ollama
# 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.com/download/Ollama-darwin.zip) ### Windows preview [Download](https://ollama.com/download/OllamaSetup.exe) ### Linux ``` curl -fsSL https://ollama.com/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. ### Libraries - [ollama-python](https://github.com/ollama/ollama-python) - [ollama-js](https://github.com/ollama/ollama-js) ## Quickstart To run and chat with [Llama 2](https://ollama.com/library/llama2): ``` ollama run llama2 ``` ## Model library Ollama supports a list of models available on [ollama.com/library](https://ollama.com/library 'ollama model 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` | | Gemma | 2B | 1.4GB | `ollama run gemma:2b` | | Gemma | 7B | 4.8GB | `ollama run gemma:7b` | > 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: 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. ``` 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](https://github.com/jmorganca/ollama/blob/main/docs/development.md) ### 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 Browse [the list of community integrations](./docs/community.md) to see tools built on Ollama.