chunk_content: er
  
  model = AutoModelForSemanticSegmentation.from_pretrained(
      checkpoint, id2label=id2label, label2id=label2id, ignore_mismatched_sizes=True
  )
  print_trainable_parameters(model)
  At this point you can check with the print_trainable_parameters helper function that all 100% parameters in the base
  model (aka model) are trainable.
  
  Wrap the base model as a PeftModel for LoRA training
  	
  To leverage the LoRa method, you need to wrap the base model 