Fine-tuning

Fine-tuning is a form of Transfer Learning in which a Pre-trained Model—such as a Large Language Model or Convolutional Neural Network—is trained further on task- or domain-specific data, often a smaller dataset, to adapt its Model Parameters. It builds on representations learned during Pre-training, which may involve Self-Supervised Learning on large datasets, as used by organizations such as OpenAI and Google DeepMind. Adaptation commonly uses Supervised Learning with labeled data, but is not restricted to this approach. Parameter-Efficient Fine-Tuning methods such as LoRA and Adapter modules train a small subset of original or additional parameters while keeping most pretrained parameters fixed, generally reducing memory, computation, and storage costs compared with updating the entire model. These methods can help preserve pretrained knowledge, but do not inherently prevent Catastrophic Forgetting, which remains a risk even with LoRA. Companies such as NVIDIA provide GPU hardware and software that support fine-tuning at scale; this infrastructure does not ensure high Model Accuracy. Performance depends on the model, data, training procedure, evaluation, and deployment context, and fine-tuning alone does not guarantee reliability in medical diagnosis, legal analysis, or other high-stakes applications, which require additional validation and safeguards.