Method _save

src/transformers/trainer.py:3833–3870

Save model weights, configuration, and processing class to `output_dir`.

(self, output_dir: str | None = None, state_dict: dict | None = None)

            self.push_to_hub(commit_message="Model save", revision=self.args.hub_revision)

    def _save(self, output_dir: str | None = None, state_dict: dict | None = None) -> None:
        """Save model weights, configuration, and processing class to `output_dir`."""
        # If we are executing this function, we are the process zero, so we don't check for that.
        output_dir = output_dir if output_dir is not None else self.args.output_dir
        os.makedirs(output_dir, exist_ok=True)
        logger.info(f"Saving model checkpoint to {output_dir}")

        supported_classes = (PreTrainedModel,) if not is_peft_available() else (PreTrainedModel, PeftModel)
        # Save a trained model and configuration using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        if not isinstance(self.model, supported_classes):
            if state_dict is None:
                state_dict = self.model.state_dict()

            if isinstance(self.accelerator.unwrap_model(self.model, keep_torch_compile=False), supported_classes):
                self.accelerator.unwrap_model(self.model, keep_torch_compile=False).save_pretrained(
                    output_dir, state_dict=state_dict
                )
            else:
                logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
                safetensors.torch.save_file(
                    state_dict, os.path.join(output_dir, SAFE_WEIGHTS_NAME), metadata={"format": "pt"}
                )
        else:
            self.model.save_pretrained(output_dir, state_dict=state_dict)

        if self.processing_class is not None:
            self.processing_class.save_pretrained(output_dir)
        elif (
            self.data_collator is not None
            and hasattr(self.data_collator, "tokenizer")
            and self.data_collator.tokenizer is not None
        ):
            logger.info("Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`")
            self.data_collator.tokenizer.save_pretrained(output_dir)

        # Good practice: save your training arguments together with the trained model
        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

    # ---- Logging & Metrics ----