Edit

winml catalog

Browse the curated Windows ML CLI catalog of validated models and benchmarks.

When to use this

Use winml catalog to discover which HuggingFace models have been validated end-to-end by the Windows ML CLI team — exported, quantized, compiled, and benchmarked on real Windows ML devices. It is the starting point when you want a model that is known to work before investing time in a custom build.

Synopsis

winml catalog [options]

Flags

Flag Short Type Default Description
--model-type string null Filter the catalog by model architecture (case-insensitive). Examples: bert, roberta, vit.
--task -t string null Filter by HuggingFace task (case-insensitive). Examples: text-classification, image-segmentation.
--ep/--execution-provider string null Filter by execution provider (e.g., qnn, dml). If not specified, shows all EPs.
--device -d string null Filter by target device (e.g., npu, gpu). If not specified, shows all devices.
--output -o path null Save the displayed results to a JSON file.
--help -h flag — Show help and exit.

winml catalog reads a local catalog bundled with the package — no network access is required.

How it works

The catalog is stored in winml/modelkit/data/hub_models.json and is loaded directly from the installed package data without any network call. Each catalog entry records the model ID, task, architecture type, and model size. Use --model-type, --task, --ep, or --device to narrow the displayed list. When --output is provided, the filtered results are written as indented JSON to the specified path.

Examples

The catalog is a discovery aid, not a guarantee that every listed model works with every installed EP or precision. Filter by task and target, then inspect and validate the selected model. Use --format json for stdout or --output to save a JSON file; add --overwrite only when intentionally replacing an existing file.

# List all validated models in the catalog
winml catalog

+--- winml-cli Catalog  |  12 validated model(s) --------------------------+
|  Model                             Task                    Model Type     |
|  microsoft/resnet-50              image-classification    resnet          |
|  bert-base-uncased                fill-mask               bert            |
|  ProsusAI/finbert                 text-classification     bert            |
|  ...                                                                      |
+---------------------------------------------------------------------------+
Use  --ep  or  --device  to filter by execution provider or target device.

# Filter to BERT-family models only
winml catalog --model-type bert

# Filter by task — show only text-classification models
winml catalog --task text-classification

# Combine filters — BERT models for text classification
winml catalog --model-type bert --task text-classification

# Save filtered results to JSON for offline review
winml catalog --task image-classification --output results/image_catalog.json

Common pitfalls

  • The catalog reflects a point-in-time snapshot. Models listed in the catalog were validated against a specific version of Windows ML CLI, ONNX Runtime, and the relevant EP driver. Accuracy and latency may differ on your hardware or with updated drivers.
  • --output only saves what was displayed. Combining a filter with --output saves the filtered list. There is no flag to dump the entire catalog in one call — omit all filters and add --output to do so.
  • A model not in the catalog can still be used with Windows ML CLI. The catalog covers tested models; winml inspect and winml export work with any HuggingFace model that has a supported architecture, whether or not it appears in the catalog.

See also

  • inspect.md — check loader, exporter, and task detection for any HuggingFace model ID
  • sys.md — verify your environment and EP availability before building
  • How Windows ML CLI Works — pipeline overview from export to benchmark
  • Quantization & QDQ — understand quantization concepts and precision options
  • Last updated on