chunk_content: ckpoint_folder, "adapter_model")
          kwargs["model"].save_pretrained(peft_model_path)
  
          pytorch_model_path = os.path.join(checkpoint_folder, "pytorch_model.bin")
          if os.path.exists(pytorch_model_path):
              os.remove(pytorch_model_path)
          return control
  Pass the Seq2SeqTrainingArguments, model, datasets, data collator, tokenizer, and callback to the Seq2SeqTrainer. You can optionally set model.config.use_cache = F