Instructions to use OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir OpenMed-PII-Turkish-SuperClinical-Large-434M-v1-mlx OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
OpenMed-PII-Turkish-SuperClinical-Large-434M-v1 for OpenMed MLX
This repository contains an MLX packaging of OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1 for Apple Silicon inference with OpenMed.
At a Glance
- Source checkpoint:
OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1 - Model family:
deberta-v2(DebertaV2ForTokenClassification) - Primary language hint: Turkish (
tr) - Artifact layout: legacy-compatible MLX (
config.json,id2label.json, MLX weight files) - Weight format:
safetensors - Python MLX: supported through
openmed[mlx]on Apple Silicon Macs
Python Quick Start
Use the standard OpenMed API if you want OpenMed to choose the right runtime automatically:
pip install "openmed[mlx]"
from openmed import extract_pii
text = "<your clinical note here>"
result = extract_pii(
text,
model_name="OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1",
use_smart_merging=True,
)
for entity in result.entities:
print(entity.label, entity.text, round(entity.confidence, 4))
On Apple Silicon, OpenMed can use this preconverted MLX artifact when openmed[mlx] is installed. On other systems, OpenMed falls back to the Hugging Face / PyTorch backend.
Use This Preconverted MLX Repo Directly
If you want to use this MLX snapshot explicitly, download it locally and point OpenMed at the directory:
pip install "openmed[mlx]"
hf download OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1-mlx --local-dir ./OpenMed-PII-Turkish-SuperClinical-Large-434M-v1-mlx
If this repo is private in your environment, authenticate first with hf auth login or set HF_TOKEN.
from openmed import extract_pii
from openmed.core import OpenMedConfig
text = "<your clinical note here>"
result = extract_pii(
text,
model_name="./OpenMed-PII-Turkish-SuperClinical-Large-434M-v1-mlx",
config=OpenMedConfig(backend="mlx"),
use_smart_merging=True,
)
print(result.entities)
Swift Status
This repo is based on deberta-v2. Python MLX supports this artifact today, but the current public OpenMedKit Swift MLX rollout is limited to bert, distilbert, roberta, xlm-roberta, and electra.
If you are building an Apple app today, the recommended paths for this model are:
- Python MLX for evaluation or local workflows on Apple Silicon
- CoreML in OpenMedKit if you already have a compatible bundled Apple export
- Track the current Swift support matrix in the OpenMedKit docs
Artifact Notes
This repo uses the current legacy-compatible MLX layout:
config.jsonid2label.json- MLX weight files (
weights.safetensorsand/orweights.npz)
Tokenizer assets are bundled in this repo.
Links
- Source checkpoint:
OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1 - OpenMed GitHub: https://github.com/maziyarpanahi/openmed
- MLX backend docs: https://openmed.life/docs/mlx-backend/
- OpenMedKit docs: https://openmed.life/docs/swift-openmedkit/
- Downloads last month
- 11
Quantized
Model tree for OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1-mlx
Base model
microsoft/deberta-v3-large