README.md

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# Ollama

[![Discord](https://dcbadge.vercel.app/api/server/ollama?style=flat&compact=true)](https://discord.gg/ollama)

Get up and running with large language models locally.

### macOS

[Download](https://ollama.ai/download/Ollama-darwin.zip)

### Windows

Coming soon!

### Linux & WSL2

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

[Manual install instructions](https://github.com/jmorganca/ollama/blob/main/docs/linux.md)

### Docker

The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `ollama/ollama` is available on Docker Hub.

## Quickstart

To run and chat with [Llama 2](https://ollama.ai/library/llama2):

```
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                       |
| ------------------ | ---------- | ----- | ------------------------------ |
| Neural Chat        | 7B         | 4.1GB | `ollama run neural-chat`       |
| Starling           | 7B         | 4.1GB | `ollama run starling-lm`       |
| Mistral            | 7B         | 4.1GB | `ollama run mistral`           |
| Llama 2            | 7B         | 3.8GB | `ollama run llama2`            |
| 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 to run the 3B models, 16 GB to run the 7B models, and 32 GB to run the 13B models.

## Customize your own model

### Import from GGUF

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
   ```

### Import from PyTorch or Safetensors

See the [guide](docs/import.md) 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](examples) directory. For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.

## CLI Reference

### Create a model

`ollama create` is used to create a model from a 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

### Generic (CPU)

Install `cmake` and `go`:

```
brew install cmake go
```

Then generate dependencies:
```
go generate ./...
```
Then build the binary:
```
go build .
```

### CUDA (NVIDIA)
*Your operating system distribution may already have packages for NVIDIA CUDA. Distro packages are often preferable, but instructions are distro-specific. Please consult distro-specific docs for dependencies if available!*

Install `cmake` and `golang` as well as [NVIDIA CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) development and runtime packages.
Then generate dependencies:
```
go generate -tags cuda ./...
```
Then build the binary:
```
go build -tags cuda .
```

### ROCm (AMD)
*Your operating system distribution may already have packages for AMD ROCm and CLBlast. Distro packages are often preferable, but instructions are distro-specific. Please consult distro-specific docs for dependencies if available!*

Install [CLBlast](https://github.com/CNugteren/CLBlast/blob/master/doc/installation.md) and [ROCm](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html) developement packages first, as well as `cmake` and `golang`.
Adjust the paths below (correct for Arch) as appropriate for your distributions install locations and generate dependencies:
```
CLBlast_DIR=/usr/lib/cmake/CLBlast ROCM_PATH=/opt/rocm go generate -tags rocm ./...
```
Then build the binary:
```
go build -tags rocm
```

### 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](./docs/api.md) for all endpoints.

## Community Integrations

### Web & Desktop
- [Bionic GPT](https://github.com/bionic-gpt/bionic-gpt)
- [HTML UI](https://github.com/rtcfirefly/ollama-ui)
- [Chatbot UI](https://github.com/ivanfioravanti/chatbot-ollama)
- [Typescript UI](https://github.com/ollama-interface/Ollama-Gui?tab=readme-ov-file)
- [Minimalistic React UI for Ollama Models](https://github.com/richawo/minimal-llm-ui)
- [Web UI](https://github.com/ollama-webui/ollama-webui)
- [Ollamac](https://github.com/kevinhermawan/Ollamac)
- [big-AGI](https://github.com/enricoros/big-agi/blob/main/docs/config-ollama.md)
- [Cheshire Cat assistant framework](https://github.com/cheshire-cat-ai/core)
- [Amica](https://github.com/semperai/amica)
- [chatd](https://github.com/BruceMacD/chatd)

### Terminal

- [oterm](https://github.com/ggozad/oterm)
- [Ellama Emacs client](https://github.com/s-kostyaev/ellama)
- [Emacs client](https://github.com/zweifisch/ollama)
- [gen.nvim](https://github.com/David-Kunz/gen.nvim)
- [ollama.nvim](https://github.com/nomnivore/ollama.nvim)
- [ogpt.nvim](https://github.com/huynle/ogpt.nvim)
- [gptel Emacs client](https://github.com/karthink/gptel)
- [Oatmeal](https://github.com/dustinblackman/oatmeal)
- [cmdh](https://github.com/pgibler/cmdh)

### Database

- [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md)

### Package managers

- [Pacman](https://archlinux.org/packages/extra/x86_64/ollama/)

### Libraries

- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/modules/model_io/models/llms/integrations/ollama) with [example](https://js.langchain.com/docs/use_cases/question_answering/local_retrieval_qa)
- [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example)
- [LlamaIndex](https://gpt-index.readthedocs.io/en/stable/examples/llm/ollama.html)
- [LiteLLM](https://github.com/BerriAI/litellm)
- [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp)
- [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs)
- [Ollama4j for Java](https://github.com/amithkoujalgi/ollama4j)
- [ModelFusion Typescript Library](https://modelfusion.dev/integration/model-provider/ollama)
- [OllamaKit for Swift](https://github.com/kevinhermawan/OllamaKit)
- [Ollama for Dart](https://github.com/breitburg/dart-ollama)
- [Ollama for Laravel](https://github.com/cloudstudio/ollama-laravel)

### Mobile

- [Enchanted](https://github.com/AugustDev/enchanted)
- [Maid](https://github.com/danemadsen/Maid)

### Extensions & Plugins

- [Raycast extension](https://github.com/MassimilianoPasquini97/raycast_ollama)
- [Discollama](https://github.com/mxyng/discollama) (Discord bot inside the Ollama discord channel)
- [Continue](https://github.com/continuedev/continue)
- [Obsidian Ollama plugin](https://github.com/hinterdupfinger/obsidian-ollama)
- [Logseq Ollama plugin](https://github.com/omagdy7/ollama-logseq)
- [Dagger Chatbot](https://github.com/samalba/dagger-chatbot)
- [Discord AI Bot](https://github.com/mekb-turtle/discord-ai-bot)
- [Ollama Telegram Bot](https://github.com/ruecat/ollama-telegram)
- [Hass Ollama Conversation](https://github.com/ej52/hass-ollama-conversation)
- [Rivet plugin](https://github.com/abrenneke/rivet-plugin-ollama)
- [Llama Coder](https://github.com/ex3ndr/llama-coder) (Copilot alternative using Ollama)
- [Obsidian BMO Chatbot plugin](https://github.com/longy2k/obsidian-bmo-chatbot)