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.
Install
npx skills add davila7/claude-code-templates@knowledge-distillationRuns in your terminal. Adds the skill globally for Claude Code, Cursor, Codex and others; add -g -y to skip prompts.
What it does
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…
Excerpt from the skill's own SKILL.md. Read the full file on GitHub before installing: skills run with your agent's permissions.
View source on GitHubBefore you install
Skills are plain text instructions the agent follows, sometimes with scripts. Check the source, prefer repositories with many installs and stars, and read any script it ships.
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