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# ZeRO3: 91.2s/it; 16 * 80GiB
# Megatron-LM: 9.6s/it; 16 * 60GiB
# Launch using Alibaba Cloud DLC
# ref: https://github.com/modelscope/ms-swift/blob/main/examples/train/multi-node/dlc/train.sh
NNODES=$WORLD_SIZE \
NODE_RANK=$RANK \
megatron sft \
--load Qwen3-30B-A3B-Base-mcore \
--dataset 'liucong/Chinese-DeepSeek-R1-Distill-data-110k-SFT' \
--tensor_model_parallel_size 2 \
--expert_model_parallel_size 8 \
--moe_grouped_gemm true \
--moe_shared_expert_overlap true \
--moe_aux_loss_coeff 0.01 \
--micro_batch_size 1 \
--global_batch_size 16 \
--packing true \
--recompute_granularity full \
--recompute_method uniform \
--recompute_num_layers 1 \
--train_iters 2000 \
--eval_iters 50 \
--finetune true \
--cross_entropy_loss_fusion true \
--lr 1e-5 \
--lr_warmup_iters 100 \
--min_lr 1e-6 \
--save megatron_output/Qwen3-30B-A3B-Base \
--eval_interval 200 \
--save_interval 200 \
--max_length 8192 \
--num_workers 8 \
--dataset_num_proc 8 \
--no_save_optim true \
--no_save_rng true \
--sequence_parallel true \
--use_flash_attn true