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sft.sh
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sft.sh
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# Experimental environment: 4*A100
# 4*75GB GPU memory
nproc_per_node=4
CUDA_VISIBLE_DEVICES=0,1,2,3\
NPROC_PER_NODE=$nproc_per_node \
swift sft \
--model_type qwen1half-110b-chat \
--sft_type lora \
--tuner_backend peft \
--dtype AUTO \
--output_dir output \
--ddp_backend nccl \
--dataset alpaca-zh \
--train_dataset_sample -1 \
--num_train_epochs 2 \
--max_length 2048 \
--check_dataset_strategy warning \
--lora_rank 8 \
--lora_alpha 32 \
--lora_dropout_p 0.05 \
--lora_target_modules ALL \
--gradient_checkpointing true \
--batch_size 1 \
--weight_decay 0.1 \
--learning_rate 1e-4 \
--gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
--max_grad_norm 0.5 \
--warmup_ratio 0.03 \
--eval_steps 100 \
--save_steps 100 \
--save_total_limit 2 \
--logging_steps 10 \
--use_flash_attn true \
--deepspeed default-zero3