Get Started#
The Model Download is a microservice that downloads models from multiple hubs as follows: Hugging Face, Ollama, Geti™ software, and Ultralytics. It supports conversion to OpenVINO™ model server format for Hugging Face models, and exposes a RESTful API for managing model downloads and conversions.
Features#
Downloads models from Hugging Face, Ollama, Geti software, and Ultralytics model hubs
Converts Hugging Face models to OpenVINO model server format
Supports multiple model precisions (INT4,INT8, FP16, and FP32)
Supports various device targets (CPU, GPU, and NPU)
OpenVINO plugin supports NPU model conversion exclusively in INT4 precision.
Supports parallel download
Supports configurable model caching
Exposes a REST API with OpenAPI documentation
Prerequisites#
(Optional) Hugging Face API token, required for gated Hugging Face models or conversion.
Sufficient disk space for model storage.
Quick Start with Setup Script#
Clone the repository:
# Clone the latest on the mainline git clone https://github.com/open-edge-platform/edge-ai-libraries.git edge-ai-libraries # Alternatively, clone a specific release branch git clone https://github.com/open-edge-platform/edge-ai-libraries.git edge-ai-libraries -b <release-tag>
Navigate to the directory:
cd edge-ai-libraries/microservices/model-download
Configure the environment variables:
export REGISTRY="intel/" export TAG=latest export HUGGINGFACEHUB_API_TOKEN=<your-huggingface-token>
To use the Geti™ plugin, set these variables:
export GETI_WORKSPACE_ID=<YOUR_GETI_WORKSPACE_ID> export GETI_HOST=<GETI_HOST_ADDRESS> export GETI_TOKEN=<GETI_ACCESS_TOKEN> export GETI_SERVER_API_VERSION=v1 export GETI_SERVER_SSL_VERIFY=False # Default is FALSE
Note: For Geti™ software setup instructions, see the documentation here.
Launch the service and enable the plugins
source scripts/run_service.sh up --plugins all --model-path <host path>
Note: For public models, no token is needed. Set the Hugging Face token via the
HUGGINGFACEHUB_API_TOKENenvironment variable to download GATED models and for conversion to Openvino IR format.
Note: Ensure the host path does not require privileged access for directory creation. Intel recommends using
$PWD/host_pathor a similar location within your work directory.
The `run_service.sh` script is a Docker Compose wrapper that builds and manages the model download service container with configurable plugins, model paths, and deployment options.
Options available with the script:
__Usage__:
```bash
source scripts/run_service.sh [options] [action]
```
__Actions__:
```text
up Start the services (default)
down Stop the services
```
__Options__:
| Option | Description |
|--------------------------|--------------------------------------------------------------------------------------------------|
| `--build` | Builds the Docker image before running |
| `--rebuild` | This flag instructs to ignore any existing cached images, and rebuild them from scratch using the Dockerfile definitions|
| `--model-path <path>` | Sets the custom model path (default: `$HOME/models/`) |
| `--plugins <list>` | Comma-separated list of plugins to enable (e.g., `huggingface,ollama,openvino,ultralytics, or geti`) or `all` to enable all available plugins |
| `--help` | Shows this help message |
**Examples**:
- Start the service with default settings: `source scripts/run_service.sh up`
- Stop the service: `source scripts/run_service.sh down`
- Enable specific plugins: `source scripts/run_service.sh up --plugins huggingface`
- Enable multiple plugins: `source scripts/run_service.sh up --plugins huggingface,ollama,ultralytics,geti`
- Use a custom model storage: `source scripts/run_service.sh up --model-path /data/my-models`
- Production deployment with all plugins: `source scripts/run_service.sh up --plugins all --model-path tmp/models`
- Display usage information: `source scripts/run_service.sh --help`
Access the service
The service will be available at
http://<host-ip>:8200/api/v1/docs, where you can view the Swagger documentation for the available APIs.
Verification#
Ensure that the application is running by checking the Docker container status:
docker psAccess the application dashboard and verify that it is functioning as expected.
Sample usage with CURL Command#
Download a Hugging Face model:
curl -X POST "http://<host-ip>:8200/api/v1/models/download?download_path=hf_model" \
-H "Content-Type: application/json" \
-d '{
"models": [
{
"name": "microsoft/Phi-3.5-mini-instruct",
"hub": "huggingface",
"type": "llm"
}
],
"parallel_downloads": false
}'
Download an Ollama model:
curl -X POST "http://<host-ip>:8200/api/v1/models/download?download_path=ollama_model" \
-H "Content-Type: application/json" \
-d '{
"models": [
{
"name": "tinyllama",
"hub": "ollama",
"type": "llm"
}
],
"parallel_downloads": false
}'
Download a YOLO vision model from Ultralytics:
curl -X POST "http://<host-ip>:8200/api/v1/models/download?download_path=yolo_model" \
-H "Content-Type: application/json" \
-d '{
"models": [
{
"name": "yolov8s",
"hub": "ultralytics",
"type": "vision"
}
],
"parallel_downloads": true
}'
Note: YOLO vision models from Ultralytics model hub will be downloaded and converted to the OpenVINO IR format with FP32 and FP16 precision by default.
Download a Hugging Face model and convert it to OpenVINO IR format:
curl -X POST "http://<host-ip>:8200/api/v1/models/download?download_path=ovms_model" \
-H "Content-Type: application/json" \
-d '{
"models": [
{
"name": "BAAI/bge-reranker-base",
"hub": "openvino",
"type": "rerank",
"is_ovms": true,
"config": {
"precision": "fp32",
"device": "CPU",
"cache_size": 10
}
}
],
"parallel_downloads": false
}'
Download models from GETI software, which are optimized through OpenVINO toolkit’s optimization tool:
curl -X POST 'http://<host-ip>:8200/api/v1/models/download?download_path=geti_folder' \
-H "Content-Type: application/json" \
-d '{
"models": [
{
"name": "yolox-tiny",
"hub": "geti",
"revision": "1",
"config":{
"precision": "fp32"
}
}
],
"parallel_downloads": true
}'
Note: The default precision is FP16.
Query Parameter:
download_path(string): Specify a local filesystem path for saving the downloaded model. If not provided, the model will be saved to the default location.
Response: Sample Response (when a download request is started):
{
"message": "Started processing 1 model(s)",
"job_ids": [
"5f0d4eba-c79c-4d02-97a6-43c3d0168ca0"
],
"status": "processing"
}
Each model-download request returns a job_id. To check the status of a download:
curl -X GET "http://<host-ip>:8200/api/v1/jobs/<job_id>"
Sample Response (when the job is completed):
{
"id": "5f0d4eba-c79c-4d02-97a6-43c3d0168ca0",
"operation_type": "download",
"model_name": "yolov8s",
"hub": "ultralytics",
"output_dir": "/opt/models/ultra_folder",
"status": "completed",
"start_time": "2025-10-27T08:24:23.510870",
"plugin_name": "ultralytics",
"model_type":"vision",
"plugin": "ultralytics",
"completion_time": "2025-10-27T08:30:14.443898",
"result": {
"model_name": "yolov8s",
"source": "ultralytics",
"download_path": "model/download/path",
"return_code": 0
}
}
For details, see the API Spec
Configuration#
You can configure the service through environment variables and Docker volumes:
Environment Variables:
HF_HUB_ENABLE_HF_TRANSFER: Enable Hugging Face transfer (default: 1)HUGGINGFACEHUB_API_TOKEN: Hugging Face token (only required for gated models or conversion)
Volumes:
~/models:/app/models: Persist downloaded models
Troubleshooting#
If you encounter any issues during the build or run process, check the Docker logs for errors:
docker logs <container-id>
Best Practices#
Use parallel downloads with caution because they can consume significant resources.
Configure cache sizes based on available memory.
Select model precision according to your performance requirements.
Use appropriate model types and configurations for OpenVINO model server conversion.
Run in Kubernetes Cluster#
See Deploy with Helm Chart for details. Address the prerequisites mentioned on this page before deploying with Helm chart.
Learn More#
For alternative ways to set up the sample application, see: