221 lines
7.6 KiB
Markdown
221 lines
7.6 KiB
Markdown
<div align="center">
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<source media="(prefers-color-scheme: dark)" height="200px" srcset="https://github.com/jmorganca/ollama/assets/3325447/56ea1849-1284-4645-8970-956de6e51c3c">
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<img alt="logo" height="200px" src="https://github.com/jmorganca/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
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</picture>
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</div>
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# Ollama
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[](https://discord.gg/ollama)
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Run, create, and share large language models (LLMs).
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> Note: Ollama is in early preview. Please report any issues you find.
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## Download
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- [Download](https://ollama.ai/download) for macOS
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- Download for Windows and Linux (coming soon)
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- Build [from source](#building)
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## Quickstart
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To run and chat with [Llama 2](https://ai.meta.com/llama), the new model by Meta:
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```
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ollama run llama2
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```
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## 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')
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Here are some example open-source models that can be downloaded:
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| Model | Parameters | Size | Download |
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| ------------------------ | ---------- | ----- | ------------------------------- |
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| Llama2 | 7B | 3.8GB | `ollama pull llama2` |
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| Llama2 13B | 13B | 7.3GB | `ollama pull llama2:13b` |
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| Llama2 70B | 70B | 39GB | `ollama pull llama2:70b` |
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| Llama2 Uncensored | 7B | 3.8GB | `ollama pull llama2-uncensored` |
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| Code Llama | 7B | 3.8GB | `ollama pull codellama` |
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| Orca Mini | 3B | 1.9GB | `ollama pull orca-mini` |
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| Vicuna | 7B | 3.8GB | `ollama pull vicuna` |
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| Nous-Hermes | 7B | 3.8GB | `ollama pull nous-hermes` |
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| Nous-Hermes 13B | 13B | 7.3GB | `ollama pull nous-hermes:13b` |
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| Wizard Vicuna Uncensored | 13B | 7.3GB | `ollama pull wizard-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.
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## Examples
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### Pull a public model
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```
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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 updated changes will be pulled.
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### Run a model interactively
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```
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ollama run llama2
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>>> hi
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Hello! How can I help you today?
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```
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For multiline input, you can wrap text with `"""`:
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```
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>>> """Hello,
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... world!
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... """
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I'm a basic program that prints the famous "Hello, world!" message to the console.
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```
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### Run a model non-interactively
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```
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$ ollama run llama2 'tell me a joke'
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Sure! Here's a quick one:
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Why did the scarecrow win an award? Because he was outstanding in his field!
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```
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```
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$ cat <<EOF >prompts.txt
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tell me a joke about llamas
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tell me another one
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EOF
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$ ollama run llama2 <prompts.txt
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>>> tell me a joke about llamas
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Why did the llama refuse to play hide-and-seek?
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nobody likes to be hided!
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>>> tell me another one
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Sure, here's another one:
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Why did the llama go to the bar?
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To have a hay-often good time!
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```
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### Run a model on contents of a text file
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```
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$ ollama run llama2 "summarize this file:" "$(cat README.md)"
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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.
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```
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### Customize a model
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Pull a base 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]
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PARAMETER temperature 1
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# 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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```
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ollama create mario -f ./Modelfile
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ollama run mario
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>>> hi
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Hello! It's your friend Mario.
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```
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For more examples, see the [examples](./examples) directory. For more information on creating a Modelfile, see the [Modelfile](./docs/modelfile.md) documentation.
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### Listing local models
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```
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ollama list
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```
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### Removing local models
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```
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ollama rm llama2
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```
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## Model packages
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### Overview
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Ollama bundles model weights, configurations, and data into a single package, defined by a [Modelfile](./docs/modelfile.md).
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<picture>
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<source media="(prefers-color-scheme: dark)" height="480" srcset="https://github.com/jmorganca/ollama/assets/251292/2fd96b5f-191b-45c1-9668-941cfad4eb70">
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<img alt="logo" height="480" src="https://github.com/jmorganca/ollama/assets/251292/2fd96b5f-191b-45c1-9668-941cfad4eb70">
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</picture>
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## Building
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Install `cmake` and `go`:
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```
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brew install cmake
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brew install go
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```
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Then generate dependencies and build:
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```
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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
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> See the [API documentation](./docs/api.md) for all endpoints.
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Ollama has an API for running and managing models. For example to generate text from a model:
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```
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curl -X POST http://localhost:11434/api/generate -d '{
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"model": "llama2",
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"prompt":"Why is the sky blue?"
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}'
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```
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## Community Projects using Ollama
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| Project | Description |
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| -------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| [LangChain][1] and [LangChain.js][2] | Also, there is a question-answering [example][3]. |
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| [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. |
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| [LiteLLM](https://github.com/BerriAI/litellm) | Lightweight Python package to simplify LLM API calls. |
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| [Discord AI Bot](https://github.com/mekb-turtle/discord-ai-bot) | Interact with Ollama as a chatbot on Discord. |
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| [Raycast Ollama](https://github.com/MassimilianoPasquini97/raycast_ollama) | Raycast extension to use Ollama for local llama inference on Raycast. |
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| [Simple HTML UI](https://github.com/rtcfirefly/ollama-ui) | Also, there is a Chrome extension. |
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| [Emacs client](https://github.com/zweifisch/ollama) | |
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[1]: https://python.langchain.com/docs/integrations/llms/ollama
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[2]: https://js.langchain.com/docs/modules/model_io/models/llms/integrations/ollama
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[3]: https://js.langchain.com/docs/use_cases/question_answering/local_retrieval_qa
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