#!/bin/bash RANK=0 WORLD_SIZE=1 DATA_PATH=_text_sentence CHECKPOINT_PATH= python pretrain_bert.py \ --num-layers 24 \ --hidden-size 1024 \ --num-attention-heads 16 \ --micro-batch-size 4 \ --global-batch-size 8 \ --seq-length 512 \ --max-position-embeddings 512 \ --train-iters 2000000 \ --lr-decay-iters 990000 \ --save $CHECKPOINT_PATH \ --load $CHECKPOINT_PATH \ --data-path $DATA_PATH \ --vocab-file bert-vocab.txt \ --data-impl mmap \ --split 949,50,1 \ --lr 0.0001 \ --min-lr 0.00001 \ --lr-decay-style linear \ --lr-warmup-fraction .01 \ --weight-decay 1e-2 \ --clip-grad 1.0 \ --log-interval 100 \ --save-interval 10000 \ --eval-interval 1000 \ --eval-iters 10 \ --fp16