model-download-user

name	model-download-user
description	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	hub value	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.

{
  "models": [
    {
      "hub": "ollama",
      "name": "<model-family>",
      "revision": "<tag>"
    }
  ]
}

• hub must be "ollama" (not model_hub, not type)
• name is the base model family: "llama3.2", "mistral", "gemma2" (no tag suffix)
• revision is the tag: "3b", "7b", "latest" (separate field, not model_name)
• Port is always 8200 (not 8080, not 8000)
• Plugin flag: source scripts/run_service.sh up --plugins ollama

Example — download llama3.2:3b:

curl -s -X POST "http://localhost:8200/api/v1/models/download?download_path=ollama-models" \
  -H "Content-Type: application/json" \
  -d '{"models": [{"hub": "ollama", "name": "llama3.2", "revision": "3b"}]}'

Common Mistakes to Avoid

Mistake	Correct
Port 8080 or 8000	Port 8200 always
"model_hub": "ollama"	"hub": "ollama"
"model_name": "llama3.2:3b"	"name": "llama3.2", "revision": "3b"
docker compose up -d	source scripts/run_service.sh up --plugins <list>
Starting without --plugins <hub>	Always activate the plugin for your hub
Polling /api/v1/jobs without job ID	Use the job_ids[0] from the download response

---

Reference Lookup

Read a reference file only when you need the detail it contains:

Reference	When to read
service-setup.md	Starting the service, Docker Compose, plugin flags, env vars
plugins-guide.md	Per-plugin request bodies, parameters, and curl examples
troubleshooting.md	Auth errors, stuck jobs, plugin not activated, venv failures

---

Procedure

Execution Overview

After Step 0 (gather requirements), start the service setup in parallel with composing the API call.

Step 0 (gather requirements — interactive)
  │
  ├──► Step 1 (service setup — may require user action)
  └──► Step 2 (compose API call body — reasoning)
         │
         ├──► Step 3 (submit job + poll status)
         └──► Step 4 (verify result + next steps)

---

Step 0 — Gather Requirements

Extract the following from the user's prompt. If anything is missing, ask before proceeding.

Required	What to look for	Default if absent
Model name	Exact model identifier (e.g. meta-llama/Llama-3.2-1B)	Must ask
Hub	One of: huggingface, openvino, ollama, ultralytics, geti, pipeline-zoo-models, hls	Must ask
Conversion needed?	User says "OVMS", "OpenVINO format", "convert", "is_ovms"	false
Device	CPU / GPU / NPU / HETERO:<dev>[,<dev>...] (e.g. HETERO:GPU,CPU)	CPU
Precision	int4 / int8 / fp16 / fp32	int8 for LLMs; fp16 for others
Model type	llm / vlm / embeddings / rerank / text2speech / speech2text / image_generation / vision / 3d-pose / rppg / ai-ecg	Infer from context

OpenVINO-specific rules (ask only if the user wants OVMS / OpenVINO conversion):

• NPU forces int4 regardless of other settings (applies only to the exact NPU device, not HETERO combinations such as HETERO:NPU,CPU)
• HETERO devices appear in the output path as a filesystem-safe slug: HETERO:GPU,CPU → openvino_models/hetero_gpu_cpu/
• LLM/VLM conversions support cache_size (KV cache in GB) — ask if user mentioned memory constraints
• Embeddings and reranker conversions use text_generation/embeddings_ov/rerank_ov export types internally — these are resolved automatically from type

If the user's prompt explicitly names a model AND hub, go straight to Step 1. Otherwise ask.

---

Step 1 — Service Setup

Read service-setup.md for full details.

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 vars
export HUGGINGFACEHUB_API_TOKEN=<your-hf-token>   # mapped into the container as HF_TOKEN
export 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

Plugin list recommendations:

• HuggingFace only → --plugins huggingface
• HuggingFace + OpenVINO conversion → --plugins huggingface,openvino
• Ollama → --plugins ollama
• Ultralytics → --plugins ultralytics
• All → --plugins all

Confirm the service is healthy before proceeding:

curl http://localhost:8200/api/v1/health
# Expected: {"status": "ok"}

---

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:

{
  "models": [
    {
      "name": "<model-identifier>",
      "hub": "<hub-value>",
      "type": "<model-type-or-omit>",
      "is_ovms": false,
      "config": {}
    }
  ]
}

Key rules:

• is_ovms: true triggers OpenVINO conversion
• Use hub: "openvino" with is_ovms: true and a type field for conversion
• config holds precision, device, cache_size, and plugin-specific params
• download_path query param sets the subdirectory under the model store

---

Step 3 — Submit Job and Poll Status

# 1. Submit download job
JOB_RESPONSE=$(curl -s -X POST \
  "http://localhost:8200/api/v1/models/download?download_path=my-models" \
  -H "Content-Type: application/json" \
  -d '<your-request-body>')

echo "$JOB_RESPONSE"
# Response: {"job_ids": ["<uuid>"]}

# 2. Extract job ID
JOB_ID=$(echo "$JOB_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_ids'][0])")

# 3. Poll until completed or failed
watch -n 5 "curl -s http://localhost:8200/api/v1/jobs/$JOB_ID | python3 -m json.tool"

Job status values: queued → downloading / converting → completed / failed

If status is failed, read the error field and check troubleshooting.md.

---

Step 4 — Verify and Next Steps

# 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

curl -sSLO https://raw.githubusercontent.com/open-edge-platform/edge-ai-libraries/main/microservices/model-download/scripts/get_model.sh
source ./get_model.sh --model-name <model> --hub <hub> --plugins <plugins>