Am I doing wrongly the whole time?

I have a long PyTorch custom pipeline, where I have been using for a year-ish. However, recently, I noticed that I called self.model.eval() everytime I save a model to state dict. This does not seem to be at all common and I am worried if this is wrong.

Snippet of code:

    def save_model_artifacts(
        path: str,
        valid_trues: torch.Tensor,
        valid_logits: torch.Tensor,
        valid_preds: torch.Tensor,
        valid_probs: torch.Tensor,
    ) -> None:
        """Save the weight for the best evaluation metric and also the OOF scores."""
        # self.model.eval() # I been calling this .
                "model_state_dict": self.model.state_dict(),
                "optimizer_state_dict": self.optimizer.state_dict(),
                "scheduler_state_dict": self.scheduler.state_dict(),
                "oof_trues": valid_trues,
                "oof_logits": valid_logits,
                "oof_preds": valid_preds,
                "oof_probs": valid_probs,
      def fit(...):

I did it for very long and did not notice inference issues (results look ok), but Just want to be sure if this is a potential issue and how to avoid it.

calling eval may change the forward function behavior in the model, but not network parameter.
If you appropriately call .train() or .eval() after loading the model parameter, it will be okay. However, seems that it is not usual to call eval() before saving the parameters.