How to Train Custom Language Models: Fine-Tuning vs Training From Scratch (2026)

The article compares three approaches to building custom language models: prompt engineering (no training), fine-tuning pre-trained models, and pre-training from scratch. It covers dataset preparation, model selection, training code, evaluation, and deployment with compute estimates and Python examples. Fine-tuning is recommended as the sweet spot for most production use cases, while training from scratch is the most expensive but necessary for certain specialized requirements.