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.
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.
Ensure you have the following installed:
- Python 3.6 or higher
- pip
- Jupyter Notebook or JupyterLab