---
tags:
- medical
- disease
- symptoms
- treatment
- gpt2
license: apache-2.0
datasets:
- QuyenAnhDE/Diseases_Symptoms
---

# SmallMedLM

**SmallMedLM** is a fine-tuned [`distilgpt2`](https://huggingface.co/distilgpt2) model trained on medical text data about diseases, symptoms, and treatments.

---

## 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.

---

## Training Data

- Dataset: [Diseases_Symptoms](https://huggingface.co/datasets/QuyenAnhDE/Diseases_Symptoms)  
- Domain: Disease → Symptoms → Treatment mapping  
- Base model: `distilgpt2`

---

## Usage

### Inference Example

```python
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"))