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knowledge-distillation

Compress large language models into smaller student models using knowledge distillation.

1|Updated Feb 10, 2026
Search Tags:#llm#knowledge distillation#model compression#model transfer#inference optimization#teacher-student
One-click install
npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill knowledge-distillation-informatico-madrid
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: knowledge-distillation
Source: https://github.com/informatico-madrid/Architect-Expert-Gap-Forge/tree/main/.github/skills/knowledge-distillation
Command: npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill knowledge-distillation-informatico-madrid

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, torch, datasets, accelerate, deepspeed, wandb, and includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of deploying large, computationally expensive language models by enabling their compression into smaller, more efficient student models while preserving significant performance.

Core Features & Use Cases

  • Model Compression: Reduce model size (e.g., 70B to 7B parameters) with minimal performance degradation (e.g., >90%).
  • Knowledge Transfer: Transfer capabilities from proprietary models (like GPT-4) to open-source alternatives (like LLaMA or Mistral).
  • Cost Reduction: Lower inference costs by using smaller, faster student models.
  • Specialized Models: Create domain-specific models by distilling expert knowledge.
  • Use Case: You have a powerful but slow 70B parameter model and need to deploy a faster, cheaper 7B model for real-time customer support. This Skill allows you to distill the 70B model's knowledge into the 7B model, ensuring it retains most of the original's capabilities.

Quick Start

Use the knowledge-distillation skill to compress the 'teacher-model-name' into the 'student-model-name' using a temperature of 2.0 and an alpha of 0.7.

Frequently Asked Questions about knowledge-distillation

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

FAQPage Schema
How do I compress a large language model using knowledge distillation?▼

Knowledge distillation compresses large language models by transferring capabilities from a large teacher model to a smaller student model using techniques like temperature scaling and soft targets to retain performance.

What is the best way to reduce inference costs for a 70B parameter model?▼

Reducing inference costs involves distilling the 70B parameter teacher model into a smaller 7B student model, preserving over 90% of performance while significantly lowering deployment expenses.

Can I transfer capabilities from GPT-4 to an open-source LLaMA or Mistral model?▼

Yes, knowledge distillation enables transferring capabilities from proprietary models like GPT-4 to open-source alternatives like LLaMA or Mistral through logit distillation and reverse KLD techniques.

Does knowledge distillation work with DeepSpeed and Accelerate for training?▼

Knowledge distillation supports integration with DeepSpeed and Accelerate to facilitate efficient training of student models, utilizing MiniLLM training strategies for optimized knowledge transfer.

What are the limitations of using logit distillation for model compression?▼

Logit distillation requires careful tuning of temperature and alpha parameters, and while it reduces model size significantly, some performance degradation compared to the teacher model is inevitable.

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