Edge 459: Quantization Plus Distillation

Some insights into quantized distillation

In this issue:

1. An overview of quantized distillation.

2. A review of Google DeepMind’s paper on model quantization and distillation.

3. An introduction to IBM Granite 3.0 enterprise foundation models.

💡 ML Concept of the Day: Understanding Quantized Distillation

To conclude our series about knowledge distillation, I would like to dive into one of the most sophisticated methods that combines distillation and quantization.

Quantized distillation has emerged as a powerful technique for compressing and optimizing deep neural networks, combining the benefits of knowledge distillation and quantization. This approach aims to transfer knowledge from a high-precision teacher model to a low-precision student model, enabling the deployment of compact, efficient networks without significant loss in performance. By leveraging the soft targets produced by the teacher model, quantized distillation can help mitigate the accuracy degradation typically associated with aggressive quantization schemes.