Update README.md

README.md

@@ -37,28 +37,4 @@ Limitations:
 The model should not be used as a substitute for professional medical advice.
 Responses should be verified by qualified medical professionals.
 
-RUNNING THROUGH ADAPTER MERGE: 
 
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-import torch
-
-base_model_name = "unsloth/Llama-3.2-3B-Instruct"  
-base_model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float16, device_map="auto")
-
-adapter_path = "vishal042002/Llama3.2-3b-Instruct-ClinicalSurgery"
-base_model = PeftModel.from_pretrained(base_model, adapter_path)
-
-tokenizer = AutoTokenizer.from_pretrained(base_model_name)
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-base_model.to(device)
-
-# Sample usage
-input_text = "What is the mortality rate for patients requiring surgical intervention who were unstable preoperatively?"
-inputs = tokenizer(input_text, return_tensors="pt").to(device)
-
-outputs = base_model.generate(**inputs, max_new_tokens=200, temperature=1.5, top_p=0.9)
-decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-print(decoded_output)