question: Saving and loading HF transformer model fine tuned with PL?
context: I am fine-tuning hugging face transformer models, essentially exactly as shown in the following example found in the pytorch lightning docs:
  https://pytorch-lightning.readthedocs.io/en/latest/notebooks/lightning_examples/text-transformers.html
  Where we instantiate the LightningModule doing something like this:
  class GLUETransformer(LightningModule):
  
      def __init__(self, ... ):
          super().__init__()
          self.config = AutoConfig.from_pretrained(model_name_or_path, num_labels=num_labels)
          self.model = AutoModelForSequenceClassification.from_pretrained(
              model_name_or_path, config=self.config
          )
  But I have been confused about how I should be saving and loading checkpoints.
  When saving checkpoints, should I be using
  mymodel.model.save_pretrained("model_save_dir"),
  and reloading from this checkpoint using
  AutoModelForSequenceClassification.from_pretrained("model_save_dir"),
  or saving with
  trainer.save_checkpoint("model_save_dir/checkpoint.ckpt"),
  and reloading with
  GLUETransformer.load_from_checkpoint("model_save_dir/checkpoint.ckpt")?
answer: Dear @brijow,
  You should be using the second approach. An even better one would be to rely on ModelCheckpoint to save the checkpoints and provide Trainer(resume_from_checkpoint=...) for reloading all the states.
  Best,
  T.C