wehub-resource-sync
42 lines
1.5 KiB
Bash
42 lines
1.5 KiB
Bash
# 8 * 60GiB; 9s/it
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# For ease of use, we use moonshotai/Moonlight-16B-A3B-Instruct, which is also based on the DeepseekV3ForCausalLM architecture.
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# https://modelscope.cn/models/moonshotai/Moonlight-16B-A3B-Instruct/file/view/master/config.json?status=1
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PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
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NPROC_PER_NODE=8 \
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
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megatron sft \
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--model moonshotai/Moonlight-16B-A3B-Instruct \
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--save_safetensors true \
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--dataset 'liucong/Chinese-DeepSeek-R1-Distill-data-110k-SFT' \
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--load_from_cache_file true \
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--split_dataset_ratio 0.01 \
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--pipeline_model_parallel_size 2 \
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--decoder_last_pipeline_num_layers 13 \
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--moe_permute_fusion true \
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--expert_model_parallel_size 4 \
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--moe_grouped_gemm true \
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--moe_shared_expert_overlap true \
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--moe_aux_loss_coeff 1e-3 \
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--micro_batch_size 1 \
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--global_batch_size 16 \
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--packing true \
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--recompute_granularity full \
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--recompute_method uniform \
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--recompute_num_layers 1 \
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--num_train_epochs 1 \
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--finetune true \
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--cross_entropy_loss_fusion true \
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--lr 1e-5 \
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--lr_warmup_fraction 0.05 \
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--min_lr 1e-6 \
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--output_dir megatron_output/Moonlight-16B-A3B-Instruct \
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--eval_steps 200 \
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--save_steps 200 \
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--max_length 8192 \
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--dataloader_num_workers 8 \
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--dataset_num_proc 8 \
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--no_save_optim true \
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--no_save_rng true \
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--sequence_parallel true \
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--attention_backend flash
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