Create app.py

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Model name
+model_name = "MONAI/Llama3-VILA-M3-8B"
+
+# Load tokenizer and model with trust_remote_code=True
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,  # Use float16 for optimized inference
+    device_map="auto",          # Automatically assigns model to available GPU/CPU
+    trust_remote_code=True      # Allows loading custom model code if needed
+)
+
+# Example input prompt
+prompt = "Explain the findings in this chest X-ray report:"
+
+# Tokenize input
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
+
+# Generate response
+with torch.no_grad():
+    output = model.generate(**inputs, max_length=200)
+
+# Decode and print response
+response = tokenizer.decode(output[0], skip_special_tokens=True)
+print("\nGenerated Response:\n", response)