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# use `--loss_scale ignore_empty_think`
# Avoid losing the think capability by ignoring the loss of empty `<think>\n\n</think>\n\n`
# This method is also applicable to the Deepseek-R1 series of models.
CUDA_VISIBLE_DEVICES=0 \
swift sft \
--model Qwen/Qwen3-8B \
--train_type lora \
--dataset 'swift/Qwen3-SFT-Mixin#2000' \
'swift/self-cognition:empty_think#600' \
--torch_dtype bfloat16 \
--num_train_epochs 1 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--learning_rate 1e-4 \
--lora_rank 8 \
--lora_alpha 32 \
--target_modules all-linear \
--gradient_accumulation_steps 16 \
--eval_steps 50 \
--save_steps 50 \
--save_total_limit 2 \
--logging_steps 5 \
--max_length 2048 \
--output_dir output \
--warmup_ratio 0.05 \
--dataloader_num_workers 4 \
--use_liger_kernel true \
--loss_scale ignore_empty_think \
--model_author swift \
--model_name swift-robot
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