Quickstart
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 and architectures.
LocalAI is available as a container image and binary, compatible with various container engines like Docker, Podman, and Kubernetes. Container images are published on quay.io and Docker Hub. Binaries can be downloaded from GitHub.
Prerequisites
Before you begin, ensure you have a container engine installed if you are not using the binaries. Suitable options include Docker or Podman. For installation instructions, refer to the following guides:
Hardware Requirements: The hardware requirements for LocalAI vary based on the model size and quantization method used. For performance benchmarks with different backends, such as llama.cpp, visit this link. The rwkv backend is noted for its lower resource consumption.
Running LocalAI with All-in-One (AIO) Images
Do you have already a model file? Skip to Run models manually or Run other models to use an already-configured model.
LocalAI’s All-in-One (AIO) images are pre-configured with a set of models and backends to fully leverage almost all the LocalAI featureset. If you don’t need models pre-configured, you can use the standard images.
These images are available for both CPU and GPU environments. The AIO images are designed to be easy to use and requires no configuration.
It suggested to use the AIO images if you don’t want to configure the models to run on LocalAI. If you want to run specific models, you can use the manual method.
The AIO Images comes pre-configured with the following features:
- Text to Speech (TTS)
- Speech to Text
- Function calling
- Large Language Models (LLM) for text generation
- Image generation
- Embedding server
Start the image with Docker:
docker run -p 8080:8080 --name local-ai -ti localai/localai:latest-aio-cpu
# For Nvidia GPUs:
# docker run -p 8080:8080 --gpus all --name local-ai -ti localai/localai:latest-aio-gpu-nvidia-cuda-11
# docker run -p 8080:8080 --gpus all --name local-ai -ti localai/localai:latest-aio-gpu-nvidia-cuda-12
Or with a docker-compose file:
version: "3.9"
services:
api:
image: localai/localai:latest-aio-cpu
# For a specific version:
# image: localai/localai:v2.14.0-aio-cpu
# For Nvidia GPUs decomment one of the following (cuda11 or cuda12):
# image: localai/localai:v2.14.0-aio-gpu-nvidia-cuda-11
# image: localai/localai:v2.14.0-aio-gpu-nvidia-cuda-12
# image: localai/localai:latest-aio-gpu-nvidia-cuda-11
# image: localai/localai:latest-aio-gpu-nvidia-cuda-12
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8080/readyz"]
interval: 1m
timeout: 20m
retries: 5
ports:
- 8080:8080
environment:
- DEBUG=true
# ...
volumes:
- ./models:/build/models:cached
# decomment the following piece if running with Nvidia GPUs
# deploy:
# resources:
# reservations:
# devices:
# - driver: nvidia
# count: 1
# capabilities: [gpu]
For a list of all the container-images available, see Container images. To learn more about All-in-one images instead, see All-in-one Images.
Models caching: The AIO image will download the needed models on the first run if not already present and store those in /build/models inside the container. The AIO models will be automatically updated with new versions of AIO images.
You can change the directory inside the container by specifying a MODELS_PATH environment variable (or --models-path).
If you want to use a named model or a local directory, you can mount it as a volume to /build/models:
docker run -p 8080:8080 --name local-ai -ti -v $PWD/models:/build/models localai/localai:latest-aio-cpu
or associate a volume:
docker volume create localai-models
docker run -p 8080:8080 --name local-ai -ti -v localai-models:/build/models localai/localai:latest-aio-cpu
From binary
LocalAI is available as a standalone binary as well. Binaries are compiled for Linux and MacOS and automatically uploaded in the Github releases. Windows is known to work with WSL.
You can check out the releases in https://github.com/mudler/LocalAI/releases.
Try it out
Connect to LocalAI, by default the WebUI should be accessible from http://localhost:8080 . You can also use 3rd party projects to interact with LocalAI as you would use OpenAI (see also Integrations ).
You can also test out the API endpoints using curl, examples below.
Text Generation
Creates a model response for the given chat conversation. OpenAI documentation.
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{ "model": "gpt-4", "messages": [{"role": "user", "content": "How are you doing?", "temperature": 0.1}] }'
GPT Vision
Understand images.
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "user", "content": [
{"type":"text", "text": "What is in the image?"},
{
"type": "image_url",
"image_url": {
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
}
}
],
"temperature": 0.9
}
]
}'
Function calling
Call functions
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4",
"messages": [
{
"role": "user",
"content": "What is the weather like in Boston?"
}
],
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"]
}
},
"required": ["location"]
}
}
}
],
"tool_choice": "auto"
}'
Image Generation
Creates an image given a prompt. OpenAI documentation.
curl http://localhost:8080/v1/images/generations \
-H "Content-Type: application/json" -d '{
"prompt": "A cute baby sea otter",
"size": "256x256"
}'
Text to speech
Generates audio from the input text. OpenAI documentation.
curl http://localhost:8080/v1/audio/speech \
-H "Content-Type: application/json" \
-d '{
"model": "tts-1",
"input": "The quick brown fox jumped over the lazy dog.",
"voice": "alloy"
}' \
--output speech.mp3
Audio Transcription
Transcribes audio into the input language. OpenAI Documentation.
Download first a sample to transcribe:
wget --quiet --show-progress -O gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg
Send the example audio file to the transcriptions endpoint :
curl http://localhost:8080/v1/audio/transcriptions \
-H "Content-Type: multipart/form-data" \
-F file="@$PWD/gb1.ogg" -F model="whisper-1"
Embeddings Generation
Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms. OpenAI Embeddings.
curl http://localhost:8080/embeddings \
-X POST -H "Content-Type: application/json" \
-d '{
"input": "Your text string goes here",
"model": "text-embedding-ada-002"
}'
Don’t use the model file as model in the request unless you want to handle the prompt template for yourself.
Use the model names like you would do with OpenAI like in the examples below. For instance gpt-4-vision-preview, or gpt-4.
What’s next?
There is much more to explore! run any model from huggingface, video generation, and voice cloning with LocalAI, check out the features section for a full overview.
Explore further resources and community contributions:
Last updated 28 Apr 2024, 23:41 +0200 .