Full Fine-Tuning

Also known as: FFT, standard fine-tuning

Full fine-tuning updates every parameter in the base model during training, as opposed to PEFT methods that freeze most weights. It offers the greatest flexibility and can achieve the deepest behavioral adaptation, but demands significant GPU memory — often requiring multiple high-end accelerators for models over a few billion parameters. Full fine-tuning also carries a higher risk of catastrophic forgetting and typically requires careful learning rate scheduling to avoid destroying pretraining knowledge.