Sadou/medgemma-4b-it-medical-report-simplifier

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How to Get Started with the Model

Use the code below to get started with the model.

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Framework versions

• PEFT 0.15.2

Utilisation avec LoRA

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel

# Configuration
base_model_id = "google/medgemma-4b-it"
model_id = "Sadou/medgemma-4b-it-medical-report-simplifier"
device = "cuda" if torch.cuda.is_available() else "cpu"

# Chargement du modèle de base
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=BitsAndBytesConfig(load_in_4bit=True),
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

# Chargement des adaptateurs LoRA fine-tunés
model = PeftModel.from_pretrained(base_model, model_id)

# Chargement du tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_id)

def simplify_medical_report(medical_text, max_length=300, temperature=0.3):
    """
    Simplifie un rapport médical pour un patient
    
    Args:
        medical_text (str): Texte médical à simplifier
        max_length (int): Longueur maximale de la réponse
        temperature (float): Créativité (0.1 = conservateur, 0.7 = créatif)
    
    Returns:
        str: Explication simplifiée et rassurante
    """
    
    prompt = f"""<start_of_turn>user
{medical_text}<end_of_turn>
<start_of_turn>model
"""
    
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    
    with torch.inference_mode():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_length,
            do_sample=True,
            temperature=temperature,
            pad_token_id=tokenizer.eos_token_id,

        )
    
    response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
    return response.strip()

# Exemple d'utilisation
medical_text = "Bilan thyroïdien : 'TSH : 8.5 mUI/L (N: 0.4-4.0), T4 libre : 10 pmol/L (N: 10-25). Hypothyroïdie fruste.'"
simplified = simplify_medical_report(medical_text)
print(f"📋 Rapport médical:\n{medical_text}\n")
print(f"💬 Explication patient:\n{simplified}\n")