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knowledge-distillation

Compress large language models via teacher-to-student knowledge distillation.

Updated May 4, 2026
Search Tags:#knowledge distillation#temperature scaling#model compression#llm training#reverse kld#minillm#teacher student
One-click install
npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill knowledge-distillation-supporter09
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: knowledge-distillation
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/knowledge-distillation
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill knowledge-distillation-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you shrink large language models into smaller, cheaper students while preserving as much capability as possible. It is useful when you need lower inference cost, faster deployment, or an open-source student model that retains the behavior of a stronger teacher.

Core Features & Use Cases

  • Teacher-to-student distillation: Transfer knowledge from a large teacher model into a smaller student using soft targets and blended losses.
  • Generative model training: Apply reverse KLD and MiniLLM-style techniques when distilling open-ended text generation systems.
  • Practical workflows: Use it for model compression, capability transfer, synthetic response training, and multi-teacher experimentation in research or production settings.

Quick Start

Ask the skill to distill a teacher model into a smaller student for your target prompts, then tune temperature and loss balance for the desired tradeoff between fidelity, diversity, and compute savings.

Frequently Asked Questions about knowledge-distillation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does knowledge distillation compress large language models?▼

Knowledge distillation compresses large language models by transferring capabilities from a teacher model to a smaller student using temperature-scaled soft targets and blended losses, retaining behavior while lowering inference costs.

What is reverse KLD in MiniLLM generative model training?▼

Reverse KLD in MiniLLM generative model training is a divergence-based loss technique used to distill open-ended text generation systems, helping smaller student models better mimic teacher outputs.

Can I use multi-teacher training for model compression?▼

Yes, you can use multi-teacher training for model compression. This Skill supports multi-teacher experimentation, allowing you to transfer knowledge from several teacher models into a single student.

How do I tune temperature scaling during teacher student distillation?▼

To tune temperature scaling during teacher student distillation, adjust the temperature parameter to control the softness of targets, balancing the tradeoff between fidelity, diversity, and compute savings.

What are the limitations of response distillation for llm training?▼

A limitation of response distillation for llm training is the potential quality drop compared to the original model, requiring evaluation of the student's behavior against the teacher to ensure acceptable fidelity.

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