Knowledge Distillation

Knowledge Distillation: Compressing LLMs

When to Use This Skill

Use Knowledge Distillation when you need to:

• Compress models from 70B → 7B while retaining 90%+ performance
• Transfer capabilities from proprietary models (GPT-4) to open-source (LLaMA, Mistral)
• Reduce inference costs by deploying smaller student models
• Create specialized models by distilling domain-specific knowledge
• Improve small models using synthetic data from large teachers

Key Techniques: Temperature scaling, soft targets, reverse KLD (MiniLLM), logit distillation, response distillation

Papers: Hinton et al. 2015 (arXiv 1503.02531), MiniLLM (arXiv 2306.08543), KD Survey (arXiv 2402.13116)

Installation

# Standard transformers
pip install transformers datasets accelerate

# For training
pip install torch deepspeed wandb

# Optional: MiniLLM implementation
git clone https://github.com/microsoft/LMOps
cd LMOps/minillm
pip install -e .

Quick Start

Basic Knowledge Distillation

import torch
import torch.nn.functional as F
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments