I have a model that looks like the following:
class Model(pl.LightningModule): def __init__( self, base: str, embedding_dim: int, num_classes: int, n_hidden: int, loss_fn: Callable, lr: float, metrics: Dict[str, Metric], ): super().__init__() self.base = AutoModel.from_pretrained(base) self.tokenizer = AutoTokenizer.from_pretrained(base)
I can see how to load a model using the docs. However, I am a bit worried that considering the
base parameter is a string (name of HF model), it will run the first line and start downloading the huggingface model first before it overlays it with the weights from
The pod that I’m using does not have access to the internet and will fail in prod. Regardless I don’t want it to download a model first everytime this pod spins up (since its a hourly job). So any thoughts on this?