🧠
Openmed Pii Telugu Clinicale5 Small 33m V1 Mlx model by OpenMed
⭐ 37.7
🧠
Model

Openmed Pii Telugu Clinicale5 Small 33m V1 Mlx

by OpenMed hf-model--openmed--openmed-pii-telugu-clinicale5-small-33m-v1-mlx
Nexus Index
37.7 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 3
R: Recency 97
Q: Quality 50
Tech Context
Vital Performance
36 DL / 30D
0.0%
Source →
Audited 37.7 FNI Score
Tiny - Params
- Context
36 Downloads
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID hf-model--openmed--openmed-pii-telugu-clinicale5-small-33m-v1-mlx
License Apache-2.0
Provider huggingface
📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__openmed__openmed_pii_telugu_clinicale5_small_33m_v1_mlx,
  author = {OpenMed},
  title = {Openmed Pii Telugu Clinicale5 Small 33m V1 Mlx Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/openmed/openmed-pii-telugu-clinicale5-small-33m-v1-mlx}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
OpenMed. (2026). Openmed Pii Telugu Clinicale5 Small 33m V1 Mlx [Model]. Free2AITools. https://huggingface.co/openmed/openmed-pii-telugu-clinicale5-small-33m-v1-mlx

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download openmed/openmed-pii-telugu-clinicale5-small-33m-v1-mlx

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
37.7
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 3
Recency (R) 97
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for Openmed Pii Telugu Clinicale5 Small 33m V1 Mlx: Semantic (S:50), Authority (A:0), Popularity (P:3), Recency (R:97), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
---

🚀 What's Next?

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⚡

Deployment Guide

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Technical Deep Dive

OpenMed-PII-Telugu-ClinicalE5-Small-33M-v1 for OpenMed MLX

This repository contains an MLX packaging of OpenMed/OpenMed-PII-Telugu-ClinicalE5-Small-33M-v1 for Apple Silicon inference with OpenMed.

At a Glance

  • Source checkpoint: OpenMed/OpenMed-PII-Telugu-ClinicalE5-Small-33M-v1
  • Model family: bert (BertForTokenClassification)
  • Primary language hint: Telugu (te)
  • Artifact layout: legacy-compatible MLX (config.json, id2label.json, MLX weight files)
  • Python MLX: supported through openmed[mlx] on Apple Silicon Macs
  • Swift MLX: supported today in OpenMedKit on Apple Silicon macOS and real iPhone/iPad hardware

Python Quick Start

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

bash 
pip install "openmed[mlx]"
python 
from openmed import extract_pii

text = ""
result = extract_pii(
    text,
    model_name="OpenMed/OpenMed-PII-Telugu-ClinicalE5-Small-33M-v1",
    lang="te",
    use_smart_merging=True,
)

for entity in result.entities:
    print(entity.label, entity.text, round(entity.confidence, 4))

On Apple Silicon, OpenMed auto-selects MLX when openmed[mlx] is installed. On other systems it 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-Telugu-ClinicalE5-Small-33M-v1-mlx --local-dir ./OpenMed-PII-Telugu-ClinicalE5-Small-33M-v1-mlx

If this repo is still private in your environment, authenticate first with hf auth login or set HF_TOKEN.

python 
from openmed import extract_pii
from openmed.core import OpenMedConfig

text = ""
result = extract_pii(
    text,
    model_name="./OpenMed-PII-Telugu-ClinicalE5-Small-33M-v1-mlx",
    config=OpenMedConfig(backend="mlx"),
    lang="te",
    use_smart_merging=True,
)

print(result.entities)

Swift Quick Start with OpenMedKit

OpenMedKit can download and run this MLX repo directly.

swift 
import OpenMedKit

let modelDirectory = try await OpenMedModelStore.downloadMLXModel(
    repoID: "OpenMed/OpenMed-PII-Telugu-ClinicalE5-Small-33M-v1-mlx",
    authToken: ""
)

let openmed = try OpenMed(
    backend: .mlx(modelDirectoryURL: modelDirectory)
)

let entities = try openmed.extractPII(
    ""
)

Notes:

  • Swift MLX targets Apple Silicon macOS and physical iPhone/iPad hardware.
  • iOS Simulator is not a Swift MLX target.
  • If you already have a bundled .mlmodelc or .mlpackage, use the CoreML backend in OpenMedKit instead.

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 not bundled in this repo layout. OpenMed and OpenMedKit keep backward compatibility by falling back to the source tokenizer reference in config.json when needed.

⚠ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source →

📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
36Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--openmed--openmed-pii-telugu-clinicale5-small-33m-v1-mlx
slug
openmed--openmed-pii-telugu-clinicale5-small-33m-v1-mlx
source
huggingface
author
OpenMed
license
Apache-2.0
tags
openmed, bert, mlx, apple-silicon, token-classification, pii, de-identification, medical, clinical, license:apache-2.0, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
token-classification

📊 Engagement & Metrics

downloads
36
stars
0
forks
0

Data indexed from public sources. Updated daily.