from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "kaashh02/phi3-mini-medical-merged"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Example usage
prompt = "What are the symptoms of diabetes?"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Details

Fine-tuned using LoRA adapters
Optimized for medical question-answering and healthcare information
Trained with 8-bit quantization for efficiency

Limitations

This model is intended for educational and research purposes
Should not be used as a substitute for professional medical advice
Always consult healthcare professionals for medical decisions

Citation

If you use this model, please cite:

bibtex

@misc{phi3_mini_medical_merged,
  title={phi3-mini-medical-merged: Medical Fine-tuned Phi-3 Model},
  author={Your Name},
  year={2025},
  howpublished={\url{https://huggingface.co/kaashh02/phi3-mini-medical-merged}}
}