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# loss_scale all to train all tokens
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# use loss_type loss_scale
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# This is just an example
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CUDA_VISIBLE_DEVICES=0 \
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swift sft \
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--model Qwen/Qwen2.5-7B-Instruct \
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--tuner_type lora \
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--dataset 'swift/self-cognition#1000' \
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--num_train_epochs 1 \
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--per_device_train_batch_size 1 \
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--learning_rate 1e-4 \
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--lora_rank 8 \
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--lora_alpha 32 \
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--gradient_accumulation_steps 16 \
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--eval_steps 100 \
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--save_steps 100 \
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--save_total_limit 2 \
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--logging_steps 5 \
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--model_author swift \
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--model_name swift-robot \
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--loss_scale all \
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--loss_type loss_scale
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# `--tuner_type dummy`
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CUDA_VISIBLE_DEVICES=0 \
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swift sft \
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--model LLM-Research/Phi-4-multimodal-instruct \
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--dataset 'AI-ModelScope/LaTeX_OCR:human_handwrite#20000' \
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--load_from_cache_file true \
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--split_dataset_ratio 0.01 \
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--tuner_type dummy \
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--torch_dtype bfloat16 \
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--num_train_epochs 1 \
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--per_device_train_batch_size 1 \
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--per_device_eval_batch_size 1 \
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--learning_rate 1e-4 \
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--gradient_accumulation_steps 16 \
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--eval_steps 200 \
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--save_steps 200 \
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--save_total_limit 2 \
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--logging_steps 5 \
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--max_length 2048 \
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--output_dir output \
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--warmup_ratio 0.05 \
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--dataloader_num_workers 4
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