Having established the core Transformer architecture and its scaling properties, practical applications often require further adjustments for efficiency or specialized adaptation. Retraining massive models entirely for every new requirement is computationally expensive and often unnecessary.

This chapter focuses on modifications to the standard Transformer design that address these needs. We will examine:

• Parameter-Efficient Adaptation: Techniques like Adapter modules that allow fine-tuning for specific tasks by modifying only a small fraction of the total parameters.
• Conditional Computation: Methods such as Mixture-of-Experts (MoE) layers, which increase the total number of parameters in a model to boost capacity, but only activate a subset of these parameters for any given input, managing computational cost.

You will learn the motivation behind these approaches, understand their structural differences from the base Transformer, and explore implementation considerations like routing mechanisms and load balancing in MoE systems.