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.
7.0
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AI & LLM
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Excellent skill for knowledge distillation with comprehensive coverage of techniques (temperature scaling, soft targets, reverse KLD, MiniLLM). The description clearly articulates when to use this skill (model compression, cost reduction, capability transfer). Task knowledge is thorough with complete code examples for basic distillation, MiniLLM, response distillation, and production deployment. Structure is good with logical flow from quick start to advanced strategies, though the SKILL.md is somewhat long and could benefit from moving some advanced content to separate files. Novelty is solid - implementing proper knowledge distillation with temperature scaling, multiple loss functions, and modern techniques like reverse KLD would be token-intensive for a CLI agent and requires deep ML knowledge. The skill provides significant value for practitioners needing to compress LLMs while maintaining performance. Minor improvement areas: could externalize some production examples to keep SKILL.md more concise, and add more quantitative benchmarks showing compression vs performance tradeoffs.
zechenzhangAGI
Skill Author
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