Parameter-Efficient Fine-Tuning (LoRA, Adapters)

Traditional fine-tuning updates every single parameter in a model, which for a model with billions of parameters is expensive and produces a complete copy of the model for each use case. Parameter-efficient fine-tuning (PEFT) methods offer a smarter alternative: they freeze the original model and add or modify only a tiny fraction of parameters. LoRA (Low-Rank Adaptation) is the most popular approach - it inserts small, trainable layers alongside the frozen model, typically modifying less than 1% of the total parameters. The result is remarkably close to full fine-tuning in quality but at a fraction of the cost. You can store just the small adapted layers rather than an entire model copy, making it practical to maintain dozens of specialised versions for different tasks. Adapters work on a similar principle, inserting small trainable modules between the existing layers. For businesses, PEFT methods have been transformative because they democratise customisation. What once required enterprise-scale computing budgets can now be done on a single high-end GPU. You can experiment with domain adaptation quickly and cheaply, maintain multiple specialised versions without multiplying your infrastructure costs, and iterate rapidly as your requirements change.