./ollama serve
Finally, in a separate shell, run a model:
./ollama run llama2
REST API
Ollama has a REST API for running and managing models.
Generate a response
curl http://localhost:11434/api/generate -d '{
"model": "llama2",
"prompt":"Why is the sky blue?"
}'
Chat with a model
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.
Community Integrations
Web & Desktop
- Bionic GPT
- HTML UI
- Chatbot UI
- Typescript UI
- Minimalistic React UI for Ollama Models
- Open WebUI
- Ollamac
- big-AGI
- Cheshire Cat assistant framework
- Amica
- chatd
- Ollama-SwiftUI
- MindMac
- NextJS Web Interface for Ollama
- Msty
Terminal
- oterm
- Ellama Emacs client
- Emacs client
- gen.nvim
- ollama.nvim
- ollama-chat.nvim
- ogpt.nvim
- gptel Emacs client
- Oatmeal
- cmdh
- tenere
- llm-ollama for Datasette's LLM CLI.
- ShellOracle
Database
Package managers
Libraries
- LangChain and LangChain.js with example
- LangChainGo with example
- LlamaIndex
- LangChain4j
- LiteLLM
- OllamaSharp for .NET
- Ollama for Ruby
- Ollama-rs for Rust
- Ollama4j for Java
- ModelFusion Typescript Library
- OllamaKit for Swift
- Ollama for Dart
- Ollama for Laravel
- LangChainDart
- Semantic Kernel - Python
- Haystack
- Elixir LangChain
- Ollama for R - rollama
- Ollama-ex for Elixir
Mobile
Extensions & Plugins
- 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
- Open Interpreter
- twinny (Copilot and Copilot chat alternative using Ollama)
- Wingman-AI (Copilot code and chat alternative using Ollama and HuggingFace)
- Page Assist (Chrome Extension)