bert-large-config-24l-1024h-340m-params
IN premise — summaries/2026/08/24/wiki-BERT_language_model-chunk-1.md
Created 2026-08-24T17:11:05+00:00
BERT-LARGE has 24 Transformer layers, 1024 hidden size, 16 attention heads, 4096 feed-forward size, and 340M parameters; it was trained on 16 TPUs (64 chips) for 4 days.
Summary
This pins down the exact size and shape of BERT-LARGE — how many stacked layers it has, how wide its memory is, and roughly how many tunable numbers it contains — along with the hardware and time it took to train. It matters because any downstream decision about cost, capability comparisons, or whether a different model can substitute for it needs this concrete anchor to reason from.