Everyone is fine-tuning constantly though. Training an entire model in excess of a few billion parameters. It’s pretty much on nobody’s personal radar, you have a handful of well fundedgroups using pytorch to do that. The masses are still using pytorch, just on small training jobs.
Fine-tuning is great for known, concrete use cases where you have the data in hand already, but how much of the industry does that actually cover? Managers have hated those use cases since the beginning of the deep learning era — huge upfront cost for data collection, high latency cycles for training and validation, slow reaction speed to new requirements and conditions.
