knowledge-distillation
knowledge-distillation is a Fine-tuning & training data skill for AI agents, published by davila7 in the claude-code-templates repository on GitHub. Skillspace rates knowledge-distillation C (78/100) and it passed the safety scan; a judge model has also reviewed it.
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.
- Grade
- C
- Quality
- 78 / 100
- Safety
- Passed
- GitHub stars
- 30,694
Placed #4 of 6 in the judged category final for Fine-tuning & training data.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 96 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Judge review
Placed #4 of 6 in the judged category final for Fine-tuning & training data.
- Trigger
85 85 - Instructions
88 88 - Specificity
90 90 - Safety
78 78 - Disclosure
55 55 - Maintenance
50 50
Well-cited, code-complete coverage of distillation techniques including reverse-KL/MiniLLM, though it is one long file and leans on a dated teacher-model example.
Not checked yet
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- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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