Fine-Tuning

Fine-tuning is the process of taking a pre-trained model and continuing its training on a specialized dataset, steering it toward a specific task, style, or domain. With LLaMA 4's permissive license, fine-tuning has exploded: 400+ community variants appeared within 72 hours of the model's release. Common fine-tuning targets include medical QA, legal analysis, customer support, specific coding languages, and creative writing styles. Parameter-efficient methods like LoRA (Low-Rank Adaptation) allow fine-tuning on consumer GPUs, democratizing the process further.