Knowledge distillation techniques for LLMs explained

Knowledge distillation is a technique used to train smaller, more efficient language models (LLMs) by leveraging larger, more capable teacher models. Several methods exist, including matching the teacher's probability distribution over tokens, fine-tuning on teacher-generated text, and on-policy distillation where the teacher scores the student's output. Feature distillation also matches internal model representations, significantly reducing model size while retaining performance. AI

IMPACT Enables the creation of smaller, more efficient LLMs, potentially reducing computational costs and increasing accessibility.

RANK_REASON The item describes research papers and techniques for knowledge distillation in LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• Alexander M. Rush
• BERT
• DistilBERT
• Distilling the Knowledge in a Neural Network
• Geoffrey Hinton
• Hugging Face TRL
• Jeff Dean
• nodus/distill
• Oriol Vinyals
• Sequence-Level Knowledge Distillation
• TinyBERT
• Yoon Kim

• paper
• model release

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