SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics

no code implementations • 29 May 2023 • Arash Ardakani, Altan Haan, Shangyin Tan, Doru Thom Popovici, Alvin Cheung, Costin Iancu, Koushik Sen

This allows SlimFit to freeze up to 95% of layers and reduce the overall on-device GPU memory usage of transformer-based models such as ViT and BERT by an average of 2. 2x, across different NLP and CV benchmarks/datasets such as GLUE, SQuAD 2. 0, CIFAR-10, CIFAR-100 and ImageNet with an average degradation of 0. 2% in accuracy.

Quantization Scheduling