Source checkpoint: OpenMed/OpenMed-PII-Vietnamese-SuperClinical-Base-184M-v1
Model family: deberta-v2 (DebertaV2ForTokenClassification)
Primary language hint: English (en)
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

OpenMed MLX Status

MLX rollout: refreshed for public access on 2026-06-23
Hub artifact: OpenMed MLX repository
Source checkpoint: OpenMed/OpenMed-PII-Vietnamese-SuperClinical-Base-184M-v1
Collection: 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:

hf download OpenMed/OpenMed-PII-Vietnamese-SuperClinical-Base-184M-v1-mlx --local-dir ./OpenMed-PII-Vietnamese-SuperClinical-Base-184M-v1-mlx

Use the downloaded directory when you want to pin this exact MLX artifact in an offline or local Apple Silicon workflow.

Python Quick Start

Use the standard OpenMed API if you want OpenMed to choose the right runtime automatically:

pip install "openmed[mlx]"

python

from openmed import extract_pii

text = "<your clinical note here>"
result = extract_pii(
    text,
    model_name="OpenMed/OpenMed-PII-Vietnamese-SuperClinical-Base-184M-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:

bash

pip install "openmed[mlx]"
hf download OpenMed/OpenMed-PII-Vietnamese-SuperClinical-Base-184M-v1-mlx --local-dir ./OpenMed-PII-Vietnamese-SuperClinical-Base-184M-v1-mlx

python

from openmed import extract_pii
from openmed.core import OpenMedConfig

text = "<your clinical note here>"
result = extract_pii(
    text,
    model_name="./OpenMed-PII-Vietnamese-SuperClinical-Base-184M-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.json
id2label.json
MLX weight files (weights.safetensors and/or weights.npz)

Tokenizer assets are bundled in this repo.

OpenMed/OpenMed-PII-Vietnamese-SuperClinical-Base-184M-v1-mlx

OpenMed-PII-Vietnamese-SuperClinical-Base-184M-v1 for OpenMed MLX

At a Glance