Applying muP allows Probabilistic Transformers to scale to 0.4B parameters with transferred hyperparameters and outperform standard transformers on MLM tasks under equal parameter budgets.
In Advances in Neural Information Processing Systems (NeurIPS)
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Scaling Probabilistic Transformer via Efficient Cross-Scale Hyperparameter Transfer
Applying muP allows Probabilistic Transformers to scale to 0.4B parameters with transferred hyperparameters and outperform standard transformers on MLM tasks under equal parameter budgets.