--- base_model: unsloth/phi-4-unsloth-bnb-4bit library_name: peft license: mit datasets: - bigbio/pubmed_qa language: - en pipeline_tag: question-answering tags: - medicalQA --- # Model Card for Model ID ## Model Details ### Model Description ## How to Use ```python # Install required libraries !pip install unsloth peft bitsandbytes accelerate transformers # Import necessary modules from transformers import AutoTokenizer from unsloth import FastLanguageModel # Define the MedQA prompt medqa_prompt = """You are a medical QA system. Answer the following medical question clearly and in detail with complete sentences. ### Question: {} ### Answer: """ # Load the model and tokenizer using unsloth model_name = "Vijayendra/Phi4-MedQA" # Replace with your Hugging Face model name model, tokenizer = FastLanguageModel.from_pretrained( model_name=model_name, max_seq_length=2048, dtype=None, # Use default precision load_in_4bit=True, # Enable 4-bit quantization device_map="auto" # Automatically map model to available devices ) # Enable faster inference FastLanguageModel.for_inference(model) # Prepare the medical question medical_question = "What are the common symptoms of diabetes?" # Replace with your medical question inputs = tokenizer( [medqa_prompt.format(medical_question)], return_tensors="pt", padding=True, truncation=True, max_length=1024 ).to("cuda") # Ensure inputs are on the GPU # Generate the output outputs = model.generate( **inputs, max_new_tokens=512, # Allow for detailed responses use_cache=True # Speeds up generation ) # Decode and clean the response response = tokenizer.decode(outputs[0], skip_special_tokens=True) # Extract and print the generated answer answer_text = response.split("### Answer:")[1].strip() if "### Answer:" in response else response.strip() print(f"Question: {medical_question}") print(f"Answer: {answer_text}") ``` [More Information Needed] ### Framework versions - PEFT 0.14.0