chore: import upstream snapshot with attribution
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wehub-resource-sync
2026-07-13 13:34:58 +08:00
commit a203934033
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# loss_scale all to train all tokens
# use loss_type loss_scale
# This is just an example
CUDA_VISIBLE_DEVICES=0 \
swift sft \
--model Qwen/Qwen2.5-7B-Instruct \
--tuner_type lora \
--dataset 'swift/self-cognition#1000' \
--num_train_epochs 1 \
--per_device_train_batch_size 1 \
--learning_rate 1e-4 \
--lora_rank 8 \
--lora_alpha 32 \
--gradient_accumulation_steps 16 \
--eval_steps 100 \
--save_steps 100 \
--save_total_limit 2 \
--logging_steps 5 \
--model_author swift \
--model_name swift-robot \
--loss_scale all \
--loss_type loss_scale
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# `--tuner_type dummy`
CUDA_VISIBLE_DEVICES=0 \
swift sft \
--model LLM-Research/Phi-4-multimodal-instruct \
--dataset 'AI-ModelScope/LaTeX_OCR:human_handwrite#20000' \
--load_from_cache_file true \
--split_dataset_ratio 0.01 \
--tuner_type dummy \
--torch_dtype bfloat16 \
--num_train_epochs 1 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--learning_rate 1e-4 \
--gradient_accumulation_steps 16 \
--eval_steps 200 \
--save_steps 200 \
--save_total_limit 2 \
--logging_steps 5 \
--max_length 2048 \
--output_dir output \
--warmup_ratio 0.05 \
--dataloader_num_workers 4