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knowledge-distillation

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

2|Updated Feb 4, 2026
Search Tags:#llm#knowledge distillation#model compression#teacher-student#miniLLM#reverse KLD
One-click install
npx skills add https://github.com/zhuangbiaowei/smart_bot --skill knowledge-distillation-zhuangbiaowei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: knowledge-distillation
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/knowledge-distillation
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill knowledge-distillation-zhuangbiaowei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, torch, datasets, accelerate, 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.
  • Capability Transfer: Distill knowledge from proprietary models (like GPT-4) into open-source alternatives.
  • Cost Reduction: Lower inference costs by using smaller, faster student models.
  • Use Case: You have a powerful but slow 70B parameter model and need to deploy a faster, cheaper 7B model for real-time applications. This skill helps you train the 7B model to mimic the 70B model's capabilities.

Quick Start

Use the knowledge-distillation skill to compress a large teacher model into a smaller student model using reverse KLD.

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 into a smaller student model?▼

Compress a large language model into a smaller student model using knowledge distillation techniques like reverse KLD and logit distillation to transfer capabilities from a teacher to a student model.

What is reverse KLD in knowledge distillation?▼

Reverse KLD in knowledge distillation is a method used by MiniLLM to train a smaller student model by minimizing the reverse Kullback-Leibler divergence, effectively transferring capabilities from a large teacher model.

Can I distill knowledge from GPT-4 into an open-source model like LLaMA?▼

You can distill knowledge from proprietary models like GPT-4 into open-source alternatives such as LLaMA or Mistral, transferring capabilities while reducing inference costs for real-time applications.

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

Reduce inference costs for a 70B parameter model by using knowledge distillation to train a faster, cheaper 7B student model that mimics the teacher model's capabilities with minimal performance loss.

Do I need DeepSpeed and Accelerate to train a student model with reverse KLD?▼

Training a student model with reverse KLD requires dependencies including DeepSpeed and Accelerate, alongside PyTorch and Transformers, to facilitate the distillation process.

Does knowledge distillation with temperature scaling work for model compression?▼

Knowledge distillation uses temperature scaling and soft targets to compress large language models, reducing model size from 70B to 7B parameters while retaining significant performance.

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