README.md

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license: apache-2.0
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QuaLA-MiniLM: a Quantized Length Adaptive
MiniLM

The article discusses the challenge of making transformer-based models efficient enough for practical use, 
given their size and computational requirements. The authors propose a new approach called QuaLA-MiniLM, 
which combines knowledge distillation, the length-adaptive transformer (LAT) technique, 
and low-bit quantization. This approach trains a single model that can adapt to any 
inference scenario with a given computational budget, achieving a superior accuracy-efficiency 
trade-off on the SQuAD1.1 dataset. The authors compare this approach to other efficient methods 
and find that it achieves up to an x8.8 speedup with less than 1% accuracy loss. 
The authors also provide their code publicly on GitHub. The article also discusses other related work 
in the field, including dynamic transformers and other knowledge distillation approaches.