Model Quantization

Having fine-tuned our model and augmented its training data, we arrive at the final stage of optimization before deployment. Large Language Models, by their nature, are computationally intensive. They consume a significant amount of memory (VRAM and RAM) and can be slow during inference, making deployment challenging, especially on resource-constrained hardware. This chapter introduces Quantization, a powerful set of techniques designed to shrink a model's size and accelerate its performance, making it leaner and more efficient for real-world applications.