# Ollama
[](https://discord.gg/ollama)
Run, create, and share large language models (LLMs).
> Note: Ollama is in early preview. Please report any issues you find.
## Download
- [Download](https://ollama.ai/download) for macOS
- Download for Windows and Linux (coming soon)
- Build [from source](#building)
## Quickstart
To run and chat with [Llama 2](https://ai.meta.com/llama), the new model by Meta:
```
ollama run llama2
```
## Model library
Ollama supports a list of open-source models available on [ollama.ai/library](https://ollama.ai/library 'ollama model library')
Here are some example open-source models that can be downloaded:
| Model | Parameters | Size | Download |
| ------------------------ | ---------- | ----- | ------------------------------- |
| Llama2 | 7B | 3.8GB | `ollama pull llama2` |
| Llama2 13B | 13B | 7.3GB | `ollama pull llama2:13b` |
| Llama2 70B | 70B | 39GB | `ollama pull llama2:70b` |
| Llama2 Uncensored | 7B | 3.8GB | `ollama pull llama2-uncensored` |
| Code Llama | 7B | 3.8GB | `ollama pull codellama` |
| Orca Mini | 3B | 1.9GB | `ollama pull orca-mini` |
| Vicuna | 7B | 3.8GB | `ollama pull vicuna` |
| Nous-Hermes | 7B | 3.8GB | `ollama pull nous-hermes` |
| Nous-Hermes 13B | 13B | 7.3GB | `ollama pull nous-hermes:13b` |
| Wizard Vicuna Uncensored | 13B | 7.3GB | `ollama pull wizard-vicuna` |
> 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.
## Examples
### Pull a public model
```
ollama pull llama2
```
> This command can also be used to update a local model. Only updated changes will be pulled.
### Run a model interactively
```
ollama run llama2
>>> hi
Hello! How can I help you today?
```
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.
```
### Run a model non-interactively
```
$ ollama run llama2 'tell me a joke'
Sure! Here's a quick one:
Why did the scarecrow win an award? Because he was outstanding in his field!
```
```
$ cat <prompts.txt
tell me a joke about llamas
tell me another one
EOF
$ ollama run llama2 >> tell me a joke about llamas
Why did the llama refuse to play hide-and-seek?
nobody likes to be hided!
>>> tell me another one
Sure, here's another one:
Why did the llama go to the bar?
To have a hay-often good time!
```
### Run a model on contents of a text file
```
$ 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.
```
### Customize a model
Pull a base 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](./examples) directory. For more information on creating a Modelfile, see the [Modelfile](./docs/modelfile.md) documentation.
### Listing local models
```
ollama list
```
### Removing local models
```
ollama rm llama2
```
## Model packages
### Overview
Ollama bundles model weights, configurations, and data into a single package, defined by a [Modelfile](./docs/modelfile.md).
## Building
Install `cmake` and `go`:
```
brew install cmake
brew install 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
```
## REST API
> See the [API documentation](./docs/api.md) for all endpoints.
Ollama has an API for running and managing models. For example to generate text from a model:
```
curl -X POST http://localhost:11434/api/generate -d '{
"model": "llama2",
"prompt":"Why is the sky blue?"
}'
```
## Community Projects using Ollama
| Project | Description |
| -------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [LangChain][1] and [LangChain.js][2] | Also, there is a question-answering [example][3]. |
| [Continue](https://github.com/continuedev/continue) | Embeds Ollama inside Visual Studio Code. The extension lets you highlight code to add to the prompt, ask questions in the sidebar, and generate code inline. |
| [LiteLLM](https://github.com/BerriAI/litellm) | Lightweight Python package to simplify LLM API calls. |
| [Discord AI Bot](https://github.com/mekb-turtle/discord-ai-bot) | Interact with Ollama as a chatbot on Discord. |
| [Raycast Ollama](https://github.com/MassimilianoPasquini97/raycast_ollama) | Raycast extension to use Ollama for local llama inference on Raycast. |
| [Simple HTML UI](https://github.com/rtcfirefly/ollama-ui) | Also, there is a Chrome extension. |
| [Emacs client](https://github.com/zweifisch/ollama) | |
[1]: https://python.langchain.com/docs/integrations/llms/ollama
[2]: https://js.langchain.com/docs/modules/model_io/models/llms/integrations/ollama
[3]: https://js.langchain.com/docs/use_cases/question_answering/local_retrieval_qa