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Model Registry

OpenMed ships a manifest-backed registry (openmed.core.model_registry.OPENMED_MODELS) that annotates every official checkpoint with metadata such as category, specialization, recommended confidence, Hugging Face IDs, device fit, and benchmark summaries. Use it to pick the right model, surface dropdowns in UIs, or validate incoming requests.

Exploring the registry

from openmed.core.model_registry import (
    get_all_models,
    list_model_categories,
    get_models_by_category,
    get_model_info,
    get_model_suggestions,
)

print(list_model_categories())

oncology_models = get_models_by_category("Oncology")
for info in oncology_models:
    print(info.display_name, info.model_id, info.recommended_confidence)

info = get_model_info("disease_detection_superclinical")
print(info.description, info.entity_types)

suggestions = get_model_suggestions("Metastatic breast cancer on paclitaxel.")
for model_key, info, reason in suggestions:
    print(model_key, info.display_name, reason)
  • ModelInfo objects include display_name, category, entity_types, size hints, benchmark data, optional latency/RAM maps, optional recommended tier, and a default confidence threshold.
  • get_model_suggestions leans on lightweight heuristics to recommend models based on text snippets or hints (disease, pharma, oncology, etc.).

Metadata for UIs & validation

  • ModelInfo.size_category, .size_mb, .latency_ms, .peak_ram_mb, and .recommended_tier help you decide whether a model can fit on CPU-only infrastructure or a target device tier.
  • entity_types feed dropdowns or filter chips in your frontend.
  • recommended_confidence can drive slider defaults or guardrails on API calls (pass it to analyze_text).

Keeping the registry fresh

If you add a new Hugging Face checkpoint, refresh models.jsonl and, when measurements are available, enrich it with benchmark and device-fit results. See Model Manifest for the schema and merge command.

Manifest rows drive openmed/core/model_registry.py; avoid hand-editing the registry for new models. A new row should include:

  1. The full HF model id (OpenMed/...) and core release metadata.
  2. Representative canonical entity labels.
  3. Optional benchmark, latency, RAM, and recommended-tier enrichment.

CI will enforce type safety through the unit tests, and the docs automatically pick up the new entry via the examples above.