app.py

app.py.txt

@@ -0,0 +1,54 @@
+# 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" 
+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}")