LLM Optimization

LLM optimization techniques

distillation

• = a larger teacher model train a smaller student model.
• the student model learns to statistically mimic the behavior of the teacher model
  • either just in the final prediction layer
  • or in the model's hidden layers as well
• typically effective for encoder-only models
• 

quantization

• = reduce weight's precision
• example: Post-Traning Quantization (PTQ)
• 

pruning

• = remove model weights with values close or equal to 0
• pruning methods
  • full model re-training
  • PEFT/LoRA (LLM Finetuning#PEFT techniques)
  • Post-training (Foundation Models#Post-training)
• in theory vs. in practice
  • in theory, it can reduce model size and improve performance
  • in practice, only small % in LLMs are 0-weights