Full Fine-Tuning
A rigorous training method where every single parameter in a pre-trained neural network is updated, requiring significant computational power and system memory.
Think of It Like This
Like completely rebuilding a house from the foundation up to change its layout, rather than just remodeling a single room or painting the exterior walls.
Unlike Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA, full fine-tuning modifies the entire weight matrix of the model. While this can yield the absolute highest performance for complex domain adaptations, it requires massive GPU clusters to store optimizer states and gradients. It is generally reserved for highly specialized, high-budget enterprise models.