knowledge-distillation

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

3|Updated Feb 21, 2026

Search Tags:#llm #knowledge distillation #model compression #teacher-student #miniLLM #reverse KLD

Ready to run

npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill knowledge-distillation-ihatesea69

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: knowledge-distillation
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/19-emerging-techniques/knowledge-distillation
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill knowledge-distillation-ihatesea69

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 the creation of smaller, more efficient student models that retain a significant portion of the teacher model's performance.

Core Features & Use Cases

Model Compression: Reduce model size (e.g., 70B to 7B parameters) while maintaining high accuracy.
Capability Transfer: Distill knowledge from proprietary models (like GPT-4) into open-source alternatives.
Cost Reduction: Lower inference costs by using smaller, faster models.
Use Case: You have a powerful but slow 70B parameter model and need to deploy a faster, cheaper version for real-time applications. This skill helps you train a 7B parameter model that performs nearly as well.

Quick Start

Use the knowledge-distillation skill to train a smaller student model by distilling knowledge from a larger teacher model using reverse KL divergence.

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 to reduce inference costs?▼

Compress large language models into smaller student models using knowledge distillation from teacher models. This reduces model size and lowers inference costs while retaining a significant portion of the teacher's performance.

What is reverse KL divergence in knowledge distillation?▼

Reverse KL divergence is a training strategy used in knowledge distillation to train smaller student models. It helps transfer capabilities from large teacher models by optimizing soft targets and temperature scaling.

Can I distill knowledge from GPT-4 into an open-source model using transformers?▼

Yes, you can distill knowledge from proprietary models like GPT-4 into open-source alternatives. The skill uses transformers, torch, and datasets libraries to transfer capabilities and train efficient student models.

Does this knowledge distillation skill support MiniLLM training strategies?▼

Yes, the skill supports MiniLLM training strategies along with logit distillation, temperature scaling, and reverse KLD. These techniques help compress models like 70B parameters down to 7B.

Do I need DeepSpeed and accelerate for model compression with reverse KLD?▼

DeepSpeed and accelerate are dependencies used for training student models with reverse KLD. They work alongside transformers, torch, datasets, and wandb to handle the knowledge distillation workflow efficiently.