Parameter-Efficient Fine-Tuning with LoRA Using Custom Data

LoRA (Low-Rank Adaptation) enables efficient fine-tuning of large language models by freezing the base model and training only small adapter weights, dramatically reducing computational costs while preserving general reasoning capabilities. This technique allows developers to customize models for specific domains, writing styles, or tasks using just hundreds of high-quality examples rather than expensive full model retraining.