Previous chapters introduced the mechanics of Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA, QLoRA, and Adapter Tuning. This chapter shifts focus to the practical considerations involved in effectively training, optimizing, and deploying models fine-tuned using these techniques. We will address the operational aspects required to apply PEFT successfully in practice.

Here, you will learn to:

• Assess infrastructure needs, considering GPU memory, compute capabilities, and software environments for PEFT tasks.
• Configure suitable optimizers like AdamW or its memory-efficient variants (e.g., 8-bit Adam) and learning rate schedulers specific to PEFT.
• Implement strategies for handling multiple PEFT adapters concurrently, facilitating multi-task fine-tuning.
• Diagnose and troubleshoot common problems arising during the implementation and execution of PEFT training jobs.
• Employ performance profiling techniques to measure and enhance GPU utilization, memory consumption, and throughput.
• Adapt distributed training frameworks, including DDP and DeepSpeed ZeRO, for large-scale PEFT fine-tuning.
• Develop approaches for deploying models that utilize interchangeable PEFT adapters for efficient inference serving.

By covering these topics, this chapter equips you with the knowledge to refine PEFT workflows for optimal performance, resource management, and deployment.