LocalAI/docs/content/getting-started/models.md

+++ disableToc = false title = "Setting Up Models" weight = 3 icon = "hub" description = "Learn how to install, configure, and manage models in LocalAI" +++

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]({{% relref "getting-started/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. Progress appears in the strip at the top of the app, and Operate → Activity shows every install in flight, plus what failed and what finished (see [Activity]({{% relref "operations/activity" %}}))

For more details, refer to the [Gallery Documentation]({{% relref "features/model-gallery" %}}).

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]({{% relref "features/model-gallery" %}}).

Browse Online

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

Method 1.5: Import Models via WebUI

The WebUI import page takes either a source to resolve or a configuration to write. Both live on the same page, behind the two tabs in its header.

From a source

1. Open the LocalAI WebUI at http://localhost:8080
2. Click "Import Model"
3. Paste the source into the Source field (e.g. https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF)
4. Press Enter, or click Import

The What you can paste panel beside the field lists every accepted scheme: huggingface://, hf://, a full Hugging Face URL, any direct https:// URL, file:// and absolute paths on the host, oci://, ocifile://, and ollama://.

Expanding Import options reveals everything you can override before the import runs: backend, name, description, quantizations, MMProj quantizations, model type, embeddings support, the diffusers-specific fields, and arbitrary custom key-value preferences. The backend list can be narrowed by modality first. Fields that the selected backend cannot use are hidden, and anything you typed into them is kept in case you switch back.

Leaving the backend on auto-detect lets LocalAI choose from the source. If more than one installed backend can serve the detected modality, the page says so and offers the candidates inline — picking one resubmits the import.

Repositories under mlx-community are imported with the native MLX backend. LocalAI uses Hugging Face's pipeline metadata to select mlx-vlm for vision-language models and mlx-audio for text-to-speech models; other MLX repositories use mlx. An explicit backend selection in the import form always overrides this automatic routing.

Once the import starts, the page reports the current phase, the bytes transferred and a progress bar until the model is ready.

Writing YAML

For full control over model configuration, switch to the Write YAML tab and edit the configuration directly, then click Create. The editor provides syntax highlighting and a copy button, and accepts the same configuration keys documented under [Advanced]({{% relref "advanced" %}}).

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

{{% notice note %}} On every model download — Ollama and OCI registries, the model gallery, and plain HTTP(S) file URLs alike — LocalAI identifies itself with a LocalAI/<version> (<os>; <arch>) User-Agent header (for example LocalAI/v3.2.1 (linux; amd64)) so registry and gallery operators can attribute usage to LocalAI. Builds from source that carry no stamped version send LocalAI (<os>; <arch>) instead. {{% /notice %}}

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 (absolute path to a file within your models directory)
• 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

{{% notice note %}} When using file:// URLs, the path must point to a file within your models directory (specified by MODELS_PATH). Files outside this directory are rejected for security reasons. {{% /notice %}}

Configuration files can be used to customize the model defaults and settings. For advanced configurations, refer to the [Customize Models section]({{% relref "getting-started/customize-model" %}}).

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]({{% relref "advanced" %}}).

Step 3: Run LocalAI

Choose one of the following methods to run LocalAI:

{{< tabs >}} {{% tab title="Docker" %}}

mkdir models

cp your-model.gguf models/

docker run -p 8080:8080 -v $PWD/models:/models -ti --rm localai/localai: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
   }'

{{% notice tip %}} Other Docker Images:

For other Docker images, please refer to the table in [the container images section]({{% relref "getting-started/containers" %}}). {{% /notice %}}

Example:

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 localai/localai: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
   }'

{{% notice note %}}

• If running on Apple Silicon (ARM), it is not recommended to run on Docker due to emulation. Follow the [build instructions]({{% relref "getting-started/build" %}}) to use Metal acceleration for full GPU support.
• If you are running on Apple x86_64, you can use Docker without additional gain from building it from source. {{% /notice %}}

{{% /tab %}} {{% tab title="Docker Compose" %}}

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
   }'

{{% notice tip %}} Other Docker Images:

For other Docker images, please refer to the table in Getting Started. {{% /notice %}}

Note: If you are on Windows, ensure the project is on the Linux filesystem to avoid slow model loading. For more information, see the Microsoft Docs.

{{% /tab %}} {{% tab title="Kubernetes" %}}

For Kubernetes deployment, see the [Kubernetes installation guide]({{% relref "getting-started/kubernetes" %}}).

{{% /tab %}} {{% tab title="From Binary" %}}

LocalAI binary releases are available on GitHub.

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

{{% notice tip %}} If installing on macOS, you might encounter a message saying:

"local-ai-git-Darwin-arm64" (or the name you gave the binary) can't be opened because Apple cannot check it for malicious software.

Hit OK, then go to Settings > Privacy & Security > Security and look for the message:

"local-ai-git-Darwin-arm64" was blocked from use because it is not from an identified developer.

Press "Allow Anyway." {{% /notice %}}

{{% /tab %}} {{% tab title="From Source" %}}

For instructions on building LocalAI from source, see the [Build from Source guide]({{% relref "getting-started/build" %}}).

{{% /tab %}} {{< /tabs >}}

GPU Acceleration

For instructions on GPU acceleration, visit the [GPU Acceleration]({{% relref "features/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):

Quantization	Size	Quality	Use Case
Q8_0	Largest	Highest	Best quality, requires more RAM
Q6_K	Large	Very High	High quality
Q4_K_M	Medium	High	Balanced (recommended)
Q4_K_S	Small	Medium	Lower RAM usage
Q2_K	Smallest	Lower	Minimal RAM, lower quality

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]({{% relref "advanced/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]({{% relref "reference/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