chunk_content:  as well
  as how many of them are trainable.
  
  
  	Copied
  def print_trainable_parameters(model):
      trainable_params = 0
      all_param = 0
      for _, param in model.named_parameters():
          all_param += param.numel()
          if param.requires_grad:
              trainable_params += param.numel()
      print(
          f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param:.2f}"
      )
  It’s 