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Repository Overview

This repository contains example notebooks and homeworks demonstrating various techniques in model optimization, such as knowledge distillation, model pruning, quantization, and low-rank approximation. Below is a breakdown of the included files and directories.

Directory Structure

├── Example Notebooks
│   ├── Knowledge Distillation
│   ├── Low Rank Approximation
│   ├── Model Pruning
│   └── Model Quantization
├── Pruning Homework
│   └── Pruning Homework.ipynb
├── Quantization Homework
│   └── input.py
├── LICENSE
└── README.md

Contents

1. Example Notebooks

This folder includes Jupyter notebooks that provide detailed examples and explanations of key optimization techniques:

  • Knowledge Distillation: Learn how to transfer knowledge from a larger teacher model to a smaller student model.
  • Low Rank Approximation: Explore techniques for reducing the rank of model weight matrices to save memory and computation.
  • Model Pruning: Understand strategies to remove unnecessary parameters from a model to improve efficiency.
  • Model Quantization: Discover methods to reduce model size and increase inference speed by lowering numerical precision.

2. Pruning Homework

  • Pruning Homework.ipynb: A hands-on Jupyter notebook exercise focused on implementing and understanding model pruning.

3. Quantization Homework

  • input.py: A Python script designed to accompany the quantization homework, serving as a starting point for further experimentation.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request to improve the repository.

Contact

For questions or feedback, please contact the repository maintainer.