Quantization is a technique used to reduce the computational and memory overhead of a machine learning model by reducing the precision of the numbers used to represent the model's parameters. Typically, models use 32-bit floating-point numbers, but quantization converts these to 8-bit integers (or 4-bit integers). This can significantly reduce the model size and increase the inference speed, especially on CPUs and other hardware with limited computational resources. While this can lead to a slight reduction in model accuracy, the trade-off is often worthwhile for faster and more efficient deployments.
This GitHub repo contains the notebook from "A Hands-On Walkthrough on Model Quantization" blog post. This notebook demonstrates the process of quantizing and saving a Transformer model to improve the inference speed on a CPU and reduce the model size.
| Description | Link |
|---|---|
| A Hands-On Walkthrough on Model Quantization |  |
See our LICENSE for more details.