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MaziyarPanahi 
posted an update 2 days ago
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1053
Announcing: OpenMed Multilingual PII Detection Models

Today I am releasing 105 open-source models for Personally Identifiable Information (PII) detection in French, German, and Italian.

All Apache 2.0 licensed. Free for commercial use. No restrictions.

Performance:

- French: 97.97% F1 (top model)
- German: 97.61% F1 (top model)
- Italian: 97.28% F1 (top model)

All top-10 models per language exceed 96% F1

Coverage:

55+ PII entity types per language
Native ID formats: NSS (French), Sozialversicherungsnummer (German), Codice Fiscale (Italian)
Language-specific address, phone, and name patterns

Training Data:

French: 49,580 samples
German: 42,250 samples
Italian: 40,944 samples

Why Multilingual?

European healthcare operates in European languages. Clinical notes, patient records, and medical documents are generated in French, German, Italian, and other languages.

Effective de-identification requires:

- Native language understanding — not translation
- Local ID format recognition — each country has unique patterns
- Cultural context awareness — names, addresses, and formats vary
- These models deliver production-ready accuracy without requiring data to leave your infrastructure or language.

HIPAA & GDPR Compliance
Built for US and European privacy regulations:

- On-premise deployment: Process data locally with zero external dependencies
- Data sovereignty: No API calls, no cloud services, no cross-border transfers
- Air-gapped capable: Deploy in fully isolated environments if required
- Regulatory-grade accuracy: Supporting Expert Determination standards
- HIPAA and GDPR compliance across languages, without compliance gaps.

Use Cases
- Hospital EHR systems: Automated patient record de-identification
- Clinical research: Multilingual dataset preparation for studies
- Insurance companies: Claims processing across

https://huggingface.co/collections/OpenMed/multilingual-pii-and-de-identification
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MaziyarPanahi 
posted an update 5 days ago
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1158
From Golden Gate Bridge to Broken JSON: Why Anthropic's SAE Steering Fails for Structured Output

I ran 6 experiments trying to use Anthropic's SAE steering for JSON generation.

- Base model: 86.8% valid JSON
- Steering only: 24.4%
- Fine-tuned: 96.6%
- FSM constrained: 100%

Steering is for semantics, not syntax.

https://huggingface.co/blog/MaziyarPanahi/sae-steering-json
MaziyarPanahi 
posted an update 6 days ago
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3883
🚨 Day 8/8: OpenMed Medical Reasoning Dataset Release - THE GRAND FINALE

Today I complete my 8-day release series with Medical-Reasoning-SFT-Mega.
The largest open medical reasoning dataset, combining 7 state-of-the-art AI models with fair distribution deduplication.

THE 7 SOURCE MODELS (Original Sample Counts):

1. Trinity-Mini: 810,284 samples
2. Qwen3-Next-80B: 604,249 samples
3. GPT-OSS-120B: 506,150 samples
4. Nemotron-Nano-30B: 444,544 samples
5. GLM-4.5-Air: 225,179 samples
6. MiniMax-M2.1: 204,773 samples
7. Baichuan-M3-235B: 124,520 samples

TOTAL BEFORE DEDUPLICATION: 2,919,699 samples

TOKEN COUNTS:
- Content tokens: 2.22 Billion
- Reasoning tokens: 1.56 Billion
- Total tokens: 3.78 Billion
- Samples with chain-of-thought: 100%

Quick Start:
from datasets import load_dataset
ds = load_dataset("OpenMed/Medical-Reasoning-SFT-Mega")


All datasets Apache 2.0 licensed. Free for research and commercial use.

Thank you for following OpenMed's release series. I can't wait to see what you build. 🔥

OpenMed/Medical-Reasoning-SFT-Mega
OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B-V2
OpenMed/Medical-Reasoning-SFT-Trinity-Mini
OpenMed/Medical-Reasoning-SFT-GLM_4.5_Air
OpenMed/Medical-Reasoning-SFT-MiniMax-M2.1
OpenMed/Medical-Reasoning-SFT-Qwen3-Next-80B
OpenMed/Medical-Reasoning-SFT-Nemotron-Nano-30B
https://huggingface.co/datasets/OpenMed/Medical-Reasonin

https://huggingface.co/collections/OpenMed/medical-datasets
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