wehub-resource-sync
41 lines
2.2 KiB
Bash
41 lines
2.2 KiB
Bash
# 22GB
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# Change: https://github.com/modelscope/ms-swift/blob/main/swift/callbacks/early_stop.py
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# If you have custom implementations
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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 'AI-ModelScope/alpaca-gpt4-data-zh#500' \
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'AI-ModelScope/alpaca-gpt4-data-en#500' \
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'swift/self-cognition#500' \
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--split_dataset_ratio 0.1 \
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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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--lora_rank 8 \
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--lora_alpha 32 \
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--early_stop_interval 3 \
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--target_modules all-linear \
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--gradient_accumulation_steps 16 \
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--eval_steps 50 \
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--save_steps 50 \
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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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--system 'You are a helpful assistant.' \
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--warmup_ratio 0.05 \
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--dataloader_num_workers 4 \
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--model_author swift \
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--model_name swift-robot \
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--metric_for_best_model loss \
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# a sample result
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# Train: 83%|██████████████████████████████████████████████████████████████████████████████████████████▊ | 10/12 [00:42<00:06, 3.14s/it]
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#{'eval_loss': 4.26491737, 'eval_token_acc': 0.57142857, 'eval_runtime': 20.3945, 'eval_samples_per_second': 0.049, 'eval_steps_per_second': 0.049, 'epoch': 2.5, 'global_step/max_steps': '10/12', 'percentage': '83.33%', 'elapsed_time': '1m 2s', 'remaining_time': '12s'}
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#Val: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 28.85it/s]
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#[INFO:swift] Saving model checkpoint to output/xxx/checkpoint-10
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#[INFO:swift] Training stop because of eval metric is stable at step 10
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