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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, pytorch and more. Does not require GPU.
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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 in
llama.cpp-compatible LLMs. See also the build section.
- Optional, GPU Acceleration is available in
- Supports multiple models
- 🏃 Once loaded the first time, it keep models loaded in memory for faster inference
- ⚡ Doesn't shell-out, but uses C++ bindings for a faster inference and better performance.
LocalAI was created by Ettore Di Giacinto and is a community-driven project, 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 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
- Create a slackbot for teams and OSS projects that answer to documentation
- LocalAI meets k8sgpt
- Question Answering on Documents locally with LangChain, LocalAI, Chroma, and GPT4All
- Tutorial to use k8sgpt with LocalAI
Check out the Getting started section in our documentation.
See the documentation
- How to build locally
- How to install in Kubernetes
- Projects integrating LocalAI
- How tos section (curated by our community)
If you utilize this repository, data in a downstream project, please consider citing it with:
@misc{localai,
author = {Ettore Di Giacinto},
title = {LocalAI: The free, Open source OpenAI alternative},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/go-skynet/LocalAI}},
Do you find LocalAI useful?
Support the project by becoming a backer or sponsor. Your logo will show up here with a link to your website.
A huge thank you to our generous sponsors who support this project:
 |
|---|
| Spectro Cloud |
| Spectro Cloud kindly supports LocalAI by providing GPU and computing resources to run tests on lamdalabs! |
And a huge shout-out to individuals sponsoring the project by donating hardware or backing the project.
- Sponsor list
- JDAM00 (donating HW for the CI)
LocalAI is a community-driven project created by Ettore Di Giacinto.
MIT - Author Ettore Di Giacinto
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
This is a community project, a special thanks to our contributors! 🤗