Docker Installation

LocalAI provides Docker images that work with Docker, Podman, and other container engines. These images are available on Docker Hub and Quay.io.

Prerequisites

Before you begin, ensure you have Docker or Podman installed:

• Install Docker Desktop (Mac, Windows, Linux)
• Install Podman (Linux alternative)
• Install Docker Engine (Linux servers)

Quick Start

The fastest way to get started is with the CPU image:

docker run -p 8080:8080 --name local-ai -ti localai/localai:latest

This will:

• Start LocalAI (you’ll need to install models separately)
• Make the API available at http://localhost:8080

Image Types

LocalAI provides several image types to suit different needs:

Standard Images

Standard images don’t include pre-configured models. Use these if you want to configure models manually.

CPU Image

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest

GPU Images

NVIDIA CUDA 12:

docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12

NVIDIA CUDA 11:

docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-11

AMD GPU (ROCm):

docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-gpu-hipblas

Intel GPU:

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-intel

Vulkan:

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan

NVIDIA Jetson (L4T ARM64):

docker run -ti --name local-ai -p 8080:8080 --runtime nvidia --gpus all localai/localai:latest-nvidia-l4t-arm64

All-in-One (AIO) Images

Recommended for beginners - These images come pre-configured with models and backends, ready to use immediately.

CPU Image

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-aio-cpu

GPU Images

NVIDIA CUDA 12:

docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-12

NVIDIA CUDA 11:

docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-11

AMD GPU (ROCm):

docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-aio-gpu-hipblas

Intel GPU:

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-aio-gpu-intel

Using Docker Compose

For a more manageable setup, especially with persistent volumes, use Docker Compose:

version: "3.9"
services:
  api:
    image: localai/localai:latest-aio-cpu
    # For GPU support, use one of:
    # image: localai/localai:latest-aio-gpu-nvidia-cuda-12
    # image: localai/localai:latest-aio-gpu-nvidia-cuda-11
    # image: localai/localai:latest-aio-gpu-hipblas
    # image: localai/localai:latest-aio-gpu-intel
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8080/readyz"]
      interval: 1m
      timeout: 20m
      retries: 5
    ports:
      - 8080:8080
    environment:
      - DEBUG=true
    volumes:
      - ./models:/models:cached
    # For NVIDIA GPUs, uncomment:
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - driver: nvidia
    #           count: 1
    #           capabilities: [gpu]

Save this as docker-compose.yml and run:

docker compose up -d

Persistent Storage

To persist models and configurations, mount a volume:

docker run -ti --name local-ai -p 8080:8080 \
  -v $PWD/models:/models \
  localai/localai:latest-aio-cpu

Or use a named volume:

docker volume create localai-models
docker run -ti --name local-ai -p 8080:8080 \
  -v localai-models:/models \
  localai/localai:latest-aio-cpu

What’s Included in AIO Images

All-in-One images come pre-configured with:

• Text Generation: LLM models for chat and completion
• Image Generation: Stable Diffusion models
• Text to Speech: TTS models
• Speech to Text: Whisper models
• Embeddings: Vector embedding models
• Function Calling: Support for OpenAI-compatible function calling

The AIO images use OpenAI-compatible model names (like gpt-4, gpt-4-vision-preview) but are backed by open-source models. See the container images documentation for the complete mapping.

Next Steps

After installation:

1. Access the WebUI at http://localhost:8080
2. Check available models: curl http://localhost:8080/v1/models
3. Install additional models
4. Try out examples

Advanced Configuration

For detailed information about:

• All available image tags and versions
• Advanced Docker configuration options
• Custom image builds
• Backend management

See the Container Images documentation.

Troubleshooting

Container won’t start

• Check Docker is running: docker ps
• Check port 8080 is available: netstat -an | grep 8080 (Linux/Mac)
• View logs: docker logs local-ai

GPU not detected

• Ensure Docker has GPU access: docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi
• For NVIDIA: Install NVIDIA Container Toolkit
• For AMD: Ensure devices are accessible: ls -la /dev/kfd /dev/dri

Models not downloading

• Check internet connection
• Verify disk space: df -h
• Check Docker logs for errors: docker logs local-ai

See Also

• Container Images Reference - Complete image reference
• Install Models - Install and configure models
• GPU Acceleration - GPU setup and optimization
• Kubernetes Installation - Deploy on Kubernetes

macOS Installation

The easiest way to install LocalAI on macOS is using the DMG application.

Download

Download the latest DMG from GitHub releases:

Installation Steps

1. Download the LocalAI.dmg file from the link above
2. Open the downloaded DMG file
3. Drag the LocalAI application to your Applications folder
4. Launch LocalAI from your Applications folder

Known Issues

Note: The DMGs are not signed by Apple and may show as quarantined.

Workaround: See this issue for details on how to bypass the quarantine.

Fix tracking: The signing issue is being tracked in this issue.

Next Steps

After installing LocalAI, you can:

• Access the WebUI at http://localhost:8080
• Try it out with examples
• Learn about available models
• Customize your configuration

Linux Installation

One-Line Installer (Recommended)

The fastest way to install LocalAI on Linux is with the installation script:

curl https://localai.io/install.sh | sh

This script will:

• Detect your system architecture
• Download the appropriate LocalAI binary
• Set up the necessary configuration
• Start LocalAI automatically

Installer Configuration Options

The installer can be configured using environment variables:

curl https://localai.io/install.sh | VAR=value sh

Environment Variables

Image Selection

The installer will automatically detect your GPU and select the appropriate image. By default, it uses the standard images without extra Python dependencies. You can customize the image selection:

• USE_AIO=true: Use all-in-one images that include all dependencies
• USE_VULKAN=true: Use Vulkan GPU support instead of vendor-specific GPU support

Uninstallation

To uninstall LocalAI installed via the script:

curl https://localai.io/install.sh | sh -s -- --uninstall

Manual Installation

Download Binary

You can manually download the appropriate binary for your system from the releases page:

1. Go to GitHub Releases
2. Download the binary for your architecture (amd64, arm64, etc.)
3. Make it executable:

chmod +x local-ai-*

4. Run LocalAI:

./local-ai-*

System Requirements

Hardware requirements vary based on:

• Model size
• Quantization method
• Backend used

For performance benchmarks with different backends like llama.cpp, visit this link.

Configuration

After installation, you can:

• Access the WebUI at http://localhost:8080
• Configure models in the models directory
• Customize settings via environment variables or config files

Next Steps

• Try it out with examples
• Learn about available models
• Configure GPU acceleration
• Customize your configuration

Run with Kubernetes

For installing LocalAI in Kubernetes, the deployment file from the examples can be used and customized as preferred:

kubectl apply -f https://raw.githubusercontent.com/mudler/LocalAI-examples/refs/heads/main/kubernetes/deployment.yaml

For Nvidia GPUs:

kubectl apply -f https://raw.githubusercontent.com/mudler/LocalAI-examples/refs/heads/main/kubernetes/deployment-nvidia.yaml

Alternatively, the helm chart can be used as well:

helm repo add go-skynet https://go-skynet.github.io/helm-charts/
helm repo update
helm show values go-skynet/local-ai > values.yaml


helm install local-ai go-skynet/local-ai -f values.yaml

Build LocalAI

Build

LocalAI can be built as a container image or as a single, portable binary. Note that some model architectures might require Python libraries, which are not included in the binary.

LocalAI’s extensible architecture allows you to add your own backends, which can be written in any language, and as such the container images contains also the Python dependencies to run all the available backends (for example, in order to run backends like Diffusers that allows to generate images and videos from text).

This section contains instructions on how to build LocalAI from source.

Build LocalAI locally

Requirements

In order to build LocalAI locally, you need the following requirements:

• Golang >= 1.21
• GCC
• GRPC

To install the dependencies follow the instructions below:

brew install go protobuf protoc-gen-go protoc-gen-go-grpc wget

apt install golang make protobuf-compiler-grpc

go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af

make build

Build

To build LocalAI with make:

git clone https://github.com/go-skynet/LocalAI
cd LocalAI
make build

This should produce the binary local-ai

Container image

Requirements:

• Docker or podman, or a container engine

In order to build the LocalAI container image locally you can use docker, for example:

docker build -t localai .
docker run localai

Example: Build on mac

Building on Mac (M1, M2 or M3) works, but you may need to install some prerequisites using brew.

The below has been tested by one mac user and found to work. Note that this doesn’t use Docker to run the server:

Install xcode from the Apps Store (needed for metalkit)

brew install abseil cmake go grpc protobuf wget protoc-gen-go protoc-gen-go-grpc

git clone https://github.com/go-skynet/LocalAI.git

cd LocalAI

make build

wget https://huggingface.co/TheBloke/phi-2-GGUF/resolve/main/phi-2.Q2_K.gguf -O models/phi-2.Q2_K

cp -rf prompt-templates/ggml-gpt4all-j.tmpl models/phi-2.Q2_K.tmpl

./local-ai backends install llama-cpp

./local-ai --models-path=./models/ --debug=true

curl http://localhost:8080/v1/models

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
     "model": "phi-2.Q2_K",
     "messages": [{"role": "user", "content": "How are you?"}],
     "temperature": 0.9 
   }'

Troubleshooting mac

• If you encounter errors regarding a missing utility metal, install Xcode from the App Store.

• After the installation of Xcode, if you receive a xcrun error 'xcrun: error: unable to find utility "metal", not a developer tool or in PATH'. You might have installed the Xcode command line tools before installing Xcode, the former one is pointing to an incomplete SDK.

xcode-select --print-path

sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer

• If completions are slow, ensure that gpu-layers in your model yaml matches the number of layers from the model in use (or simply use a high number such as 256).

• If you get a compile error: error: only virtual member functions can be marked 'final', reinstall all the necessary brew packages, clean the build, and try again.

brew reinstall go grpc protobuf wget

make clean

make build

Build backends

LocalAI have several backends available for installation in the backend gallery. The backends can be also built by source. As backends might vary from language and dependencies that they require, the documentation will provide generic guidance for few of the backends, which can be applied with some slight modifications also to the others.

Manually

Typically each backend include a Makefile which allow to package the backend.

In the LocalAI repository, for instance you can build bark-cpp by doing:

git clone https://github.com/go-skynet/LocalAI.git

make -C LocalAI/backend/go/bark-cpp build package

make -C LocalAI/backend/python/vllm

With Docker

Building with docker is simpler as abstracts away all the requirement, and focuses on building the final OCI images that are available in the gallery. This allows for instance also to build locally a backend and install it with LocalAI. You can refer to Backends for general guidance on how to install and develop backends.

In the LocalAI repository, you can build bark-cpp by doing:

git clone https://github.com/go-skynet/LocalAI.git

make docker-build-bark-cpp

Note that make is only by convenience, in reality it just runs a simple docker command as:

docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:bark-cpp -f LocalAI/backend/Dockerfile.golang --build-arg BACKEND=bark-cpp .               

Note:

• BUILD_TYPE can be either: cublas, hipblas, sycl_f16, sycl_f32, metal.
• BASE_IMAGE is tested on ubuntu:22.04 (and defaults to it) and quay.io/go-skynet/intel-oneapi-base:latest for intel/sycl

Quickstart

LocalAI is a free, open-source alternative to OpenAI (Anthropic, etc.), functioning as a drop-in replacement REST API for local inferencing. It allows you to run LLMs, generate images, and produce audio, all locally or on-premises with consumer-grade hardware, supporting multiple model families and architectures.

Quickstart

This guide assumes you have already installed LocalAI. If you haven’t installed it yet, see the Installation guide first.

Starting LocalAI

Once installed, start LocalAI. For Docker installations:

docker run -p 8080:8080 --name local-ai -ti localai/localai:latest

The API will be available at http://localhost:8080.

Downloading models on start

When starting LocalAI (either via Docker or via CLI) you can specify as argument a list of models to install automatically before starting the API, for example:

local-ai run llama-3.2-1b-instruct:q4_k_m
local-ai run huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf
local-ai run ollama://gemma:2b
local-ai run https://gist.githubusercontent.com/.../phi-2.yaml
local-ai run oci://localai/phi-2:latest

For a full list of options, you can run LocalAI with --help or refer to the Linux Installation guide for installer configuration options.

Using LocalAI and the full stack with LocalAGI

LocalAI is part of the Local family stack, along with LocalAGI and LocalRecall.

LocalAGI is a powerful, self-hostable AI Agent platform designed for maximum privacy and flexibility which encompassess and uses all the software stack. It provides a complete drop-in replacement for OpenAI’s Responses APIs with advanced agentic capabilities, working entirely locally on consumer-grade hardware (CPU and GPU).

Quick Start

git clone https://github.com/mudler/LocalAGI
cd LocalAGI

docker compose up

docker compose -f docker-compose.nvidia.yaml up

docker compose -f docker-compose.intel.yaml up

MODEL_NAME=gemma-3-12b-it docker compose up

MODEL_NAME=gemma-3-12b-it \
MULTIMODAL_MODEL=minicpm-v-4_5 \
IMAGE_MODEL=flux.1-dev-ggml \
docker compose -f docker-compose.nvidia.yaml up

Key Features

• Privacy-Focused: All processing happens locally, ensuring your data never leaves your machine
• Flexible Deployment: Supports CPU, NVIDIA GPU, and Intel GPU configurations
• Multiple Model Support: Compatible with various models from Hugging Face and other sources
• Web Interface: User-friendly chat interface for interacting with AI agents
• Advanced Capabilities: Supports multimodal models, image generation, and more
• Docker Integration: Easy deployment using Docker Compose

Environment Variables

You can customize your LocalAGI setup using the following environment variables:

• MODEL_NAME: Specify the model to use (e.g., gemma-3-12b-it)
• MULTIMODAL_MODEL: Set a custom multimodal model
• IMAGE_MODEL: Configure an image generation model

For more advanced configuration and API documentation, visit the LocalAGI GitHub repository.

What’s Next?

There is much more to explore with LocalAI! You can run any model from Hugging Face, perform video generation, and also voice cloning. For a comprehensive overview, check out the features section.

Explore additional resources and community contributions:

• Linux Installation Options
• Run from Container images
• Examples to try from the CLI
• Build LocalAI from source
• Run models manually
• Examples

Setting Up Models

This section covers everything you need to know about installing and configuring models in LocalAI. You’ll learn multiple methods to get models running.

Prerequisites

• LocalAI installed and running (see Quickstart if you haven’t set it up yet)
• Basic understanding of command line usage

Method 1: Using the Model Gallery (Easiest)

The Model Gallery is the simplest way to install models. It provides pre-configured models ready to use.

Via WebUI

1. Open the LocalAI WebUI at http://localhost:8080
2. Navigate to the “Models” tab
3. Browse available models
4. Click “Install” on any model you want
5. Wait for installation to complete

For more details, refer to the Gallery Documentation.

Via CLI

# List available models
local-ai models list

# Install a specific model
local-ai models install llama-3.2-1b-instruct:q4_k_m

# Start LocalAI with a model from the gallery
local-ai run llama-3.2-1b-instruct:q4_k_m

To run models available in the LocalAI gallery, you can use the model name as the URI. For example, to run LocalAI with the Hermes model, execute:

local-ai run hermes-2-theta-llama-3-8b

To install only the model, use:

local-ai models install hermes-2-theta-llama-3-8b

Note: The galleries available in LocalAI can be customized to point to a different URL or a local directory. For more information on how to setup your own gallery, see the Gallery Documentation.

Browse Online

Visit models.localai.io to browse all available models in your browser.

Method 1.5: Import Models via WebUI

The WebUI provides a powerful model import interface that supports both simple and advanced configuration:

Simple Import Mode

1. Open the LocalAI WebUI at http://localhost:8080
2. Click “Import Model”
3. Enter the model URI (e.g., https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF)
4. Optionally configure preferences:
   • Backend selection
   • Model name
   • Description
   • Quantizations
   • Embeddings support
   • Custom preferences
5. Click “Import Model” to start the import process

Advanced Import Mode

For full control over model configuration:

1. In the WebUI, click “Import Model”
2. Toggle to “Advanced Mode”
3. Edit the YAML configuration directly in the code editor
4. Use the “Validate” button to check your configuration
5. Click “Create” or “Update” to save

The advanced editor includes:

• Syntax highlighting
• YAML validation
• Format and copy tools
• Full configuration options

This is especially useful for:

• Custom model configurations
• Fine-tuning model parameters
• Setting up complex model setups
• Editing existing model configurations

Method 2: Installing from Hugging Face

LocalAI can directly install models from Hugging Face:

# Install and run a model from Hugging Face
local-ai run huggingface://TheBloke/phi-2-GGUF

The format is: huggingface://<repository>/<model-file> ( is optional)

Examples

local-ai run huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf

Method 3: Installing from OCI Registries

Ollama Registry

local-ai run ollama://gemma:2b

Standard OCI Registry

local-ai run oci://localai/phi-2:latest

Run Models via URI

To run models via URI, specify a URI to a model file or a configuration file when starting LocalAI. Valid syntax includes:

• file://path/to/model
• huggingface://repository_id/model_file (e.g., huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf)
• From OCIs: oci://container_image:tag, ollama://model_id:tag
• From configuration files: https://gist.githubusercontent.com/.../phi-2.yaml

Configuration files can be used to customize the model defaults and settings. For advanced configurations, refer to the Customize Models section.

Examples

local-ai run huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf
local-ai run ollama://gemma:2b
local-ai run https://gist.githubusercontent.com/.../phi-2.yaml
local-ai run oci://localai/phi-2:latest

Method 4: Manual Installation

For full control, you can manually download and configure models.

Step 1: Download a Model

Download a GGUF model file. Popular sources:

• Hugging Face

Example:

mkdir -p models

wget https://huggingface.co/TheBloke/phi-2-GGUF/resolve/main/phi-2.Q4_K_M.gguf \
  -O models/phi-2.Q4_K_M.gguf

Step 2: Create a Configuration File (Optional)

Create a YAML file to configure the model:

# models/phi-2.yaml
name: phi-2
parameters:
  model: phi-2.Q4_K_M.gguf
  temperature: 0.7
context_size: 2048
threads: 4
backend: llama-cpp

Customize model defaults and settings with a configuration file. For advanced configurations, refer to the Advanced Documentation.

Step 3: Run LocalAI

Choose one of the following methods to run LocalAI:

mkdir models

cp your-model.gguf models/

docker run -p 8080:8080 -v $PWD/models:/models -ti --rm quay.io/go-skynet/local-ai:latest --models-path /models --context-size 700 --threads 4

curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
     "model": "your-model.gguf",
     "prompt": "A long time ago in a galaxy far, far away",
     "temperature": 0.7
   }'

mkdir models

wget https://huggingface.co/TheBloke/Luna-AI-Llama2-Uncensored-GGUF/resolve/main/luna-ai-llama2-uncensored.Q4_0.gguf -O models/luna-ai-llama2

cp -rf prompt-templates/getting_started.tmpl models/luna-ai-llama2.tmpl

docker run -p 8080:8080 -v $PWD/models:/models -ti --rm quay.io/go-skynet/local-ai:latest --models-path /models --context-size 700 --threads 4

curl http://localhost:8080/v1/models

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
     "model": "luna-ai-llama2",
     "messages": [{"role": "user", "content": "How are you?"}],
     "temperature": 0.9
   }'

git clone https://github.com/go-skynet/LocalAI

cd LocalAI

cp your-model.gguf models/

docker compose up -d --pull always

curl http://localhost:8080/v1/models

curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
     "model": "your-model.gguf",
     "prompt": "A long time ago in a galaxy far, far away",
     "temperature": 0.7
   }'

# With binary
local-ai --models-path ./models

GPU Acceleration

For instructions on GPU acceleration, visit the GPU Acceleration page.

For more model configurations, visit the Examples Section.

Understanding Model Files

File Formats

• GGUF: Modern format, recommended for most use cases
• GGML: Older format, still supported but deprecated

Quantization Levels

Models come in different quantization levels (quality vs. size trade-off):

Choosing the Right Model

Consider:

• RAM available: Larger models need more RAM
• Use case: Different models excel at different tasks
• Speed: Smaller quantizations are faster
• Quality: Higher quantizations produce better output

Model Configuration

Basic Configuration

Create a YAML file in your models directory:

name: my-model
parameters:
  model: model.gguf
  temperature: 0.7
  top_p: 0.9
context_size: 2048
threads: 4
backend: llama-cpp

Advanced Configuration

See the Model Configuration guide for all available options.

Managing Models

List Installed Models

# Via API
curl http://localhost:8080/v1/models

# Via CLI
local-ai models list

Remove Models

Simply delete the model file and configuration from your models directory:

rm models/model-name.gguf
rm models/model-name.yaml  # if exists

Troubleshooting

Model Not Loading

1. Check backend: Ensure the required backend is installed

   local-ai backends list
   local-ai backends install llama-cpp  # if needed

2. Check logs: Enable debug mode

   DEBUG=true local-ai

3. Verify file: Ensure the model file is not corrupted

Out of Memory

• Use a smaller quantization (Q4_K_S or Q2_K)
• Reduce context_size in configuration
• Close other applications to free RAM

Wrong Backend

Check the Compatibility Table to ensure you’re using the correct backend for your model.

Best Practices

1. Start small: Begin with smaller models to test your setup
2. Use quantized models: Q4_K_M is a good balance for most use cases
3. Organize models: Keep your models directory organized
4. Backup configurations: Save your YAML configurations
5. Monitor resources: Watch RAM and disk usage

Try it out

Once LocalAI is installed, you can start it (either by using docker, or the cli, or the systemd service).

By default the LocalAI 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 ).

After installation, install new models by navigating the model gallery, or by using the local-ai CLI.

You can test out the API endpoints using curl, few examples are listed below. The models we are referring here (gpt-4, gpt-4-vision-preview, tts-1, whisper-1) are the default models that come with the AIO images - you can also use any other model you have installed.

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.

wget --quiet --show-progress -O gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg 

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"
      }'

Customizing the Model

To customize the prompt template or the default settings of the model, a configuration file is utilized. This file must adhere to the LocalAI YAML configuration standards. For comprehensive syntax details, refer to the advanced documentation. The configuration file can be located either remotely (such as in a Github Gist) or within the local filesystem or a remote URL.

LocalAI can be initiated using either its container image or binary, with a command that includes URLs of model config files or utilizes a shorthand format (like huggingface:// or github://), which is then expanded into complete URLs.

The configuration can also be set via an environment variable. For instance:

local-ai github://owner/repo/file.yaml@branch

MODELS="github://owner/repo/file.yaml@branch,github://owner/repo/file.yaml@branch" local-ai

Here’s an example to initiate the phi-2 model:

docker run -p 8080:8080 localai/localai:v3.7.0 https://gist.githubusercontent.com/mudler/ad601a0488b497b69ec549150d9edd18/raw/a8a8869ef1bb7e3830bf5c0bae29a0cce991ff8d/phi-2.yaml

You can also check all the embedded models configurations here.

Example: Customizing the Prompt Template

To modify the prompt template, create a Github gist or a Pastebin file, and copy the content from https://github.com/mudler/LocalAI/blob/master/examples/configurations/phi-2.yaml. Alter the fields as needed:

name: phi-2
context_size: 2048
f16: true
threads: 11
gpu_layers: 90
mmap: true
parameters:
  # Reference any HF model or a local file here
  model: huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf
  temperature: 0.2
  top_k: 40
  top_p: 0.95
template:
  
  chat: &template |
    Instruct: {{.Input}}
    Output:
  # Modify the prompt template here ^^^ as per your requirements
  completion: *template

Then, launch LocalAI using your gist’s URL:

## Important! Substitute with your gist's URL!
docker run -p 8080:8080 localai/localai:v3.7.0 https://gist.githubusercontent.com/xxxx/phi-2.yaml

Next Steps

• Visit the advanced section for more insights on prompt templates and configuration files.
• To learn about fine-tuning an LLM model, check out the fine-tuning section.

Build LocalAI from source

Building LocalAI from source is an installation method that allows you to compile LocalAI yourself, which is useful for custom configurations, development, or when you need specific build options.

For complete build instructions, see the Build from Source documentation in the Installation section.

Run with container images

LocalAI provides a variety of images to support different environments. These images are available on quay.io and Docker Hub.

All-in-One images comes with a pre-configured set of models and backends, standard images instead do not have any model pre-configured and installed.

For GPU Acceleration support for Nvidia video graphic cards, use the Nvidia/CUDA images, if you don’t have a GPU, use the CPU images. If you have AMD or Mac Silicon, see the build section.

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:

• Install Docker Desktop (Mac, Windows, Linux)
• Install Podman (Linux)
• Install Docker engine (Servers)

Standard container images

Standard container images do not have pre-installed models. Use these if you want to configure models manually.

All-in-one images

All-In-One images are images that come pre-configured with a set of models and backends to fully leverage almost all the LocalAI featureset. These images are available for both CPU and GPU environments. The AIO images are designed to be easy to use and require no configuration. Models configuration can be found here separated by size.

In the AIO images there are models configured with the names of OpenAI models, however, they are really backed by Open Source models. You can find the table below

Usage

Select the image (CPU or GPU) and start the container with Docker:

docker run -p 8080:8080 --name local-ai -ti localai/localai:latest-aio-cpu

LocalAI will automatically download all the required models, and the API will be available at localhost:8080.

Or with a docker-compose file:

version: "3.9"
services:
  api:
    image: localai/localai:latest-aio-cpu
    # For a specific version:
    # image: localai/localai:v3.7.0-aio-cpu
    # For Nvidia GPUs decomment one of the following (cuda11 or cuda12):
    # image: localai/localai:v3.7.0-aio-gpu-nvidia-cuda-11
    # image: localai/localai:v3.7.0-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:/models:cached
    # decomment the following piece if running with Nvidia GPUs
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - driver: nvidia
    #           count: 1
    #           capabilities: [gpu]

docker run -p 8080:8080 --name local-ai -ti -v $PWD/models:/models localai/localai:latest-aio-cpu

docker volume create localai-models
docker run -p 8080:8080 --name local-ai -ti -v localai-models:/models localai/localai:latest-aio-cpu

Available AIO images

Available environment variables

The AIO Images are inheriting the same environment variables as the base images and the environment of LocalAI (that you can inspect by calling --help). However, it supports additional environment variables available only from the container image

See Also

• GPU acceleration

Run with Kubernetes

For installing LocalAI in Kubernetes, the deployment file from the examples can be used and customized as preferred:

kubectl apply -f https://raw.githubusercontent.com/mudler/LocalAI-examples/refs/heads/main/kubernetes/deployment.yaml

For Nvidia GPUs:

kubectl apply -f https://raw.githubusercontent.com/mudler/LocalAI-examples/refs/heads/main/kubernetes/deployment-nvidia.yaml

Alternatively, the helm chart can be used as well:

helm repo add go-skynet https://go-skynet.github.io/helm-charts/
helm repo update
helm show values go-skynet/local-ai > values.yaml


helm install local-ai go-skynet/local-ai -f values.yaml

⚙️ Backends

LocalAI supports a variety of backends that can be used to run different types of AI models. There are core Backends which are included, and there are containerized applications that provide the runtime environment for specific model types, such as LLMs, diffusion models, or text-to-speech models.

Managing Backends in the UI

The LocalAI web interface provides an intuitive way to manage your backends:

1. Navigate to the “Backends” section in the navigation menu
2. Browse available backends from configured galleries
3. Use the search bar to find specific backends by name, description, or type
4. Filter backends by type using the quick filter buttons (LLM, Diffusion, TTS, Whisper)
5. Install or delete backends with a single click
6. Monitor installation progress in real-time

Each backend card displays:

• Backend name and description
• Type of models it supports
• Installation status
• Action buttons (Install/Delete)
• Additional information via the info button

Backend Galleries

Backend galleries are repositories that contain backend definitions. They work similarly to model galleries but are specifically for backends.

Adding a Backend Gallery

You can add backend galleries by specifying the Environment Variable LOCALAI_BACKEND_GALLERIES:

export LOCALAI_BACKEND_GALLERIES='[{"name":"my-gallery","url":"https://raw.githubusercontent.com/username/repo/main/backends"}]'

The URL needs to point to a valid yaml file, for example:

- name: "test-backend"
  uri: "quay.io/image/tests:localai-backend-test"
  alias: "foo-backend"

Where URI is the path to an OCI container image.

Backend Gallery Structure

A backend gallery is a collection of YAML files, each defining a backend. Here’s an example structure:

name: "llm-backend"
description: "A backend for running LLM models"
uri: "quay.io/username/llm-backend:latest"
alias: "llm"
tags:
  - "llm"
  - "text-generation"

Pre-installing Backends

You can pre-install backends when starting LocalAI using the LOCALAI_EXTERNAL_BACKENDS environment variable:

export LOCALAI_EXTERNAL_BACKENDS="llm-backend,diffusion-backend"
local-ai run

Creating a Backend

To create a new backend, you need to:

1. Create a container image that implements the LocalAI backend interface
2. Define a backend YAML file
3. Publish your backend to a container registry

Backend Container Requirements

Your backend container should:

1. Implement the LocalAI backend interface (gRPC or HTTP)
2. Handle model loading and inference
3. Support the required model types
4. Include necessary dependencies
5. Have a top level run.sh file that will be used to run the backend
6. Pushed to a registry so can be used in a gallery

Getting started

For getting started, see the available backends in LocalAI here: https://github.com/mudler/LocalAI/tree/master/backend .

• For Python based backends there is a template that can be used as starting point: https://github.com/mudler/LocalAI/tree/master/backend/python/common/template .
• For Golang based backends, you can see the bark-cpp backend as an example: https://github.com/mudler/LocalAI/tree/master/backend/go/bark-cpp
• For C++ based backends, you can see the llama-cpp backend as an example: https://github.com/mudler/LocalAI/tree/master/backend/cpp/llama-cpp

Publishing Your Backend

1. Build your container image:

   docker build -t quay.io/username/my-backend:latest .

2. Push to a container registry:

   docker push quay.io/username/my-backend:latest

3. Add your backend to a gallery:

   • Create a YAML entry in your gallery repository
   • Include the backend definition
   • Make the gallery accessible via HTTP/HTTPS

Backend Types

LocalAI supports various types of backends:

• LLM Backends: For running language models
• Diffusion Backends: For image generation
• TTS Backends: For text-to-speech conversion
• Whisper Backends: For speech-to-text conversion

⚡ GPU acceleration

This section contains instruction on how to use LocalAI with GPU acceleration.

Automatic Backend Detection

When you install a model from the gallery (or a YAML file), LocalAI intelligently detects the required backend and your system’s capabilities, then downloads the correct version for you. Whether you’re running on a standard CPU, an NVIDIA GPU, an AMD GPU, or an Intel GPU, LocalAI handles it automatically.

For advanced use cases or to override auto-detection, you can use the LOCALAI_FORCE_META_BACKEND_CAPABILITY environment variable. Here are the available options:

• default: Forces CPU-only backend. This is the fallback if no specific hardware is detected.
• nvidia: Forces backends compiled with CUDA support for NVIDIA GPUs.
• amd: Forces backends compiled with ROCm support for AMD GPUs.
• intel: Forces backends compiled with SYCL/oneAPI support for Intel GPUs.

Model configuration

Depending on the model architecture and backend used, there might be different ways to enable GPU acceleration. It is required to configure the model you intend to use with a YAML config file. For example, for llama.cpp workloads a configuration file might look like this (where gpu_layers is the number of layers to offload to the GPU):

name: my-model-name
parameters:
  # Relative to the models path
  model: llama.cpp-model.ggmlv3.q5_K_M.bin

context_size: 1024
threads: 1

f16: true # enable with GPU acceleration
gpu_layers: 22 # GPU Layers (only used when built with cublas)

For diffusers instead, it might look like this instead:

name: stablediffusion
parameters:
  model: toonyou_beta6.safetensors
backend: diffusers
step: 30
f16: true
diffusers:
  pipeline_type: StableDiffusionPipeline
  cuda: true
  enable_parameters: "negative_prompt,num_inference_steps,clip_skip"
  scheduler_type: "k_dpmpp_sde"

CUDA(NVIDIA) acceleration

Requirements

Requirement: nvidia-container-toolkit (installation instructions 1 2)

If using a system with SELinux, ensure you have the policies installed, such as those provided by nvidia

To check what CUDA version do you need, you can either run nvidia-smi or nvcc --version.

Alternatively, you can also check nvidia-smi with docker:

docker run --runtime=nvidia --rm nvidia/cuda:12.8.0-base-ubuntu24.04 nvidia-smi

To use CUDA, use the images with the cublas tag, for example.

The image list is on quay:

• CUDA 11 tags: master-gpu-nvidia-cuda-11, v1.40.0-gpu-nvidia-cuda-11, …
• CUDA 12 tags: master-gpu-nvidia-cuda-12, v1.40.0-gpu-nvidia-cuda-12, …

In addition to the commands to run LocalAI normally, you need to specify --gpus all to docker, for example:

docker run --rm -ti --gpus all -p 8080:8080 -e DEBUG=true -e MODELS_PATH=/models -e THREADS=1 -v $PWD/models:/models quay.io/go-skynet/local-ai:v1.40.0-gpu-nvidia-cuda12

If the GPU inferencing is working, you should be able to see something like:

5:22PM DBG Loading model in memory from file: /models/open-llama-7b-q4_0.bin
ggml_init_cublas: found 1 CUDA devices:
  Device 0: Tesla T4
llama.cpp: loading model from /models/open-llama-7b-q4_0.bin
llama_model_load_internal: format     = ggjt v3 (latest)
llama_model_load_internal: n_vocab    = 32000
llama_model_load_internal: n_ctx      = 1024
llama_model_load_internal: n_embd     = 4096
llama_model_load_internal: n_mult     = 256
llama_model_load_internal: n_head     = 32
llama_model_load_internal: n_layer    = 32
llama_model_load_internal: n_rot      = 128
llama_model_load_internal: ftype      = 2 (mostly Q4_0)
llama_model_load_internal: n_ff       = 11008
llama_model_load_internal: n_parts    = 1
llama_model_load_internal: model size = 7B
llama_model_load_internal: ggml ctx size =    0.07 MB
llama_model_load_internal: using CUDA for GPU acceleration
llama_model_load_internal: mem required  = 4321.77 MB (+ 1026.00 MB per state)
llama_model_load_internal: allocating batch_size x 1 MB = 512 MB VRAM for the scratch buffer
llama_model_load_internal: offloading 10 repeating layers to GPU
llama_model_load_internal: offloaded 10/35 layers to GPU
llama_model_load_internal: total VRAM used: 1598 MB
...................................................................................................
llama_init_from_file: kv self size  =  512.00 MB

ROCM(AMD) acceleration

There are a limited number of tested configurations for ROCm systems however most newer deditated GPU consumer grade devices seem to be supported under the current ROCm6 implementation.

Due to the nature of ROCm it is best to run all implementations in containers as this limits the number of packages required for installation on host system, compatibility and package versions for dependencies across all variations of OS must be tested independently if desired, please refer to the build documentation.

Requirements

• ROCm 6.x.x compatible GPU/accelerator
• OS: Ubuntu (22.04, 20.04), RHEL (9.3, 9.2, 8.9, 8.8), SLES (15.5, 15.4)
• Installed to host: amdgpu-dkms and rocm >=6.0.0 as per ROCm documentation.

Recommendations

• Make sure to do not use GPU assigned for compute for desktop rendering.
• Ensure at least 100GB of free space on disk hosting container runtime and storing images prior to installation.

Limitations

Ongoing verification testing of ROCm compatibility with integrated backends. Please note the following list of verified backends and devices.

LocalAI hipblas images are built against the following targets: gfx900,gfx906,gfx908,gfx940,gfx941,gfx942,gfx90a,gfx1030,gfx1031,gfx1100,gfx1101

If your device is not one of these you must specify the corresponding GPU_TARGETS and specify REBUILD=true. Otherwise you don’t need to specify these in the commands below.

Verified

The devices in the following list have been tested with hipblas images running ROCm 6.0.0

You can help by expanding this list.

System Prep

1. Check your GPU LLVM target is compatible with the version of ROCm. This can be found in the LLVM Docs.
2. Check which ROCm version is compatible with your LLVM target and your chosen OS (pay special attention to supported kernel versions). See the following for compatibility for (ROCm 6.0.0) or (ROCm 6.0.2)
3. Install you chosen version of the dkms and rocm (it is recommended that the native package manager be used for this process for any OS as version changes are executed more easily via this method if updates are required). Take care to restart after installing amdgpu-dkms and before installing rocm, for details regarding this see the installation documentation for your chosen OS (6.0.2 or 6.0.0)
4. Deploy. Yes it’s that easy.

Setup Example (Docker/containerd)

The following are examples of the ROCm specific configuration elements required.

    # For full functionality select a non-'core' image, version locking the image is recommended for debug purposes.
    image: quay.io/go-skynet/local-ai:master-aio-gpu-hipblas
    environment:
      - DEBUG=true
      # If your gpu is not already included in the current list of default targets the following build details are required.
      - REBUILD=true
      - BUILD_TYPE=hipblas
      - GPU_TARGETS=gfx906 # Example for Radeon VII
    devices:
      # AMD GPU only require the following devices be passed through to the container for offloading to occur.
      - /dev/dri
      - /dev/kfd

The same can also be executed as a run for your container runtime

docker run \
 -e DEBUG=true \
 -e REBUILD=true \
 -e BUILD_TYPE=hipblas \
 -e GPU_TARGETS=gfx906 \
 --device /dev/dri \
 --device /dev/kfd \
 quay.io/go-skynet/local-ai:master-aio-gpu-hipblas

Please ensure to add all other required environment variables, port forwardings, etc to your compose file or run command.

The rebuild process will take some time to complete when deploying these containers and it is recommended that you pull the image prior to deployment as depending on the version these images may be ~20GB in size.

Example (k8s) (Advanced Deployment/WIP)

For k8s deployments there is an additional step required before deployment, this is the deployment of the ROCm/k8s-device-plugin. For any k8s environment the documentation provided by AMD from the ROCm project should be successful. It is recommended that if you use rke2 or OpenShift that you deploy the SUSE or RedHat provided version of this resource to ensure compatibility. After this has been completed the helm chart from go-skynet can be configured and deployed mostly un-edited.

The following are details of the changes that should be made to ensure proper function. While these details may be configurable in the values.yaml development of this Helm chart is ongoing and is subject to change.

The following details indicate the final state of the localai deployment relevant to GPU function.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: {NAME}-local-ai
...
spec:
  ...
  template:
    ...
    spec:
      containers:
        - env:
            - name: HIP_VISIBLE_DEVICES
              value: '0'
              # This variable indicates the devices available to container (0:device1 1:device2 2:device3) etc.
              # For multiple devices (say device 1 and 3) the value would be equivalent to HIP_VISIBLE_DEVICES="0,2"
              # Please take note of this when an iGPU is present in host system as compatibility is not assured.
          ...
          resources:
            limits:
              amd.com/gpu: '1'
            requests:
              amd.com/gpu: '1'

This configuration has been tested on a ‘custom’ cluster managed by SUSE Rancher that was deployed on top of Ubuntu 22.04.4, certification of other configuration is ongoing and compatibility is not guaranteed.

Notes

• When installing the ROCM kernel driver on your system ensure that you are installing an equal or newer version that that which is currently implemented in LocalAI (6.0.0 at time of writing).
• AMD documentation indicates that this will ensure functionality however your mileage may vary depending on the GPU and distro you are using.
• If you encounter an Error 413 on attempting to upload an audio file or image for whisper or llava/bakllava on a k8s deployment, note that the ingress for your deployment may require the annotation nginx.ingress.kubernetes.io/proxy-body-size: "25m" to allow larger uploads. This may be included in future versions of the helm chart.

Intel acceleration (sycl)

Requirements

If building from source, you need to install Intel oneAPI Base Toolkit and have the Intel drivers available in the system.

Container images

To use SYCL, use the images with gpu-intel in the tag, for example v3.7.0-gpu-intel, …

The image list is on quay.

Example

To run LocalAI with Docker and sycl starting phi-2, you can use the following command as an example:

docker run -e DEBUG=true --privileged -ti -v $PWD/models:/models -p 8080:8080  -v /dev/dri:/dev/dri --rm quay.io/go-skynet/local-ai:master-gpu-intel phi-2

Notes

In addition to the commands to run LocalAI normally, you need to specify --device /dev/dri to docker, for example:

docker run --rm -ti --device /dev/dri -p 8080:8080 -e DEBUG=true -e MODELS_PATH=/models -e THREADS=1 -v $PWD/models:/models quay.io/go-skynet/local-ai:v3.7.0-gpu-intel

Note also that sycl does have a known issue to hang with mmap: true. You have to disable it in the model configuration if explicitly enabled.

Vulkan acceleration

Requirements

If using nvidia, follow the steps in the CUDA section to configure your docker runtime to allow access to the GPU.

Container images

To use Vulkan, use the images with the vulkan tag, for example v3.7.0-gpu-vulkan.

Example

To run LocalAI with Docker and Vulkan, you can use the following command as an example:

docker run -p 8080:8080 -e DEBUG=true -v $PWD/models:/models localai/localai:latest-gpu-vulkan

Notes

In addition to the commands to run LocalAI normally, you need to specify additional flags to pass the GPU hardware to the container.

These flags are the same as the sections above, depending on the hardware, for nvidia, AMD or Intel.

If you have mixed hardware, you can pass flags for multiple GPUs, for example:

docker run -p 8080:8080 -e DEBUG=true -v $PWD/models:/models \
--gpus=all \ # nvidia passthrough
--device /dev/dri --device /dev/kfd \ # AMD/Intel passthrough
localai/localai:latest-gpu-vulkan

📖 Text generation (GPT)

LocalAI supports generating text with GPT with llama.cpp and other backends (such as rwkv.cpp as ) see also the Model compatibility for an up-to-date list of the supported model families.

Note:

• You can also specify the model name as part of the OpenAI token.
• If only one model is available, the API will use it for all the requests.

API Reference

Chat completions

https://platform.openai.com/docs/api-reference/chat

For example, to generate a chat completion, you can send a POST request to the /v1/chat/completions endpoint with the instruction as the request body:

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "ggml-koala-7b-model-q4_0-r2.bin",
  "messages": [{"role": "user", "content": "Say this is a test!"}],
  "temperature": 0.7
}'

Available additional parameters: top_p, top_k, max_tokens

Edit completions

https://platform.openai.com/docs/api-reference/edits

To generate an edit completion you can send a POST request to the /v1/edits endpoint with the instruction as the request body:

curl http://localhost:8080/v1/edits -H "Content-Type: application/json" -d '{
  "model": "ggml-koala-7b-model-q4_0-r2.bin",
  "instruction": "rephrase",
  "input": "Black cat jumped out of the window",
  "temperature": 0.7
}'

Available additional parameters: top_p, top_k, max_tokens.

Completions

https://platform.openai.com/docs/api-reference/completions

To generate a completion, you can send a POST request to the /v1/completions endpoint with the instruction as per the request body:

curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
  "model": "ggml-koala-7b-model-q4_0-r2.bin",
  "prompt": "A long time ago in a galaxy far, far away",
  "temperature": 0.7
}'

Available additional parameters: top_p, top_k, max_tokens

List models

You can list all the models available with:

curl http://localhost:8080/v1/models

Backends

RWKV

RWKV support is available through llama.cpp (see below)

llama.cpp

llama.cpp is a popular port of Facebook’s LLaMA model in C/C++.

Features

The llama.cpp model supports the following features:

• 📖 Text generation (GPT)
• 🧠 Embeddings
• 🔥 OpenAI functions
• ✍️ Constrained grammars

Setup

LocalAI supports llama.cpp models out of the box. You can use the llama.cpp model in the same way as any other model.

Manual setup

It is sufficient to copy the ggml or gguf model files in the models folder. You can refer to the model in the model parameter in the API calls.

You can optionally create an associated YAML model config file to tune the model’s parameters or apply a template to the prompt.

Prompt templates are useful for models that are fine-tuned towards a specific prompt.

Automatic setup

LocalAI supports model galleries which are indexes of models. For instance, the huggingface gallery contains a large curated index of models from the huggingface model hub for ggml or gguf models.

For instance, if you have the galleries enabled and LocalAI already running, you can just start chatting with models in huggingface by running:

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
     "model": "TheBloke/WizardLM-13B-V1.2-GGML/wizardlm-13b-v1.2.ggmlv3.q2_K.bin",
     "messages": [{"role": "user", "content": "Say this is a test!"}],
     "temperature": 0.1
   }'

LocalAI will automatically download and configure the model in the model directory.

Models can be also preloaded or downloaded on demand. To learn about model galleries, check out the model gallery documentation.

YAML configuration

To use the llama.cpp backend, specify llama-cpp as the backend in the YAML file:

name: llama
backend: llama-cpp
parameters:
  # Relative to the models path
  model: file.gguf

Backend Options

The llama.cpp backend supports additional configuration options that can be specified in the options field of your model YAML configuration. These options allow fine-tuning of the backend behavior:

Example configuration with options:

name: llama-model
backend: llama
parameters:
  model: model.gguf
options:
  - use_jinja:true
  - context_shift:true
  - cache_ram:4096
  - parallel:2

Note: The parallel option can also be set via the LLAMACPP_PARALLEL environment variable, and grpc_servers can be set via the LLAMACPP_GRPC_SERVERS environment variable. Options specified in the YAML file take precedence over environment variables.

Reference

• llama

exllama/2

Exllama is a “A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights”. Both exllama and exllama2 are supported.

Model setup

Download the model as a folder inside the model directory and create a YAML file specifying the exllama backend. For instance with the TheBloke/WizardLM-7B-uncensored-GPTQ model:

$ git lfs install
$ cd models && git clone https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ
$ ls models/                                                                 
.keep                        WizardLM-7B-uncensored-GPTQ/ exllama.yaml
$ cat models/exllama.yaml                                                     
name: exllama
parameters:
  model: WizardLM-7B-uncensored-GPTQ
backend: exllama

Test with:

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{                                                                                                         
   "model": "exllama",
   "messages": [{"role": "user", "content": "How are you?"}],
   "temperature": 0.1
 }'

vLLM

vLLM is a fast and easy-to-use library for LLM inference.

LocalAI has a built-in integration with vLLM, and it can be used to run models. You can check out vllm performance here.

Setup

Create a YAML file for the model you want to use with vllm.

To setup a model, you need to just specify the model name in the YAML config file:

name: vllm
backend: vllm
parameters:
    model: "facebook/opt-125m"

The backend will automatically download the required files in order to run the model.

Usage

Use the completions endpoint by specifying the vllm backend:

curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{   
   "model": "vllm",
   "prompt": "Hello, my name is",
   "temperature": 0.1, "top_p": 0.1
 }'

Transformers

Transformers is a State-of-the-art Machine Learning library for PyTorch, TensorFlow, and JAX.

LocalAI has a built-in integration with Transformers, and it can be used to run models.

This is an extra backend - in the container images (the extra images already contains python dependencies for Transformers) is already available and there is nothing to do for the setup.

Setup

Create a YAML file for the model you want to use with transformers.

To setup a model, you need to just specify the model name in the YAML config file:

name: transformers
backend: transformers
parameters:
    model: "facebook/opt-125m"
type: AutoModelForCausalLM
quantization: bnb_4bit # One of: bnb_8bit, bnb_4bit, xpu_4bit, xpu_8bit (optional)

The backend will automatically download the required files in order to run the model.

Parameters

Type

• OVModelForCausalLM requires OpenVINO IR Text Generation models from Hugging face
• OVModelForFeatureExtraction works with any Safetensors Transformer Feature Extraction model from Huggingface (Embedding Model)

Please note that streaming is currently not implemente in AutoModelForCausalLM for Intel GPU. AMD GPU support is not implemented. Although AMD CPU is not officially supported by OpenVINO there are reports that it works: YMMV.

Embeddings

Use embeddings: true if the model is an embedding model

Inference device selection

Transformer backend tries to automatically select the best device for inference, anyway you can override the decision manually overriding with the main_gpu parameter.

Example for CUDA: main_gpu: cuda.0

Example for OpenVINO: main_gpu: AUTO:-CPU

This parameter applies to both Text Generation and Feature Extraction (i.e. Embeddings) models.

Inference Precision

Transformer backend automatically select the fastest applicable inference precision according to the device support. CUDA backend can manually enable bfloat16 if your hardware support it with the following parameter:

f16: true

Quantization

Trust Remote Code

Some models like Microsoft Phi-3 requires external code than what is provided by the transformer library. By default it is disabled for security. It can be manually enabled with: trust_remote_code: true

Maximum Context Size

Maximum context size in bytes can be specified with the parameter: context_size. Do not use values higher than what your model support.

Usage example: context_size: 8192

Auto Prompt Template

Usually chat template is defined by the model author in the tokenizer_config.json file. To enable it use the use_tokenizer_template: true parameter in the template section.

Usage example:

template:
  use_tokenizer_template: true

Custom Stop Words

Stopwords are usually defined in tokenizer_config.json file. They can be overridden with the stopwords parameter in case of need like in llama3-Instruct model.

Usage example:

stopwords:
- "<|eot_id|>"
- "<|end_of_text|>"

Usage

Use the completions endpoint by specifying the transformers model:

curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{   
   "model": "transformers",
   "prompt": "Hello, my name is",
   "temperature": 0.1, "top_p": 0.1
 }'

Examples

OpenVINO

A model configuration file for openvion and starling model:

name: starling-openvino
backend: transformers
parameters:
  model: fakezeta/Starling-LM-7B-beta-openvino-int8
context_size: 8192
threads: 6
f16: true
type: OVModelForCausalLM
stopwords:
- <|end_of_turn|>
- <|endoftext|>
prompt_cache_path: "cache"
prompt_cache_all: true
template:
  chat_message: |
    {{if eq .RoleName "system"}}{{.Content}}<|end_of_turn|>{{end}}{{if eq .RoleName "assistant"}}<|end_of_turn|>GPT4 Correct Assistant: {{.Content}}<|end_of_turn|>{{end}}{{if eq .RoleName "user"}}GPT4 Correct User: {{.Content}}{{end}}

  chat: |
    {{.Input}}<|end_of_turn|>GPT4 Correct Assistant:

  completion: |
    {{.Input}}

📈 Reranker

A reranking model, often referred to as a cross-encoder, is a core component in the two-stage retrieval systems used in information retrieval and natural language processing tasks. Given a query and a set of documents, it will output similarity scores.

We can use then the score to reorder the documents by relevance in our RAG system to increase its overall accuracy and filter out non-relevant results.

LocalAI supports reranker models, and you can use them by using the rerankers backend, which uses rerankers.

Usage

You can test rerankers by using container images with python (this does NOT work with core images) and a model config file like this, or by installing cross-encoder from the gallery in the UI:

name: jina-reranker-v1-base-en
backend: rerankers
parameters:
  model: cross-encoder

and test it with:

    curl http://localhost:8080/v1/rerank \
      -H "Content-Type: application/json" \
      -d '{
      "model": "jina-reranker-v1-base-en",
      "query": "Organic skincare products for sensitive skin",
      "documents": [
        "Eco-friendly kitchenware for modern homes",
        "Biodegradable cleaning supplies for eco-conscious consumers",
        "Organic cotton baby clothes for sensitive skin",
        "Natural organic skincare range for sensitive skin",
        "Tech gadgets for smart homes: 2024 edition",
        "Sustainable gardening tools and compost solutions",
        "Sensitive skin-friendly facial cleansers and toners",
        "Organic food wraps and storage solutions",
        "All-natural pet food for dogs with allergies",
        "Yoga mats made from recycled materials"
      ],
      "top_n": 3
    }'

🗣 Text to audio (TTS)

API Compatibility

The LocalAI TTS API is compatible with the OpenAI TTS API and the Elevenlabs API.

LocalAI API

The /tts endpoint can also be used to generate speech from text.

Usage

Input: input, model

For example, to generate an audio file, you can send a POST request to the /tts endpoint with the instruction as the request body:

curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
  "input": "Hello world",
  "model": "tts"
}'

Returns an audio/wav file.

Backends

🐸 Coqui

Required: Don’t use LocalAI images ending with the -core tag,. Python dependencies are required in order to use this backend.

Coqui works without any configuration, to test it, you can run the following curl command:

    curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
        "backend": "coqui",
        "model": "tts_models/en/ljspeech/glow-tts",
        "input":"Hello, this is a test!"
        }'

You can use the env variable COQUI_LANGUAGE to set the language used by the coqui backend.

You can also use config files to configure tts models (see section below on how to use config files).

Bark

Bark allows to generate audio from text prompts.

This is an extra backend - in the container is already available and there is nothing to do for the setup.

Model setup

There is nothing to be done for the model setup. You can already start to use bark. The models will be downloaded the first time you use the backend.

Usage

Use the tts endpoint by specifying the bark backend:

curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "backend": "bark",
     "input":"Hello!"
   }' | aplay

To specify a voice from https://github.com/suno-ai/bark#-voice-presets ( https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c ), use the model parameter:

curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "backend": "bark",
     "input":"Hello!",
     "model": "v2/en_speaker_4"
   }' | aplay

Piper

To install the piper audio models manually:

• Download Voices from https://github.com/rhasspy/piper/releases/tag/v0.0.2
• Extract the .tar.tgz files (.onnx,.json) inside models
• Run the following command to test the model is working

To use the tts endpoint, run the following command. You can specify a backend with the backend parameter. For example, to use the piper backend:

curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
  "model":"it-riccardo_fasol-x-low.onnx",
  "backend": "piper",
  "input": "Ciao, sono Ettore"
}' | aplay

Note:

• aplay is a Linux command. You can use other tools to play the audio file.
• The model name is the filename with the extension.
• The model name is case sensitive.
• LocalAI must be compiled with the GO_TAGS=tts flag.

Transformers-musicgen

LocalAI also has experimental support for transformers-musicgen for the generation of short musical compositions. Currently, this is implemented via the same requests used for text to speech:

curl --request POST \
  --url http://localhost:8080/tts \
  --header 'Content-Type: application/json' \
  --data '{
    "backend": "transformers-musicgen",
    "model": "facebook/musicgen-medium",
    "input": "Cello Rave"
}' | aplay

Future versions of LocalAI will expose additional control over audio generation beyond the text prompt.

Vall-E-X

VALL-E-X is an open source implementation of Microsoft’s VALL-E X zero-shot TTS model.

Setup

The backend will automatically download the required files in order to run the model.

This is an extra backend - in the container is already available and there is nothing to do for the setup. If you are building manually, you need to install Vall-E-X manually first.

Usage

Use the tts endpoint by specifying the vall-e-x backend:

curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "backend": "vall-e-x",
     "input":"Hello!"
   }' | aplay

Voice cloning

In order to use voice cloning capabilities you must create a YAML configuration file to setup a model:

name: cloned-voice
backend: vall-e-x
parameters:
  model: "cloned-voice"
tts:
    vall-e:
      # The path to the audio file to be cloned
      # relative to the models directory
      # Max 15s
      audio_path: "audio-sample.wav"

Then you can specify the model name in the requests:

curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "model": "cloned-voice",
     "input":"Hello!"
   }' | aplay

Using config files

You can also use a config-file to specify TTS models and their parameters.

In the following example we define a custom config to load the xtts_v2 model, and specify a voice and language.

name: xtts_v2
backend: coqui
parameters:
  language: fr
  model: tts_models/multilingual/multi-dataset/xtts_v2

tts:
  voice: Ana Florence

With this config, you can now use the following curl command to generate a text-to-speech audio file:

curl -L http://localhost:8080/tts \
    -H "Content-Type: application/json" \
    -d '{
"model": "xtts_v2",
"input": "Bonjour, je suis Ana Florence. Comment puis-je vous aider?"
}' | aplay

Response format

To provide some compatibility with OpenAI API regarding response_format, ffmpeg must be installed (or a docker image including ffmpeg used) to leverage converting the generated wav file before the api provide its response.

Warning regarding a change in behaviour. Before this addition, the parameter was ignored and a wav file was always returned, with potential codec errors later in the integration (like trying to decode a mp3 file from a wav, which is the default format used by OpenAI)

Supported format thanks to ffmpeg are wav, mp3, aac, flac, opus, defaulting to wav if an unknown or no format is provided.

curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
  "input": "Hello world",
  "model": "tts",
  "response_format": "mp3"
}'

If a response_format is added in the query (other than wav) and ffmpeg is not available, the call will fail.

🎨 Image generation

(Generated with AnimagineXL)

LocalAI supports generating images with Stable diffusion, running on CPU using C++ and Python implementations.

Usage

OpenAI docs: https://platform.openai.com/docs/api-reference/images/create

To generate an image you can send a POST request to the /v1/images/generations endpoint with the instruction as the request body:

curl http://localhost:8080/v1/images/generations -H "Content-Type: application/json" -d '{
  "prompt": "A cute baby sea otter",
  "size": "256x256"
}'

Available additional parameters: mode, step.

Note: To set a negative prompt, you can split the prompt with |, for instance: a cute baby sea otter|malformed.

curl http://localhost:8080/v1/images/generations -H "Content-Type: application/json" -d '{
  "prompt": "floating hair, portrait, ((loli)), ((one girl)), cute face, hidden hands, asymmetrical bangs, beautiful detailed eyes, eye shadow, hair ornament, ribbons, bowties, buttons, pleated skirt, (((masterpiece))), ((best quality)), colorful|((part of the head)), ((((mutated hands and fingers)))), deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, Octane renderer, lowres, bad anatomy, bad hands, text",
  "size": "256x256"
}'

Backends

stablediffusion-ggml

This backend is based on stable-diffusion.cpp. Every model supported by that backend is supported indeed with LocalAI.

Setup

There are already several models in the gallery that are available to install and get up and running with this backend, you can for example run flux by searching it in the Model gallery (flux.1-dev-ggml) or start LocalAI with run:

local-ai run flux.1-dev-ggml

To use a custom model, you can follow these steps:

1. Create a model file stablediffusion.yaml in the models folder:

name: stablediffusion
backend: stablediffusion-ggml
parameters:
  model: gguf_model.gguf
step: 25
cfg_scale: 4.5
options:
- "clip_l_path:clip_l.safetensors"
- "clip_g_path:clip_g.safetensors"
- "t5xxl_path:t5xxl-Q5_0.gguf"
- "sampler:euler"

2. Download the required assets to the models repository
3. Start LocalAI

Diffusers

Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. LocalAI has a diffusers backend which allows image generation using the diffusers library.

(Generated with AnimagineXL)

Model setup

The models will be downloaded the first time you use the backend from huggingface automatically.

Create a model configuration file in the models directory, for instance to use Linaqruf/animagine-xl with CPU:

name: animagine-xl
parameters:
  model: Linaqruf/animagine-xl
backend: diffusers

f16: false
diffusers:
  cuda: false # Enable for GPU usage (CUDA)
  scheduler_type: euler_a

Dependencies

This is an extra backend - in the container is already available and there is nothing to do for the setup. Do not use core images (ending with -core). If you are building manually, see the build instructions.

Model setup

The models will be downloaded the first time you use the backend from huggingface automatically.

Create a model configuration file in the models directory, for instance to use Linaqruf/animagine-xl with CPU:

name: animagine-xl
parameters:
  model: Linaqruf/animagine-xl
backend: diffusers
cuda: true
f16: true
diffusers:
  scheduler_type: euler_a

Local models

You can also use local models, or modify some parameters like clip_skip, scheduler_type, for instance:

name: stablediffusion
parameters:
  model: toonyou_beta6.safetensors
backend: diffusers
step: 30
f16: true
cuda: true
diffusers:
  pipeline_type: StableDiffusionPipeline
  enable_parameters: "negative_prompt,num_inference_steps,clip_skip"
  scheduler_type: "k_dpmpp_sde"
  clip_skip: 11

cfg_scale: 8

Configuration parameters

The following parameters are available in the configuration file:

There are available several types of schedulers:

Pipelines types available:

Advanced: Additional parameters

Additional arbitrarly parameters can be specified in the option field in key/value separated by ::

name: animagine-xl
options:
- "cfg_scale:6"

Note: There is no complete parameter list. Any parameter can be passed arbitrarly and is passed to the model directly as argument to the pipeline. Different pipelines/implementations support different parameters.

The example above, will result in the following python code when generating images:

pipe(
    prompt="A cute baby sea otter", # Options passed via API
    size="256x256", # Options passed via API
    cfg_scale=6 # Additional parameter passed via configuration file
)

Usage

Text to Image

Use the image generation endpoint with the model name from the configuration file:

curl http://localhost:8080/v1/images/generations \
    -H "Content-Type: application/json" \
    -d '{
      "prompt": "<positive prompt>|<negative prompt>", 
      "model": "animagine-xl", 
      "step": 51,
      "size": "1024x1024" 
    }'

Image to Image

https://huggingface.co/docs/diffusers/using-diffusers/img2img

An example model (GPU):

name: stablediffusion-edit
parameters:
  model: nitrosocke/Ghibli-Diffusion
backend: diffusers
step: 25
cuda: true
f16: true
diffusers:
  pipeline_type: StableDiffusionImg2ImgPipeline
  enable_parameters: "negative_prompt,num_inference_steps,image"

IMAGE_PATH=/path/to/your/image
(echo -n '{"file": "'; base64 $IMAGE_PATH; echo '", "prompt": "a sky background","size": "512x512","model":"stablediffusion-edit"}') |
curl -H "Content-Type: application/json" -d @-  http://localhost:8080/v1/images/generations

🖼️ Flux kontext with stable-diffusion.cpp

LocalAI supports Flux Kontext and can be used to edit images via the API:

Install with:

local-ai run flux.1-kontext-dev

To test:

curl http://localhost:8080/v1/images/generations -H "Content-Type: application/json" -d '{
  "model": "flux.1-kontext-dev",
  "prompt": "change 'flux.cpp' to 'LocalAI'",
  "size": "256x256",
  "ref_images": [
  	"https://raw.githubusercontent.com/leejet/stable-diffusion.cpp/master/assets/flux/flux1-dev-q8_0.png"
  ]
}'

Depth to Image

https://huggingface.co/docs/diffusers/using-diffusers/depth2img

name: stablediffusion-depth
parameters:
  model: stabilityai/stable-diffusion-2-depth
backend: diffusers
step: 50
f16: true
cuda: true
diffusers:
  pipeline_type: StableDiffusionDepth2ImgPipeline
  enable_parameters: "negative_prompt,num_inference_steps,image"

cfg_scale: 6

(echo -n '{"file": "'; base64 ~/path/to/image.jpeg; echo '", "prompt": "a sky background","size": "512x512","model":"stablediffusion-depth"}') |
curl -H "Content-Type: application/json" -d @-  http://localhost:8080/v1/images/generations

img2vid

name: img2vid
parameters:
  model: stabilityai/stable-video-diffusion-img2vid
backend: diffusers
step: 25
f16: true
cuda: true
diffusers:
  pipeline_type: StableVideoDiffusionPipeline

(echo -n '{"file": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true","size": "512x512","model":"img2vid"}') |
curl -H "Content-Type: application/json" -X POST -d @- http://localhost:8080/v1/images/generations

txt2vid

name: txt2vid
parameters:
  model: damo-vilab/text-to-video-ms-1.7b
backend: diffusers
step: 25
f16: true
cuda: true
diffusers:
  pipeline_type: VideoDiffusionPipeline
  cuda: true

(echo -n '{"prompt": "spiderman surfing","size": "512x512","model":"txt2vid"}') |
curl -H "Content-Type: application/json" -X POST -d @- http://localhost:8080/v1/images/generations

🔍 Object detection

LocalAI supports object detection through various backends. This feature allows you to identify and locate objects within images with high accuracy and real-time performance. Currently, RF-DETR is available as an implementation.

Overview

Object detection in LocalAI is implemented through dedicated backends that can identify and locate objects within images. Each backend provides different capabilities and model architectures.

Key Features:

• Real-time object detection
• High accuracy detection with bounding boxes
• Support for multiple hardware accelerators (CPU, NVIDIA GPU, Intel GPU, AMD GPU)
• Structured detection results with confidence scores
• Easy integration through the /v1/detection endpoint

Usage

Detection Endpoint

LocalAI provides a dedicated /v1/detection endpoint for object detection tasks. This endpoint is specifically designed for object detection and returns structured detection results with bounding boxes and confidence scores.

API Reference

To perform object detection, send a POST request to the /v1/detection endpoint:

curl -X POST http://localhost:8080/v1/detection \
  -H "Content-Type: application/json" \
  -d '{
    "model": "rfdetr-base",
    "image": "https://media.roboflow.com/dog.jpeg"
  }'

Request Format

The request body should contain:

• model: The name of the object detection model (e.g., “rfdetr-base”)
• image: The image to analyze, which can be:
  • A URL to an image
  • A base64-encoded image

Response Format

The API returns a JSON response with detected objects:

{
  "detections": [
    {
      "x": 100.5,
      "y": 150.2,
      "width": 200.0,
      "height": 300.0,
      "confidence": 0.95,
      "class_name": "dog"
    },
    {
      "x": 400.0,
      "y": 200.0,
      "width": 150.0,
      "height": 250.0,
      "confidence": 0.87,
      "class_name": "person"
    }
  ]
}

Each detection includes:

• x, y: Coordinates of the bounding box top-left corner
• width, height: Dimensions of the bounding box
• confidence: Detection confidence score (0.0 to 1.0)
• class_name: The detected object class

Backends

RF-DETR Backend

The RF-DETR backend is implemented as a Python-based gRPC service that integrates seamlessly with LocalAI. It provides object detection capabilities using the RF-DETR model architecture and supports multiple hardware configurations:

• CPU: Optimized for CPU inference
• NVIDIA GPU: CUDA acceleration for NVIDIA GPUs
• Intel GPU: Intel oneAPI optimization
• AMD GPU: ROCm acceleration for AMD GPUs
• NVIDIA Jetson: Optimized for ARM64 NVIDIA Jetson devices

Setup

1. Using the Model Gallery (Recommended)

   The easiest way to get started is using the model gallery. The rfdetr-base model is available in the official LocalAI gallery:

   # Install and run the rfdetr-base model
   local-ai run rfdetr-base

   You can also install it through the web interface by navigating to the Models section and searching for “rfdetr-base”.

2. Manual Configuration

   Create a model configuration file in your models directory:

   name: rfdetr
   backend: rfdetr
   parameters:
     model: rfdetr-base

Available Models

Currently, the following model is available in the Model Gallery:

• rfdetr-base: Base model with balanced performance and accuracy

You can browse and install this model through the LocalAI web interface or using the command line.

Examples

Basic Object Detection

curl -X POST http://localhost:8080/v1/detection \
  -H "Content-Type: application/json" \
  -d '{
    "model": "rfdetr-base",
    "image": "https://example.com/image.jpg"
  }'

Base64 Image Detection

base64_image=$(base64 -w 0 image.jpg)
curl -X POST http://localhost:8080/v1/detection \
  -H "Content-Type: application/json" \
  -d "{
    \"model\": \"rfdetr-base\",
    \"image\": \"data:image/jpeg;base64,$base64_image\"
  }"

Troubleshooting

Common Issues

1. Model Loading Errors

   • Ensure the model file is properly downloaded
   • Check available disk space
   • Verify model compatibility with your backend version

2. Low Detection Accuracy

   • Ensure good image quality and lighting
   • Check if objects are clearly visible
   • Consider using a larger model for better accuracy

3. Slow Performance

   • Enable GPU acceleration if available
   • Use a smaller model for faster inference
   • Optimize image resolution

Debug Mode

Enable debug logging for troubleshooting:

local-ai run --debug rfdetr-base

Object Detection Category

LocalAI includes a dedicated object-detection category for models and backends that specialize in identifying and locating objects within images. This category currently includes:

• RF-DETR: Real-time transformer-based object detection

Additional object detection models and backends will be added to this category in the future. You can filter models by the object-detection tag in the model gallery to find all available object detection models.

Related Features

• 🎨 Image generation: Generate images with AI
• 📖 Text generation: Generate text with language models
• 🔍 GPT Vision: Analyze images with language models
• 🚀 GPU acceleration: Optimize performance with GPU acceleration

🧠 Embeddings

LocalAI supports generating embeddings for text or list of tokens.

For the API documentation you can refer to the OpenAI docs: https://platform.openai.com/docs/api-reference/embeddings

Model compatibility

The embedding endpoint is compatible with llama.cpp models, bert.cpp models and sentence-transformers models available in huggingface.

Manual Setup

Create a YAML config file in the models directory. Specify the backend and the model file.

name: text-embedding-ada-002 # The model name used in the API
parameters:
  model: <model_file>
backend: "<backend>"
embeddings: true

Huggingface embeddings

To use sentence-transformers and models in huggingface you can use the sentencetransformers embedding backend.

name: text-embedding-ada-002
backend: sentencetransformers
embeddings: true
parameters:
  model: all-MiniLM-L6-v2

The sentencetransformers backend uses Python sentence-transformers. For a list of all pre-trained models available see here: https://github.com/UKPLab/sentence-transformers#pre-trained-models

Llama.cpp embeddings

Embeddings with llama.cpp are supported with the llama-cpp backend, it needs to be enabled with embeddings set to true.

name: my-awesome-model
backend: llama-cpp
embeddings: true
parameters:
  model: ggml-file.bin

Then you can use the API to generate embeddings:

curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
  "input": "My text",
  "model": "my-awesome-model"
}' | jq "."

💡 Examples

• Example that uses LLamaIndex and LocalAI as embedding: here.

🥽 GPT Vision

LocalAI supports understanding images by using LLaVA, and implements the GPT Vision API from OpenAI.

Usage

OpenAI docs: https://platform.openai.com/docs/guides/vision

To let LocalAI understand and reply with what sees in the image, use the /v1/chat/completions endpoint, for example with curl:

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
     "model": "llava",
     "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}]}'

Grammars and function tools can be used as well in conjunction with vision APIs:

 curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
     "model": "llava", "grammar": "root ::= (\"yes\" | \"no\")",
     "messages": [{"role": "user", "content": [{"type":"text", "text": "Is there some grass 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}]}'

Setup

All-in-One images have already shipped the llava model as gpt-4-vision-preview, so no setup is needed in this case.

To setup the LLaVa models, follow the full example in the configuration examples.

✍️ Constrained Grammars

Overview

The chat endpoint supports the grammar parameter, which allows users to specify a grammar in Backus-Naur Form (BNF). This feature enables the Large Language Model (LLM) to generate outputs adhering to a user-defined schema, such as JSON, YAML, or any other format that can be defined using BNF. For more details about BNF, see Backus-Naur Form on Wikipedia.

Setup

To use this feature, follow the installation and setup instructions on the LocalAI Functions page. Ensure that your local setup meets all the prerequisites specified for the llama.cpp backend.

💡 Usage Example

The following example demonstrates how to use the grammar parameter to constrain the model’s output to either “yes” or “no”. This can be particularly useful in scenarios where the response format needs to be strictly controlled.

Example: Binary Response Constraint

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "gpt-4",
  "messages": [{"role": "user", "content": "Do you like apples?"}],
  "grammar": "root ::= (\"yes\" | \"no\")"
}'

In this example, the grammar parameter is set to a simple choice between “yes” and “no”, ensuring that the model’s response adheres strictly to one of these options regardless of the context.

Example: JSON Output Constraint

You can also use grammars to enforce JSON output format:

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "gpt-4",
  "messages": [{"role": "user", "content": "Generate a person object with name and age"}],
  "grammar": "root ::= \"{\" \"\\\"name\\\":\" string \",\\\"age\\\":\" number \"}\"\nstring ::= \"\\\"\" [a-z]+ \"\\\"\"\nnumber ::= [0-9]+"
}'

Example: YAML Output Constraint

Similarly, you can enforce YAML format:

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "gpt-4",
  "messages": [{"role": "user", "content": "Generate a YAML list of fruits"}],
  "grammar": "root ::= \"fruits:\" newline (\"  - \" string newline)+\nstring ::= [a-z]+\nnewline ::= \"\\n\""
}'

Advanced Usage

For more complex grammars, you can define multi-line BNF rules. The grammar parser supports:

• Alternation (|)
• Repetition (*, +)
• Optional elements (?)
• Character classes ([a-z])
• String literals ("text")

Related Features

• OpenAI Functions - Function calling with structured outputs
• Text Generation - General text generation capabilities

🆕🖧 Distributed Inference

This functionality enables LocalAI to distribute inference requests across multiple worker nodes, improving efficiency and performance. Nodes are automatically discovered and connect via p2p by using a shared token which makes sure the communication is secure and private between the nodes of the network.

LocalAI supports two modes of distributed inferencing via p2p:

• Federated Mode: Requests are shared between the cluster and routed to a single worker node in the network based on the load balancer’s decision.
• Worker Mode (aka “model sharding” or “splitting weights”): Requests are processed by all the workers which contributes to the final inference result (by sharing the model weights).

A list of global instances shared by the community is available at explorer.localai.io.

Usage

Starting LocalAI with --p2p generates a shared token for connecting multiple instances: and that’s all you need to create AI clusters, eliminating the need for intricate network setups.

Simply navigate to the “Swarm” section in the WebUI and follow the on-screen instructions.

For fully shared instances, initiate LocalAI with –p2p –federated and adhere to the Swarm section’s guidance. This feature, while still experimental, offers a tech preview quality experience.

Federated mode

Federated mode allows to launch multiple LocalAI instances and connect them together in a federated network. This mode is useful when you want to distribute the load of the inference across multiple nodes, but you want to have a single point of entry for the API. In the Swarm section of the WebUI, you can see the instructions to connect multiple instances together.

To start a LocalAI server in federated mode, run:

local-ai run --p2p --federated

This will generate a token that you can use to connect other LocalAI instances to the network or others can use to join the network. If you already have a token, you can specify it using the TOKEN environment variable.

To start a load balanced server that routes the requests to the network, run with the TOKEN:

local-ai federated

To see all the available options, run local-ai federated --help.

The instructions are displayed in the “Swarm” section of the WebUI, guiding you through the process of connecting multiple instances.

Workers mode

To connect multiple workers to a single LocalAI instance, start first a server in p2p mode:

local-ai run --p2p

And navigate the WebUI to the “Swarm” section to see the instructions to connect multiple workers to the network.

Without P2P

To start workers for distributing the computational load, run:

local-ai worker llama-cpp-rpc --llama-cpp-args="-H <listening_address> -p <listening_port> -m <memory>" 

And you can specify the address of the workers when starting LocalAI with the LLAMACPP_GRPC_SERVERS environment variable:

LLAMACPP_GRPC_SERVERS="address1:port,address2:port" local-ai run

The workload on the LocalAI server will then be distributed across the specified nodes.

Alternatively, you can build the RPC workers/server following the llama.cpp README, which is compatible with LocalAI.

Manual example (worker)

Use the WebUI to guide you in the process of starting new workers. This example shows the manual steps to highlight the process.

1. Start the server with --p2p:

./local-ai run --p2p

Copy the token from the WebUI or via API call (e.g., curl http://localhost:8000/p2p/token) and save it for later use.

To reuse the same token later, restart the server with --p2ptoken or P2P_TOKEN.

2. Start the workers. Copy the local-ai binary to other hosts and run as many workers as needed using the token:

TOKEN=XXX ./local-ai worker p2p-llama-cpp-rpc --llama-cpp-args="-m <memory>" 

(Note: You can also supply the token via command-line arguments)

The server logs should indicate that new workers are being discovered.

3. Start inference as usual on the server initiated in step 1.

Environment Variables

There are options that can be tweaked or parameters that can be set using environment variables

Architecture

LocalAI uses https://github.com/libp2p/go-libp2p under the hood, the same project powering IPFS. Differently from other frameworks, LocalAI uses peer2peer without a single master server, but rather it uses sub/gossip and ledger functionalities to achieve consensus across different peers.

EdgeVPN is used as a library to establish the network and expose the ledger functionality under a shared token to ease out automatic discovery and have separated, private peer2peer networks.

The weights are split proportional to the memory when running into worker mode, when in federation mode each request is split to every node which have to load the model fully.

Debugging

To debug, it’s often useful to run in debug mode, for instance:

LOCALAI_P2P_LOGLEVEL=debug LOCALAI_P2P_LIB_LOGLEVEL=debug LOCALAI_P2P_ENABLE_LIMITS=true LOCALAI_P2P_DISABLE_DHT=true LOCALAI_P2P_TOKEN="<TOKEN>" ./local-ai ...

Notes

• If running in p2p mode with container images, make sure you start the container with --net host or network_mode: host in the docker-compose file.
• Only a single model is supported currently.
• Ensure the server detects new workers before starting inference. Currently, additional workers cannot be added once inference has begun.
• For more details on the implementation, refer to LocalAI pull request #2343

🔈 Audio to text

Audio to text models are models that can generate text from an audio file.

The transcription endpoint allows to convert audio files to text. The endpoint is based on whisper.cpp, a C++ library for audio transcription. The endpoint input supports all the audio formats supported by ffmpeg.

Usage

Once LocalAI is started and whisper models are installed, you can use the /v1/audio/transcriptions API endpoint.

For instance, with cURL:

curl http://localhost:8080/v1/audio/transcriptions -H "Content-Type: multipart/form-data" -F file="@<FILE_PATH>" -F model="<MODEL_NAME>"

Example

Download one of the models from here in the models folder, and create a YAML file for your model:

name: whisper-1
backend: whisper
parameters:
  model: whisper-en

The transcriptions endpoint then can be tested like so:

## Get an example audio file
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"

## Result
{"text":"My fellow Americans, this day has brought terrible news and great sadness to our country.At nine o'clock this morning, Mission Control in Houston lost contact with our Space ShuttleColumbia.A short time later, debris was seen falling from the skies above Texas.The Columbia's lost.There are no survivors.One board was a crew of seven.Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark, Captain DavidBrown, Commander William McCool, Dr. Kultna Shavla, and Elon Ramon, a colonel in the IsraeliAir Force.These men and women assumed great risk in the service to all humanity.In an age when spaceflight has come to seem almost routine, it is easy to overlook thedangers of travel by rocket and the difficulties of navigating the fierce outer atmosphere ofthe Earth.These astronauts knew the dangers, and they faced them willingly, knowing they had a highand noble purpose in life.Because of their courage and daring and idealism, we will miss them all the more.All Americans today are thinking as well of the families of these men and women who havebeen given this sudden shock and grief.You're not alone.Our entire nation agrees with you, and those you loved will always have the respect andgratitude of this country.The cause in which they died will continue.Mankind has led into the darkness beyond our world by the inspiration of discovery andthe longing to understand.Our journey into space will go on.In the skies today, we saw destruction and tragedy.As farther than we can see, there is comfort and hope.In the words of the prophet Isaiah, \"Lift your eyes and look to the heavens who createdall these, he who brings out the starry hosts one by one and calls them each by name.\"Because of his great power and mighty strength, not one of them is missing.The same creator who names the stars also knows the names of the seven souls we mourntoday.The crew of the shuttle Columbia did not return safely to Earth yet we can pray that all aresafely home.May God bless the grieving families and may God continue to bless America.[BLANK_AUDIO]"}

🔥 OpenAI functions and tools

LocalAI supports running OpenAI functions and tools API with llama.cpp compatible models.

To learn more about OpenAI functions, see also the OpenAI API blog post.

LocalAI is also supporting JSON mode out of the box with llama.cpp-compatible models.

💡 Check out also LocalAGI for an example on how to use LocalAI functions.

Setup

OpenAI functions are available only with ggml or gguf models compatible with llama.cpp.

You don’t need to do anything specific - just use ggml or gguf models.

Usage example

You can configure a model manually with a YAML config file in the models directory, for example:

name: gpt-3.5-turbo
parameters:
  # Model file name
  model: ggml-openllama.bin
  top_p: 80
  top_k: 0.9
  temperature: 0.1

To use the functions with the OpenAI client in python:

from openai import OpenAI

messages = [{"role": "user", "content": "What is the weather like in Beijing now?"}]
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Return the temperature of the specified region specified by the user",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "User specified region",
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "temperature unit"
                    },
                },
                "required": ["location"],
            },
        },
    }
]

client = OpenAI(
    # This is the default and can be omitted
    api_key="test",
    base_url="http://localhost:8080/v1/"
)

response =client.chat.completions.create(
    messages=messages,
    tools=tools,
    tool_choice ="auto",
    model="gpt-4",
)
#...

For example, with curl:

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 Beijing now?"}],
  "tools": [
        {
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "Return the temperature of the specified region specified by the user",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "User specified region"
                        },
                        "unit": {
                            "type": "string",
                            "enum": ["celsius", "fahrenheit"],
                            "description": "temperature unit"
                        }
                    },
                    "required": ["location"]
                }
            }
        }
    ],
    "tool_choice":"auto"
}'