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* docs: improve getting started Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> * cleanups * Use dockerhub links * Shrink command to minimum --------- Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
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💡 Get help - ❓FAQ ❓How tos 💭Discussions 💭Discord
LocalAI is the free, Open Source OpenAI alternative. LocalAI act as a drop-in replacement REST API that's compatible with OpenAI API specifications for local inferencing. It allows you to run LLMs, generate images, audio (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. It is maintained by mudler.
In a nutshell:
- Local, OpenAI drop-in alternative REST API. You own your data.
- NO GPU required. NO Internet access is required either
- Optional, GPU Acceleration is available. See also the build section.
- Supports multiple models
- 🏃 Once loaded the first time, it keep models loaded in memory for faster inference
- ⚡ Doesn't shell-out, but uses bindings for a faster inference and better performance.
LocalAI is focused on making the AI accessible to anyone. Any contribution, feedback and PR is welcome!
Note that this started just as a fun weekend project by mudler in order to try to create the necessary pieces for a full AI assistant like ChatGPT: the community is growing fast and we are working hard to make it better and more stable. If you want to help, please consider contributing (see below)!
🚀 Features
- 📖 Text generation with GPTs (
llama.cpp,gpt4all.cpp, ... 📖 and more) - 🗣 Text to Audio
- 🔈 Audio to Text (Audio transcription with
whisper.cpp) - 🎨 Image generation with stable diffusion
- 🔥 OpenAI functions 🆕
- 🧠 Embeddings generation for vector databases
- ✍️ Constrained grammars
- 🖼️ Download Models directly from Huggingface
- 🆕 Vision API
How does it work?
LocalAI is an API written in Go that serves as an OpenAI shim, enabling software already developed with OpenAI SDKs to seamlessly integrate with LocalAI. It can be effortlessly implemented as a substitute, even on consumer-grade hardware. This capability is achieved by employing various C++ backends, including ggml, to perform inference on LLMs using both CPU and, if desired, GPU. Internally LocalAI backends are just gRPC server, indeed you can specify and build your own gRPC server and extend LocalAI in runtime as well. It is possible to specify external gRPC server and/or binaries that LocalAI will manage internally.
LocalAI uses a mixture of backends written in various languages (C++, Golang, Python, ...). You can check [the model compatibility table]({{%relref "model-compatibility" %}}) to learn about all the components of LocalAI.
Contribute and help
To help the project you can:
-
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 and help-wanted labels.
-
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!
🌟 Star history
📖 License
LocalAI is a community-driven project created by Ettore Di Giacinto.
MIT - Author Ettore Di Giacinto
🙇 Acknowledgements
LocalAI couldn't have been built without the help of great software already available from the community. Thank you!
- llama.cpp
- https://github.com/tatsu-lab/stanford_alpaca
- https://github.com/cornelk/llama-go for the initial ideas
- https://github.com/antimatter15/alpaca.cpp
- https://github.com/EdVince/Stable-Diffusion-NCNN
- https://github.com/ggerganov/whisper.cpp
- https://github.com/saharNooby/rwkv.cpp
- https://github.com/rhasspy/piper
- https://github.com/cmp-nct/ggllm.cpp
Backstory
As much as typical open source projects starts, I, mudler, was fiddling around with llama.cpp over my long nights and wanted to have a way to call it from go, as I am a Golang developer and use it extensively. So I've created LocalAI (or what was initially known as llama-cli) and added an API to it.
But guess what? The more I dived into this rabbit hole, the more I realized that I had stumbled upon something big. With all the fantastic C++ projects floating around the community, it dawned on me that I could piece them together to create a full-fledged OpenAI replacement. So, ta-da! LocalAI was born, and it quickly overshadowed its humble origins.
Now, why did I choose to go with C++ bindings, you ask? Well, I wanted to keep LocalAI snappy and lightweight, allowing it to run like a champ on any system and avoid any Golang penalties of the GC, and, most importantly built on shoulders of giants like llama.cpp. Go is good at backends and API and is easy to maintain. And hey, don't forget that I'm all about sharing the love. That's why I made LocalAI MIT licensed, so everyone can hop on board and benefit from it.
As if that wasn't exciting enough, as the project gained traction, mkellerman and Aisuko jumped in to lend a hand. mkellerman helped set up some killer examples, while Aisuko is becoming our community maestro. The community now is growing even more with new contributors and users, and I couldn't be happier about it!
Oh, and let's not forget the real MVP here—llama.cpp. Without this extraordinary piece of software, LocalAI wouldn't even exist. So, a big shoutout to the community for making this magic happen!
🤗 Contributors
This is a community project, a special thanks to our contributors! 🤗