Llm, Transformers, Fine Tuning, Gpu, Scaling Law.
factors collectively have a more substantial effect on how well the model loss can be reduced than architectural details (i.e. changing feed-forward ratio, aspect ratio, attention head dimension, etc), as shown in Fig2. This means that increasing the model’s size, using more extensive datasets, and