Model Optimization

Welcome to the Model Optimization Module!
In this series, we'll explore powerful techniques to make machine learning models more efficient in terms of memory, computation, and inference speed — all while maintaining high accuracy.

Whether you're deploying models to edge devices or scaling inference in production, model optimization is key to performance.

Prerequisites

Before you begin, it’s recommended that you have:

• A basic understanding of deep learning, model training, and fine-tuning
• Hands-on experience with TensorFlow, PyTorch, or Hugging Face Transformers
• Familiarity with concepts like weights, activations, and inference

Learning Path Overview

This module is divided into several digestible parts:

1. Introduction to Model Optimization
   Why optimization matters — motivation, trade-offs, and applications

2. Numerical Precision & Data Formats
   Understand how numbers are stored and what happens during quantization
   → FP32, FP16, BF16, INT8, INT4 explained with examples

3. Optimization During Training
   Techniques like pruning, knowledge distillation, and mixed precision training

4. Post-Training Optimization
   Speed-up techniques without retraining — quantization, operator fusion, and weight clustering

5. Quantization Techniques
   Dive into modern methods and formats:

   • GGUF (GPTQ-compatible binary format)
   • AWQ (AutoAWQ for 4-bit INT quantization)
   • GPTQ (Quantization for Transformers)

6. Serving and Inference Libraries
   Popular toolkits and runtimes for fast inference:

   • NVIDIA TensorRT, ONNX Runtime
   • vLLM, Triton Inference Server
   • AutoAWQ, Exllama, MLC