The Sandwich Transformer introduces a novel architecture that blends shallow and deep layers to optimize both model performance and computational efficiency. By combining multiple shallow sub-networks with a deep backbone, the Sandwich Transformer achieves better utilization of model capacity, enabling faster inference and improved generalization in various natural language processing tasks.
Key Features
Architecture combines shallow and deep transformer layers in parallel
Improves parameter efficiency without sacrificing model quality
Enables faster training and inference compared to standard transformers
Applicable to a variety of NLP tasks, including classification and generation
Open implementation with configuration options for sandwich structure
Android DevHub
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