mirror of
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217 lines
9.3 KiB
Markdown
217 lines
9.3 KiB
Markdown
<h1 align="center">
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<br>
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<img height="300" src="https://user-images.githubusercontent.com/2420543/233147843-88697415-6dbf-4368-a862-ab217f9f7342.jpeg"> <br>
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LocalAI
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<br>
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</h1>
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[](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml) [](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml)
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[](https://discord.gg/uJAeKSAGDy)
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**LocalAI** is a drop-in replacement REST API that’s compatible with OpenAI API specifications for local inferencing. It allows you to run LLMs (and not only) locally or on-prem with consumer grade hardware, supporting multiple model families that are compatible with the ggml format. Does not require GPU.
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For a list of the supported model families, please see [the model compatibility table](https://localai.io/model-compatibility/index.html#model-compatibility-table).
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In a nutshell:
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- Local, OpenAI drop-in alternative REST API. You own your data.
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- NO GPU required. NO Internet access is required either. Optional, GPU Acceleration is available in `llama.cpp`-compatible LLMs. [See building instructions](https://localai.io/basics/build/index.html).
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- Supports multiple models, Audio transcription, Text generation with GPTs, Image generation with stable diffusion (experimental)
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- Once loaded the first time, it keep models loaded in memory for faster inference
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- Doesn't shell-out, but uses C++ bindings for a faster inference and better performance.
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LocalAI was created by [Ettore Di Giacinto](https://github.com/mudler/) and is a community-driven project, focused on making the AI accessible to anyone. Any contribution, feedback and PR is welcome!
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| [ChatGPT OSS alternative](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) | [Image generation](https://localai.io/api-endpoints/index.html#image-generation) |
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|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|
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See the [Getting started](https://localai.io/basics/getting_started/index.html) and [examples](https://github.com/go-skynet/LocalAI/tree/master/examples/) sections to learn how to use LocalAI. For a list of curated models check out the [model gallery](https://github.com/go-skynet/model-gallery).
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## News
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- 29-05-2023: LocalAI now has a website, [https://localai.io](https://localai.io)! check the news in the [dedicated section](https://localai.io/basics/news/index.html)!
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For latest news, follow also on Twitter [@LocalAI_API](https://twitter.com/LocalAI_API) and [@mudler_it](https://twitter.com/mudler_it)
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## Contribute and help
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To help the project you can:
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- Upvote the [Reddit post](https://www.reddit.com/r/selfhosted/comments/12w4p2f/localai_openai_compatible_api_to_run_llm_models/) about LocalAI.
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- [Hacker news post](https://news.ycombinator.com/item?id=35726934) - help us out by voting if you like this project.
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- If you have technological skills and want to contribute to development, have a look at the open issues. If you are new you can have a look at the [good-first-issue](https://github.com/go-skynet/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) and [help-wanted](https://github.com/go-skynet/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) labels.
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- If you don't have technological skills you can still help improving documentation or add examples or share your user-stories with our community, any help and contribution is welcome!
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## Usage
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Check out the [Getting started](https://localai.io/basics/getting_started/index.html) section. Here below you will find generic, quick instructions to get ready and use LocalAI.
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The easiest way to run LocalAI is by using `docker-compose` (to build locally, see [building LocalAI](https://localai.io/basics/build/index.html)):
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```bash
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git clone https://github.com/go-skynet/LocalAI
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cd LocalAI
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# (optional) Checkout a specific LocalAI tag
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# git checkout -b build <TAG>
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# copy your models to models/
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cp your-model.bin models/
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# (optional) Edit the .env file to set things like context size and threads
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# vim .env
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# start with docker-compose
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docker-compose up -d --pull always
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# or you can build the images with:
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# docker-compose up -d --build
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# Now API is accessible at localhost:8080
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curl http://localhost:8080/v1/models
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# {"object":"list","data":[{"id":"your-model.bin","object":"model"}]}
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curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
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"model": "your-model.bin",
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"prompt": "A long time ago in a galaxy far, far away",
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"temperature": 0.7
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}'
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```
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### Example: Use GPT4ALL-J model
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<details>
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```bash
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# Clone LocalAI
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git clone https://github.com/go-skynet/LocalAI
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cd LocalAI
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# (optional) Checkout a specific LocalAI tag
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# git checkout -b build <TAG>
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# Download gpt4all-j to models/
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wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j
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# Use a template from the examples
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cp -rf prompt-templates/ggml-gpt4all-j.tmpl models/
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# (optional) Edit the .env file to set things like context size and threads
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# vim .env
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# start with docker-compose
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docker-compose up -d --pull always
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# or you can build the images with:
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# docker-compose up -d --build
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# Now API is accessible at localhost:8080
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curl http://localhost:8080/v1/models
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# {"object":"list","data":[{"id":"ggml-gpt4all-j","object":"model"}]}
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curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
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"model": "ggml-gpt4all-j",
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"messages": [{"role": "user", "content": "How are you?"}],
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"temperature": 0.9
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}'
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# {"model":"ggml-gpt4all-j","choices":[{"message":{"role":"assistant","content":"I'm doing well, thanks. How about you?"}}]}
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```
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</details>
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### Build locally
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<details>
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In order to build the `LocalAI` container image locally you can use `docker`:
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```
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# build the image
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docker build -t localai .
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docker run localai
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```
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Or you can build the binary with `make`:
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```
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make build
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```
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</details>
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See the [build section](https://localai.io/basics/build/index.html) in our documentation for detailed instructions.
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### Run LocalAI in Kubernetes
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LocalAI can be installed inside Kubernetes with helm. See [installation instructions](https://localai.io/basics/getting_started/index.html#run-localai-in-kubernetes).
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## Supported API endpoints
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See the [list of the supported API endpoints](https://localai.io/api-endpoints/index.html) and how to configure image generation and audio transcription.
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## Frequently asked questions
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See [the FAQ](https://localai.io/faq/index.html) section for a list of common questions.
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## Projects already using LocalAI to run local models
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Feel free to open up a PR to get your project listed!
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- [Kairos](https://github.com/kairos-io/kairos)
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- [k8sgpt](https://github.com/k8sgpt-ai/k8sgpt#running-local-models)
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- [Spark](https://github.com/cedriking/spark)
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- [autogpt4all](https://github.com/aorumbayev/autogpt4all)
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- [Mods](https://github.com/charmbracelet/mods)
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## Short-term roadmap
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- [x] Mimic OpenAI API (https://github.com/go-skynet/LocalAI/issues/10)
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- [ ] Binary releases (https://github.com/go-skynet/LocalAI/issues/6)
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- [ ] Upstream our golang bindings to llama.cpp (https://github.com/ggerganov/llama.cpp/issues/351) and [gpt4all](https://github.com/go-skynet/LocalAI/issues/85)
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- [x] Multi-model support
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- [x] Have a webUI!
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- [x] Allow configuration of defaults for models.
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- [x] Support for embeddings
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- [x] Support for audio transcription with https://github.com/ggerganov/whisper.cpp
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- [ ] GPU/CUDA support ( https://github.com/go-skynet/LocalAI/issues/69 )
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- [ ] Enable automatic downloading of models from a curated gallery, with only free-licensed models, directly from the webui.
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## Star history
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[](https://star-history.com/#go-skynet/LocalAI&Date)
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## License
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LocalAI is a community-driven project created by [Ettore Di Giacinto](https://github.com/mudler/).
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MIT
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## Author
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Ettore Di Giacinto and others
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## Acknowledgements
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LocalAI couldn't have been built without the help of great software already available from the community. Thank you!
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- [llama.cpp](https://github.com/ggerganov/llama.cpp)
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- https://github.com/tatsu-lab/stanford_alpaca
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- https://github.com/cornelk/llama-go for the initial ideas
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- https://github.com/antimatter15/alpaca.cpp
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- https://github.com/EdVince/Stable-Diffusion-NCNN
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- https://github.com/ggerganov/whisper.cpp
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- https://github.com/saharNooby/rwkv.cpp
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## Contributors
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<a href="https://github.com/go-skynet/LocalAI/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=go-skynet/LocalAI" />
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</a>
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