Fine-tuning

Fine-tuning is the process of taking a pre-trained language model and further training it on a specific dataset to adapt it for particular tasks, domains, or behaviors. This technique allows organizations to customize general-purpose models for specialized applications without training from scratch.

There are several fine-tuning approaches: Full fine-tuning updates all model parameters (resource-intensive), LoRA (Low-Rank Adaptation) and QLoRA efficiently update small adapter layers, and instruction fine-tuning teaches models to follow specific formats or guidelines.

Fine-tuning is valuable when you need consistent outputs in a specific format, domain-specific knowledge, particular writing styles, or improved performance on specialized tasks. However, it requires quality training data, compute resources, and careful evaluation. Many use cases are better served by prompt engineering or RAG, so engineers should carefully evaluate whether fine-tuning is the right approach for their needs.