ollama/README.md

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# Ollama
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[![Discord](https://dcbadge.vercel.app/api/server/ollama?style=flat&compact=true)](https://discord.gg/ollama)
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Get up and running with large language models locally.
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### macOS
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[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
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The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `ollama/ollama` is available on Docker Hub.
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## Quickstart
To run and chat with [Llama 2](https://ollama.ai/library/llama2):
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```
ollama run llama2
```
## Model library
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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:
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| 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` |
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> 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
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### Import from GGUF
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Ollama supports importing GGUF models in the Modelfile:
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1. Create a file named `Modelfile`, with a `FROM` instruction with the local filepath to the model you want to import.
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```
FROM ./vicuna-33b.Q4_0.gguf
```
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2. Create the model in Ollama
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```
ollama create example -f Modelfile
```
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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
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Models from the Ollama library can be customized with a prompt. For example, to customize the `llama2` model:
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```
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ollama pull llama2
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```
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Create a `Modelfile`:
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```
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FROM llama2
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# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# set the system prompt
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SYSTEM """
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You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
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"""
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```
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Next, create and run the model:
```
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ollama create mario -f ./Modelfile
ollama run mario
>>> hi
Hello! It's your friend Mario.
```
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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.
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### Pull a model
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```
ollama pull llama2
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```
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> This command can also be used to update a local model. Only the diff will be pulled.
### Remove a model
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```
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ollama rm llama2
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```
### 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.
```
### Pass in prompt as arguments
```
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$ 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
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```
ollama list
```
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### Start Ollama
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`ollama serve` is used when you want to start ollama without running the desktop application.
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## Building
Install `cmake` and `go`:
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```
brew install cmake go
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```
Then generate dependencies and build:
```
go generate ./...
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go build .
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```
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Next, start the server:
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```
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./ollama serve
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```
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Finally, in a separate shell, run a model:
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```
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./ollama run llama2
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```
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## REST API
Ollama has a REST API for running and managing models.
For example, to generate text from a model:
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```
curl http://localhost:11434/api/generate -d '{
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"model": "llama2",
"prompt":"Why is the sky blue?"
}'
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```
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Or send a chat message (coming in 0.1.14):
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```
curl http://localhost:11434/api/chat -d '{
"model": "mistral",
"messages": [
{ "role": "user", "content": "why is the sky blue?" }
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]
}'
```
See the [API documentation](./docs/api.md) for all endpoints.
## Community Integrations
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### Web & Desktop
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- [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)
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- [chatd](https://github.com/BruceMacD/chatd)
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### Terminal
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- [oterm](https://github.com/ggozad/oterm)
- [Ellama Emacs client](https://github.com/s-kostyaev/ellama)
- [Emacs client](https://github.com/zweifisch/ollama)
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- [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)
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- [gptel Emacs client](https://github.com/karthink/gptel)
- [Oatmeal](https://github.com/dustinblackman/oatmeal)
### Package managers
- [Pacman](https://archlinux.org/packages/extra/x86_64/ollama/)
### Libraries
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- [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)
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- [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs)
- [Ollama4j for Java](https://github.com/amithkoujalgi/ollama4j)
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- [ModelFusion Typescript Library](https://modelfusion.dev/integration/model-provider/ollama)
- [OllamaKit for Swift](https://github.com/kevinhermawan/OllamaKit)
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- [Ollama for Dart](https://github.com/breitburg/dart-ollama)
- [Ollama for Laravel](https://github.com/cloudstudio/ollama-laravel)
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### Mobile
- [Maid](https://github.com/danemadsen/Maid) (Mobile Artificial Intelligence Distribution)
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### 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)
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- [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)
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- [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)