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