Data science teams have become increasingly comfortable fine-tuning large language models (LLMs) on their workstation or a single GPU box. Libraries like training_hub make it easy to run supervised fine-tuning (SFT), orthogonal subspace fine-tuning (OSFT), or LoRA-style fine-tuning with a few lines of Python. The hard part isn’t getting a model to train once. It’s turning that one local experiment...