Distilled Transformer Inference With Reduced Memory Footprint

via “efficient-inference-with-model-distillation”

sentence-similarity model by undefined. 20,92,10,613 downloads.

Unique: Uses asymmetric distillation where student (6 layers) learns from teacher (12 layers) via MSE loss on hidden states and attention patterns, not just final embeddings; preserves semantic structure while reducing depth, enabling both speed and quality retention

vs others: Faster inference than full BERT-base (5-10x) and smaller than full models (22.7M vs 110M params), though slower than extreme compression techniques (TinyBERT, MobileBERT) which sacrifice more quality; better quality-to-speed trade-off than quantization-only approaches