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license: apache-2.0
base_model: OpenMed/OpenMed-ZeroShot-NER-DNA-Large-459M
pipeline_tag: token-classification
library_name: openmed
tags:
- openmed
- mlx
- apple-silicon
- zero-shot-ner
- gliner
- medical
- clinical
---
# OpenMed-ZeroShot-NER-DNA-Large-459M for OpenMed MLX
This repository contains an OpenMed MLX conversion of [`OpenMed/OpenMed-ZeroShot-NER-DNA-Large-459M`](https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-DNA-Large-459M) for Apple Silicon inference with [OpenMed](https://github.com/maziyarpanahi/openmed).
Artifact metadata:
- OpenMed MLX task: `zero-shot-ner`
- OpenMed MLX family: `gliner-uni-encoder-span`
- Weight format: `safetensors`
- Runtime API: `GLiNERMLXPipeline`
## OpenMed MLX Status
- MLX rollout: refreshed for public access on 2026-06-23
- Hub artifact: OpenMed MLX repository
- Source checkpoint: [`OpenMed/OpenMed-ZeroShot-NER-DNA-Large-459M`](https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-DNA-Large-459M)
- Collection: [OpenMed Medical MLX Models](https://huggingface.co/collections/OpenMed/medical-mlx-models)
- Runtime: OpenMed Python MLX backend on Apple Silicon
- Artifact layout: `config.json`, `id2label.json`, `openmed-mlx.json`, MLX weights, and tokenizer assets
## Use This MLX Snapshot
Download this OpenMed MLX artifact directly from the Hub:
```bash
hf download OpenMed/OpenMed-ZeroShot-NER-DNA-Large-459M-mlx --local-dir ./OpenMed-ZeroShot-NER-DNA-Large-459M-mlx
```
Use the downloaded directory when you want to pin this exact MLX artifact in an offline or local Apple Silicon workflow.
## Quick Start
```bash
pip install openmed
pip install "openmed[mlx]"
```
```python
from huggingface_hub import snapshot_download
from openmed.mlx.inference import GLiNERMLXPipeline
model_path = snapshot_download("OpenMed/OpenMed-ZeroShot-NER-DNA-Large-459M-mlx")
pipe = GLiNERMLXPipeline(model_path)
entities = pipe.predict_entities(
"Patient John Doe was seen at Stanford Hospital.",
labels=["person", "organization", "location"],
threshold=0.5,
)
for entity in entities:
print(entity)
```
Prompt packing metadata included with the model:
```json
{
"kind": "gliner-words",
"entity_token": "<<ENT>>",
"separator_token": "<<SEP>>",
"class_token_index": 128002,
"embed_marker_token": true,
"split_mode": "words"
}
```
## Swift and Apple Apps
Use Swift with OpenMedKit, not with MLX weight files directly.
1. Open Xcode and go to File > Add Package Dependencies.
2. Paste the OpenMed repository URL: `https://github.com/maziyarpanahi/openmed`
3. Choose the package product OpenMedKit from the repository.
4. Add a compatible CoreML model bundle plus `id2label.json` to your app target.
This MLX model is for Python services on Apple Silicon, local MLX inference on macOS, and Hub-hosted model distribution. If a given environment cannot write `weights.safetensors`, OpenMed falls back to `weights.npz` so the model remains usable.
## Credits
- Base checkpoint: [`OpenMed/OpenMed-ZeroShot-NER-DNA-Large-459M`](https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-DNA-Large-459M)
- OpenMed GitHub: [https://github.com/maziyarpanahi/openmed](https://github.com/maziyarpanahi/openmed)
- OpenMed website: [https://openmed.life](https://openmed.life)
- MLX conversion and runtime support: OpenMed
- Swift runtime for Apple apps: OpenMedKit from the OpenMed repository
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