Download and convert AI models using the Model Download microservice. Use this skill whenever a user wants to: download a model from HuggingFace, Ollama, Ultralytics, Geti, or Pipeline Zoo; convert a model to OpenVINO IR format for OVMS; download healthcare AI models (3D Pose, rPPG, AI-ECG) via the HLS plugin; set up the model download service; submit a download or conversion job via the REST API; or ask "how do I get model X working with OVMS?". Also trigger on phrases like "download model", "download weights", "convert to int4", "OVMS-ready model", "prepare model for inference".
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Download and convert AI models using the Model Download microservice. Use this skill whenever a user wants to: download a model from HuggingFace, Ollama, Ultralytics, Geti, or Pipeline Zoo; convert a model to OpenVINO IR format for OVMS; download healthcare AI models (3D Pose, rPPG, AI-ECG) via the HLS plugin; set up the model download service; submit a download or conversion job via the REST API; or ask "how do I get model X working with OVMS?". Also trigger on phrases like "download model", "download weights", "convert to int4", "OVMS-ready model", "prepare model for inference".
metadata
{"argument-hint":"Describe the model you want (e.g. \"download Llama-3.2-1B from HuggingFace and convert to OpenVINO INT4 for CPU with OVMS\")\n"}
Model Download Agent
Set up the Model Download microservice and walk the user through downloading
or converting any supported model using the REST API.
Preview: This skill is in preview — share feedback to help improve it.
When to Use
User wants to download a model from HuggingFace, Ollama, Ultralytics, Geti, Pipeline Zoo, or HLS
User wants to convert a HuggingFace model to OpenVINO IR format for OVMS deployment
User asks about model precision conversion (INT4/INT8/FP16/FP32)
User needs to target a specific device (CPU, GPU, NPU, or HETERO combinations like HETERO:GPU,CPU)
User wants to download healthcare AI models (3D Pose, rPPG, AI-ECG)
User is integrating model downloads into a Docker Compose workflow
Supported Hubs at a Glance
Hub
value
hub
What it does
Required env vars
HuggingFace
huggingface
Downloads any public or gated HF model
HUGGINGFACEHUB_API_TOKEN for compose-based startup (gated only)
Ollama
ollama
Downloads Ollama models, runs local Ollama server
—
Ultralytics
ultralytics
Downloads YOLO models, optional INT8 quantization
—
OpenVINO
openvino
Converts HF models to OpenVINO IR for OVMS
HUGGINGFACEHUB_API_TOKEN for compose-based startup (usually needed)
Geti
geti
Downloads trained models from Intel Geti platform
GETI_HOST, GETI_TOKEN, GETI_WORKSPACE_ID
Pipeline Zoo
pipeline-zoo-models
Downloads DL Streamer pipeline-zoo models
—
HLS
hls
Downloads healthcare AI models (3d-pose, rppg, ai-ecg)
—
Ollama Quick-Reference
Always use these exact field names for Ollama requests — the API differs from what
generic model-download documentation implies.
Show the user the service startup command, using only the plugins their request requires:
# Clone (if not already done)
git clone https://github.com/open-edge-platform/edge-ai-libraries.git
cd edge-ai-libraries/microservices/model-download
# Set env varsexport HUGGINGFACEHUB_API_TOKEN=<your-hf-token> # mapped into the container as HF_TOKENexport REGISTRY="intel/"export TAG=latest
# Start service (adjust --plugins to match what you need)source scripts/run_service.sh up --plugins <comma-separated-list> --model-path $PWD/models
Every final answer to the user must restate both the exact startup command (with the
right --plugins list) and the port 8200 — not just the request payload. Users copy
answers piecemeal, so a payload without its startup command or port is easy to misapply.
Step 2 — Compose the API Request
Read plugins-guide.md for the exact request body for each plugin.
The general request shape for POST /api/v1/models/download?download_path=<subdir> is:
# List all completed downloads
curl -s http://localhost:8200/api/v1/models/results | python3 -m json.tool
# Check a specific model's jobs
curl -s "http://localhost:8200/api/v1/models/jobs?model_name=<model-name>" | python3 -m json.tool
After confirming success, tell the user:
The host path where the model was saved (shown in the job result's download_path)
For OVMS conversions: how to mount the model directory into OVMS and which model name to use; the result uses conversion_path
For Ollama: the model is stored inside the container's model store volume
Important accuracy note for OpenVINO conversions: Use hub: "openvino" with is_ovms: true
for model conversion.
Quick alternative: For one-shot, ephemeral container use (CI/CD, scripted workflows), use the get_model.sh one-liner
