loading-openmed-models
Load OpenMed clinical/biomedical NER models from the Hugging Face Hub or a local path and reuse them efficiently across calls. Use when the user wants to load an OpenMed model, control the model cache, run fully offline after a one-time download, reuse a ModelLoader to avoid reloading, set a cache_dir or device, or pick between a registry key, a full Hugging Face id, and a local directory. Pairs with choosing-openmed-models (pick the model) and extracting-clinical-entities (run it).
npx skills add maziyarpanahi/openmed --skill loading-openmed-models --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ```bash 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"` | Anythi
- When to use
- Install
- The three ways to name a model
- Quick start: load and reuse a loader
- Load weights directly
- Configure the cache, device, and org
- First-run download, then fully offline
- Check a model's maximum sequence length
- Free memory when done
- Hand-off to / from OpenMed
- Edge cases & gotchas
- Standards & references
pip install "openmed[hf]" # adds Hugging Face transformers + hub download export HF_HUB_OFFLINE=1 export TRANSFORMERS_OFFLINE=1
What does the loading-openmed-models skill do?
Load OpenMed clinical/biomedical NER models from the Hugging Face Hub or a local path and reuse them efficiently across calls. Use when the user wants to load an OpenMed model, control the model cache, run fully offline after a one-time download, reuse a ModelLoader to avoid reloading, set a cache_dir or device, or pick between a registry key, a full Hugging Face id, and a local directory. Pairs with choosing-openmed-models (pick the model) and extracting-clinical-entities (run it).
How do I install it?
Run `npx skills add maziyarpanahi/openmed --skill loading-openmed-models --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.