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knowledge-distillation

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

1|Updated Feb 21, 2026
Search Tags:#llm#knowledge distillation#model compression#model transfer#inference optimization#teacher-student
One-click install
npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill knowledge-distillation-tianhao909
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: knowledge-distillation
Source: https://github.com/tianhao909/AI-Research-SKILLs-cn/tree/main/19-emerging-techniques/knowledge-distillation
Command: npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill knowledge-distillation-tianhao909

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, torch, datasets, deepspeed, wandb, and includes scripts (resource) and 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 loss (often >90%).
  • Capability Transfer: Transfer knowledge from powerful proprietary models (like GPT-4) to open-source alternatives.
  • Cost Reduction: Lower inference costs by using smaller, faster student models.
  • Specialized Models: Create domain-specific models by distilling targeted knowledge.
  • Key Techniques: Temperature scaling, soft targets, reverse KLD (MiniLLM), logit distillation, response distillation.

Quick Start

Use the knowledge-distillation skill to distill the Llama-2-70b model into the Llama-2-7b model using temperature scaling and soft targets.

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 like Llama-70B into a smaller 7B model?▼

Knowledge distillation compresses large models by transferring capabilities from a teacher to a smaller student model. This Skill facilitates compressing models like Llama-2-70b into 7b parameters using temperature scaling and soft targets, retaining over 90% performance.

How does reverse KLD logit distillation work for transferring capabilities from GPT-4?▼

Reverse KLD logit distillation transfers capabilities from proprietary models like GPT-4 to open-source alternatives using MiniLLM training strategies. This technique optimizes the student model by focusing on reverse Kullback-Leibler divergence to minimize performance loss during compression.

Can I use PyTorch and DeepSpeed for model compression?▼

Yes, you can use PyTorch and DeepSpeed for model compression. This Skill relies on dependencies including torch, transformers, datasets, and deepspeed to execute knowledge distillation techniques and optimize the training workflow.

What is the best way to reduce inference costs for open-source models like LLaMA and Mistral?▼

The best way to reduce inference costs is using knowledge distillation to compress large models into smaller student models. By transferring capabilities from powerful teacher models to efficient open-source alternatives, you significantly lower deployment expenses.

When should I use temperature scaling and soft targets for specialized model creation?▼

Use temperature scaling and soft targets during knowledge distillation when creating domain-specific models. This approach transfers targeted knowledge from teacher models to student models, ensuring the specialized model retains necessary capabilities while remaining efficient.

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