LFT replaces up to 13 of 24 transformer layers of Pythia-410M with a single flow-based layer trained with Flow Walking, achieving KL 0.736 vs 0.932 for skipping three layers.
Compressing bert: Studying the effects of weight pruning on transfer learning
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Latent Flow Transformer
LFT replaces up to 13 of 24 transformer layers of Pythia-410M with a single flow-based layer trained with Flow Walking, achieving KL 0.736 vs 0.932 for skipping three layers.