Hugging Face proposes efficient knowledge distillation with offline top-K logits and fused, chunked KL loss
Making Knowledge Distillation Cheap Enough to Run at Scale
What happened
A new Hugging Face paper proposes two systems improvements that sharply reduce the training cost of distilling LLMs: cache the teacher model's top-100 logits offline so teacher and student do not need to reside in GPU memory simultaneously, and use a fused, chunked KL loss to avoid generating a full vocabulary-by-sequence-length matrix.
Hugging FaceOpen sourceDeployment engineeringData and trainingPapers and research#Research#Data and training#Open source#Deployment and engineering#Hugging Face
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