# For more information on multi-node training launch methods, refer to:
# https://github.com/modelscope/ms-swift/tree/main/examples/train/multi-node

CUDA_VISIBLE_DEVICES=0,1,2,3 \
NNODES=2 \
NODE_RANK=0 \
MASTER_ADDR=127.0.0.1 \
MASTER_PORT=29500 \
NPROC_PER_NODE=4 \
megatron sft \
    --load Qwen2.5-14B-mcore \
    --dataset 'liucong/Chinese-DeepSeek-R1-Distill-data-110k-SFT' \
    --tensor_model_parallel_size 4 \
    --micro_batch_size 1 \
    --global_batch_size 16 \
    --packing true \
    --recompute_granularity selective \
    --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/Qwen2.5-14B \
    --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