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knowledge-distillation

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

11.5k|842|Updated Nov 3, 2025
Search Tags:#llm#knowledge distillation#model compression#model transfer#teacher-student#mini-llm
One-click install
npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill knowledge-distillation-orchestra-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: knowledge-distillation
Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/19-emerging-techniques/knowledge-distillation
Command: npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill knowledge-distillation-orchestra-research

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 degradation (often >90%).
  • Capability Transfer: Transfer knowledge from proprietary models (like GPT-4) to open-source alternatives.
  • Cost Reduction: Lower inference costs by using smaller, faster student models.
  • Specialized Models: Create tailored models by distilling domain-specific knowledge.
  • Use Case: You have a powerful but slow 70B parameter model. Use this Skill to distill its knowledge into a 7B parameter model, making it suitable for real-time applications on less powerful hardware.

Quick Start

Use the knowledge-distillation skill to compress the 'meta-llama/Llama-2-70b-hf' model into a 'meta-llama/Llama-2-7b-hf' 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 using knowledge distillation?▼

Knowledge distillation compresses large language models by transferring capabilities to smaller student models using techniques like temperature scaling and soft targets, retaining over 90% performance while significantly reducing inference costs and resource utilization.

Can I transfer capabilities from a proprietary model like GPT-4 to an open-source model?▼

You can transfer capabilities from proprietary models to open-source alternatives using capability transfer techniques, creating specialized models by distilling domain-specific knowledge into smaller, more efficient student models.

What is the best way to distill a 70B parameter model into a 7B model for real-time applications?▼

Distilling a 70B parameter model into a 7B model is best achieved using reverse KLD (MiniLLM) and logit distillation techniques, effectively reducing model size for real-time deployment on less powerful hardware with minimal degradation.

Do I need PyTorch and Hugging Face transformers to perform model compression?▼

You need PyTorch, Hugging Face transformers, and datasets libraries to perform model compression, alongside DeepSpeed and Accelerate to facilitate the teacher-student knowledge transfer process efficiently.

What are the limitations of using reverse KLD for MiniLLM model transfer?▼

Reverse KLD for MiniLLM model transfer requires significant computational resources and dependencies like DeepSpeed, making it an advanced implementation best suited for users needing high-fidelity capability transfer rather than simple model size reduction.

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