SmallMedLM

SmallMedLM is a fine-tuned distilgpt2 model trained on medical text data about diseases, symptoms, and treatments.

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Model Description

This model is designed for generating medical information given a disease or symptom prompt.
It can output possible symptoms for a disease or suggest treatment directions based on symptoms.

⚠️ Disclaimer: This model is for research/educational purposes only. It is not a substitute for professional medical advice. Always consult a qualified healthcare professional.

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Training Data

• Dataset: Diseases_Symptoms
• Domain: Disease → Symptoms → Treatment mapping
• Base model: distilgpt2

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Usage

Inference Example

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model_name = "sumanthmandavalli/SmallMedLM"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

def generate_medical_info(disease_name, max_length=100):
    prompt = f"Disease: {disease_name} | Symptoms: "
    inputs = tokenizer.encode(prompt, return_tensors="pt")
    
    outputs = model.generate(
        inputs,
        max_length=max_length,
        num_return_sequences=1,
        no_repeat_ngram_size=2,
        top_k=50,
        top_p=0.95,
        temperature=0.7,
        do_sample=True
    )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generate_medical_info("Diabetes"))