loading-openmed-models

Loading OpenMed Models

OpenMed models download once from the Hugging Face Hub into a local cache, then run fully on-device — no network, no telemetry. This skill covers how to load a model, reuse it across many calls without reloading weights, point at a local copy, and run offline.

When to use

• You are about to run NER repeatedly and want to load the model once.
• You need to control where weights are cached (cache_dir) or force CPU/GPU.
• You must run offline in a locked-down or air-gapped environment.
• You are choosing between a registry key, a full HF id, or a local directory.

For which model to load, see choosing-openmed-models. To actually run it, see extracting-clinical-entities.

Install

pip install "openmed[hf]"   # adds Hugging Face transformers + hub download

The three ways to name a model

analyze_text, extract_pii, load_model, and ModelLoader.load_model all accept the same model_name in three forms:

Form	Example	Notes
Registry key	"disease_detection_superclinical"	Short, resolved via the bundled registry.
Full HF id	"OpenMed/OpenMed-NER-DiseaseDetect-BigMed-278M"	Anything org/name; downloaded from the Hub.
Local path	"/models/my-openmed-ner"	An existing directory; loaded with local_files_only=True.

A bare name without / is prefixed with the default org (OpenMed). An existing local path is detected automatically and never hits the network.

Quick start: load and reuse a loader

The single most important pattern — build one ModelLoader, pass it everywhere. The loader caches models, tokenizers, and pipelines in memory, so the second call is instant.

import openmed
from openmed import ModelLoader, OpenMedConfig

# One loader, reused across calls. Weights load on the first call only.
loader = ModelLoader()

notes = [
    "Patient prescribed 500 mg metformin for type 2 diabetes.",
    "History of myocardial infarction; started on atorvastatin.",
]

for note in notes:
    result = openmed.analyze_text(
        note,
        model_name="disease_detection_superclinical",
        loader=loader,          # <-- reuse; no reload on subsequent calls
        output_format="dict",
    )
    print(result.entities)