Description: Model compression refers to a set of techniques used to reduce the size of a machine learning model, especially in the context of large language models, without significantly sacrificing its performance. This practice is crucial due to the growing demand for models that are not only accurate but also efficient in computational resource usage. Model compression can include methods such as pruning, which removes unnecessary parameters; quantization, which reduces the precision of the numbers used in calculations; and distillation, which involves training a smaller model to replicate the behavior of a larger one. These techniques allow models to be faster and less costly to implement, facilitating their use on resource-limited devices in various domains, such as mobile devices, embedded systems, and IoT applications. Additionally, model compression contributes to sustainability by reducing the energy consumption associated with training and inference of large models. In a world where efficiency and speed are increasingly valued, model compression has become an active and highly relevant area of research in the field of machine learning.
History: Model compression began to gain attention in the 2010s when deep learning models started to grow in size and complexity. As researchers realized that larger models did not always translate to better performance, they began exploring techniques to make these models more manageable. An important milestone was the introduction of model distillation by Geoffrey Hinton and his colleagues in 2015, which allowed smaller models to learn from larger ones. Since then, model compression has evolved and become an active area of research, with numerous advancements in pruning and quantization techniques.
Uses: Model compression is primarily used in applications where computational resources are limited, such as mobile devices, embedded systems, and cloud applications where cost reduction is sought. It is also useful in situations where a quick response is required, such as in virtual assistants and chatbots, where latency is critical. Additionally, model compression enables the deployment of artificial intelligence solutions in energy-constrained environments, such as IoT sensors.
Examples: An example of model compression is the use of distillation in the BERT model, where a smaller model is trained to mimic the behavior of the original BERT model, achieving similar performance with a significantly smaller size. Another case is the quantization of computer vision models, where the precision of the model weights is reduced to allow execution on mobile devices without losing much in terms of accuracy.
