chunk_content: odel = PeftModel.from_pretrained(model, peft_model_id)
  Grab a tweet and tokenize it:
  
  
  	Copied
  inputs = tokenizer(
      f'{text_column} : {"@nationalgridus I have no water and the bill is current and paid. Can you do something about this?"} Label : ',
      return_tensors="pt",
  )
  Put the model on a GPU and generate the predicted label:
  
  
  	Copied
  model.to(device)
  
  with torch.no_grad():
      inputs = {k: v.to(device) for k, v in inputs.items()}
      o