Hi there!
prediction_loss_only was deprecated in v3.x and has been removed in v4. In its place you have a model_init, that’s why you get this error. You should change your signature to match:
def __init__(
        self,
        model: Union[PreTrainedModel, torch.nn.Module] = None,
        args: TrainingArguments = None,
        data_collator: Optional[DataCollator] = None,
        train_dataset: Optional[Dataset] = None,
        eval_dataset: Optional[Dataset] = None,
        tokenizer: Optional["PreTrainedTokenizerBase"] = None,
        model_init: Callable[[], PreTrainedModel] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
        callbacks: Optional[List[TrainerCallback]] = None,
        optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
    ):

In general, when passing keyword arguments from one method to another, you should always use the syntax name=value (so here data_collator=data_collator, train_dataset=train_dataset…) because functions can have some keyword arguments added from one version to another. If you rely just on the order of their arguments like here, you can get some mismatches that create errors down the line.