chunk_content: k(TrainerCallback):
      def on_save(
          self,
          args: TrainingArguments,
          state: TrainerState,
          control: TrainerControl,
          **kwargs,
      ):
          checkpoint_folder = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")
  
          peft_model_path = os.path.join(checkpoint_folder, "adapter_model")
          kwargs["model"].save_pretrained(peft_model_path)
  
          pytorch_model_path = os.path.