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sft.sh
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sft.sh
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# Experimental environment: A10, A100
# 16GB GPU memory
PYTHONPATH=../../.. \
CUDA_VISIBLE_DEVICES=0 \
python llm_sft.py \
--model_type yi-6b-chat \
--lora_lr_ratio 16.0 \
--sft_type lora \
--tuner_backend swift \
--dtype AUTO \
--output_dir output \
--dataset ms-agent \
--use_loss_scale true \
--train_dataset_sample 50000 \
--train_dataset_mix_ratio 2.0 \
--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 16 \
--max_grad_norm 0.5 \
--warmup_ratio 0.03 \
--eval_steps 500 \
--save_steps 500 \
--save_total_limit 2 \
--logging_steps 10 \