GitHub - meta-pytorch/torchforge: PyTorch-native post-training at scale

This Technology Radar quadrant explores the platforms that are critical in software development

This Technology Radar quadrant explores the platforms that are critical in software development

Until recently, I held the opinion that training custom language models was inadvisable except in relatively rare cases. Simply requiring marginally better performance on your task was insufficient justification for customization; subsequent generations of models from foundation labs would inevitably catch up. Only when you had extreme latency constraints, specific tasks with low drift, and/or such high use that you could guarantee GPU utilization and ROI was it possible to justify the expected economics associated with managing the model lifecycle. And even then, achieving a successful outcome would be unlikely due to the number of ways customization could fail. Success here means both that the custom model performs as or better than expected on in-domain tasks (e.g., better than available alternatives in accuracy and/or speed) and that the business achieves positive ROI as a result of training and deploying the custom model.