Bypasses the slow GPU Main Memory (HBM) by calculating attention directly in SRAM, reducing the memory footprint from
Build a Large Language Model (From Scratch) PDF: A Comprehensive Guide build a large language model %28from scratch%29 pdf
To prevent the model from generating harmful, biased, or hallucinated content, it must be aligned with human preferences. Bypasses the slow GPU Main Memory (HBM) by
[ PE_(pos, 2i) = \sin(pos / 10000^2i/d_model) ] [ PE_(pos, 2i+1) = \cos(pos / 10000^2i/d_model) ] or hallucinated content
Pretraining on unlabeled data and fine-tuning for specific tasks or instructions.
Which option do you prefer?