Text Generation
PEFT
Safetensors
custom
medical
nail-disease
medgemma
lora
clinical-assessment
conversational
Instructions to use isumenuka/medgemma-4b-nail-clinical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use isumenuka/medgemma-4b-nail-clinical with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/medgemma-4b-it") model = PeftModel.from_pretrained(base_model, "isumenuka/medgemma-4b-nail-clinical") - Notebooks
- Google Colab
- Kaggle
MedGemma-4B Nail Disease Clinical Assessment (SFT LoRA)
Fine-tuned from google/medgemma-4b-it using QLoRA (SFT phase).
Task
Given a nail disease diagnosis + patient info โ outputs structured clinical JSON:
medical_priority: Low / Medium / Highfollow_up_required: No / Yescare_type: Nail Care / Topical Treatment / Specialist Evaluation / Nonenext_step: Awareness / Monitor Condition / Schedule Doctor Visitworry_score: 1โ10clinical_notes: brief reasoning
Deploy on HuggingFace Inference Endpoints
This repo contains a custom handler.py โ no AutoConfig needed.
Required env-var on the endpoint:
HUGGING_FACE_HUB_TOKEN = hf_xxxxxxxxxxxx
Input:
{"inputs": "{"disease_name": "Psoriasis", "age": 45, "gender": "Female", "nail_visual_features": "pitting and discolouration", "medical_history": "None"}"}
Output:
{"generated_text": "...", "parsed": {"medical_priority": "Medium", ...}}
Disclaimer
Research use only. Not for clinical diagnosis.
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