metadata
license: mit
base_model: google/medgemma-1.5-4b-it
tags:
- medical
- clinical-nlp
- lora
- peft
- hallucination-detection
- adversarial-training
- agentic-ai
language:
- en
MedBrainSquad — Auditor Agent Adapter
LoRA fine-tuned adapter for MedGemma 1.5 4B, trained via adversarial Supervised Fine-Tuning (SFT) to detect hallucinations, flag clinical inconsistencies, and validate SOAP JSON output from the Scribe agent.
Project
Part of the MedBrainSquad Clinical Safety Net — a three-tier adversarial multi-agent pipeline for safe, hallucination-resistant clinical documentation.
🔗 Full project: github.com/aydiny/medbrain-squad 🔗 Scribe Adapter: aydin237/medbrain-squad-scribe
Training Details
- Base model: google/medgemma-1.5-4b-it
- Method: LoRA (Rank 16, Alpha 32)
- Training approach: Adversarial SFT on synthetic hallucinated/corrupted SOAP examples
- Dataset: 500-row adversarial dataset with deliberate hallucinations and clinical errors
- Hardware: Dual NVIDIA T4 (Kaggle)
- Framework: HuggingFace PEFT + TRL
Intended Use
Research and development of adversarial safety layers in clinical NLP pipelines. Demonstrates hallucination detection in regulated healthcare environments (NHS/EU GDPR).
⚠️ Limitations & Disclaimer
- Research prototype — not validated for clinical deployment
- Base model (MedGemma 1.5 4B) subject to Google HAID Terms
- Auditor itself may miss hallucinations in novel edge cases
- Trained on synthetic data only