Quantization Recipes for NLP Models

This repository contains a series of Jupyter notebooks demonstrating various quantization and optimization techniques for NLP models. These notebooks provide practical implementations of state-of-the-art methods for model compression and efficient inference.

Notebooks

1. 01_large_language_model_optimization.ipynb: Optimizing a large language model for low-latency inference.
2. 02_vision_transformer_edge_optimization.ipynb: Fine-tuning and quantizing a Vision Transformer for edge devices.
3. 03_bert_question_answering_quantization.ipynb: Quantizing a BERT-based model for question answering tasks.
4. 04_multitask_nlp_quantization.ipynb: Transfer learning and quantization for multi-task NLP.

Key Features

• Mixed precision training
• Post-training quantization (PTQ)
• Quantization-aware fine-tuning (QAF)
• Dynamic quantization
• Pruning techniques
• Layer fusion
• Efficient attention mechanisms
• Knowledge distillation

Cheatsheet

Check out my cheatsheet called "Quantization and Precision Tuning for Optimization" for some more info! Feel free to share :)

Getting Started

1. Clone this repository

git clone https://github.com/ethanshebley/quantization-recipes.git cd quantization-recipes

2. Install the required packages

pip install -r requirements.txt

3. Open the Jupyter notebooks and run!

Contributing

I would welcome contributions! Please feel free to submit a Pull Request.