Individual skill
emerging-techniques-knowledge-distillation
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
skills-imported/emerging-techniques-knowledge-distillation/SKILL.md
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- emerging-techniques Declared skill tag
- knowledge-distillation Declared skill tag
- model-compression Declared skill tag
- teacher-student Declared skill tag
- minillm Declared skill tag
- reverse-kld Declared skill tag
- soft-targets Declared skill tag
- temperature-scaling Declared skill tag
- logit-distillation Declared skill tag
- model-transfer Declared skill tag
- engineering Derived category
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# Knowledge Distillation: Compressing LLMs
## When to Use This Skill
Use Knowledge Distillation when you need to:
- **Compress models** from 70B → 7B while retaining 90%+ performance
- **Transfer capabilities** from proprietary models (GPT-4) to open-source (LLaMA, Mistral)
- **Reduce inference costs** by deploying smaller student models
- **Create specialized models** by distilling domain-specific knowledge
- **Improve small models** using synthetic data from large teachers
**Key Techniques**: Temperature scaling, soft targets, reverse KLD (MiniLLM), logit distillation, response distillation
**Papers**: Hinton et al. 2015 (arXiv 1503.02531), MiniLLM (arXiv 2306.08543), KD Survey (arXiv 2402.13116)