health-link-assistant

Health Link AI Assistant

This is a specialized healthcare AI assistant trained to help with healthcare-related queries and interact with the Health Link QR System. The model is based on DistilGPT2 and fine-tuned on healthcare domain data.

Model Description

• Base Model: DistilGPT2
• Task: Healthcare Question Answering & System Assistance
• Language: English
• Training Dataset: Healthcare domain dataset
• License: MIT

Intended Uses

This model is designed to:

1. Answer general healthcare queries
2. Assist with Health Link QR System navigation
3. Help with medical record management
4. Provide healthcare documentation assistance

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("Saiguru2554/health-link-assistant")
tokenizer = AutoTokenizer.from_pretrained("Saiguru2554/health-link-assistant")

def generate_response(prompt, max_length=256, temperature=0.7):
    # Prepare the input
    inputs = tokenizer(prompt, return_tensors="pt", padding=True)
    
    # Generate response
    with torch.no_grad():
        outputs = model.generate(
            inputs.input_ids,
            max_length=max_length,
            temperature=temperature,
            pad_token_id=tokenizer.eos_token_id,
            attention_mask=inputs.attention_mask,
            do_sample=True,
            top_p=0.9,
            no_repeat_ngram_size=3,
            repetition_penalty=1.2
        )
    
    # Decode and return the response
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    if prompt in response:
        response = response[len(prompt):].strip()
    return response

# Example usage
prompt = "user: What are the symptoms of vitamin D deficiency?\nassistant:"
response = generate_response(prompt)
print(response)

Limitations & Biases

• This model is intended for general healthcare information only
• Not a replacement for professional medical advice
• May have biases from training data
• Should be used in conjunction with healthcare professionals
• Responses should be verified by medical experts

Training Details

• Training Data: Healthcare domain dataset
• Training Procedure: Fine-tuned using causal language modeling
• Parameters:
  • Max Sequence Length: 256
  • Batch Size: 1
  • Gradient Accumulation: 32
  • Learning Rate: 1e-5
  • Epochs: 3

Evaluation Results

The model has been validated on healthcare-related queries and system interaction scenarios. However, all outputs should be reviewed by healthcare professionals before making any medical decisions.

Citation

If you use this model in your research or application, please cite:

@misc{health-link-assistant,
  author = {Sai Guru},
  title = {Health Link AI Assistant},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/Saiguru2554/health-link-assistant}}
}

Contact

For questions and feedback, please open an issue on the model's repository page.