Model-Quantization-Optimization

Quantizing GPT-2 to Reduce Costs and Latency

This repository contains a Jupyter Notebook that demonstrates the process of quantizing the GPT-2 model to optimize performance by reducing inference costs and latency. The notebook provides detailed steps and code for applying quantization techniques to GPT-2, making it more efficient for deployment in production environments.

Overview

Quantizing GPT-2 serves as a practical approach to enhance the execution speed and decrease the resource consumption of deploying large transformer models. This process involves converting a model trained with high precision floating-point numbers to use lower precision integers, balancing the trade-offs between performance and accuracy.

Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.6 or higher
• pip
• Jupyter Notebook or JupyterLab