Unverified50% confidenceBenchmarkExact time
Compared to the original baseline model, the student model after joint distillation and quantization achieves a 98.21% improvement in inference speed.
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10/7/2026
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UnverifiedCompared to the original baseline model, the student model after joint distillation and quantization achieves a 69% reduction in size.77% similarUnverifiedThrough distillation, student models with only one-tenth the parameters of the teacher model can often retain 80%–95% of the teacher model's performance.70% similarUnverifiedDistilBERT retains approximately 97% of the original model's performance on the GLUE benchmark, with inference speed improved by approximately 60% and model size reduced by 40%.66% similarUnverifiedKnowledge distillation has the student network learn the soft probability distribution output by the teacher network, enabling the student model to retain core reasoning capabilities while reducing its parameter count by more than 10 times.66% similarUnverifiedIn memory tests, retaining the full context allowed the model to accurately locate specific details mentioned by students 95% of the time, whereas after summary compression the accuracy dropped sharply to 32%.65% similar
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