Medical MLX Models
Collection
A collection of MLX medical models you can run on your iPhone and macOS • 650 items • Updated • 10
How to use OpenMed/OpenMed-NER-PathologyDetect-BioMed-109M-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir OpenMed-NER-PathologyDetect-BioMed-109M-mlx OpenMed/OpenMed-NER-PathologyDetect-BioMed-109M-mlx
This repository contains an OpenMed MLX conversion of OpenMed/OpenMed-NER-PathologyDetect-BioMed-109M for Apple Silicon inference with OpenMed.
Artifact metadata:
token-classificationbertsafetensorsOpenMed MLX token-classification backendOpenMed/OpenMed-NER-PathologyDetect-BioMed-109Mconfig.json, id2label.json, openmed-mlx.json, MLX weights, and tokenizer assetsDownload this OpenMed MLX artifact directly from the Hub:
hf download OpenMed/OpenMed-NER-PathologyDetect-BioMed-109M-mlx --local-dir ./OpenMed-NER-PathologyDetect-BioMed-109M-mlx
Use the downloaded directory when you want to pin this exact MLX artifact in an offline or local Apple Silicon workflow.
pip install openmed
pip install "openmed[mlx]"
from openmed import analyze_text
from openmed.core.config import OpenMedConfig
result = analyze_text(
"Patient John Doe, DOB 1990-05-15, SSN 123-45-6789",
model_name="OpenMed/OpenMed-NER-PathologyDetect-BioMed-109M",
config=OpenMedConfig(backend="mlx"),
)
for entity in result.entities:
print(entity.label, entity.text, round(entity.confidence, 4))
Use Swift with OpenMedKit, not with MLX weight files directly.
https://github.com/maziyarpanahi/openmedid2label.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.
OpenMed/OpenMed-NER-PathologyDetect-BioMed-109MQuantized