wehub-resource-sync
36 lines
1.2 KiB
Bash
36 lines
1.2 KiB
Bash
# 4 * 36GB
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# Efficiency: With packing: 10 minutes; Without packing: >=1 hour
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# For local datasets, it is recommended to use streaming: `--streaming true` (save memory)
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# You can also use padding_free to avoid the space/time cost caused by multi-modal packing:
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# https://github.com/modelscope/ms-swift/blob/main/examples/train/padding_free/sft.sh
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NPROC_PER_NODE=4 \
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MAX_PIXELS=1003520 \
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CUDA_VISIBLE_DEVICES=0,1,2,3 \
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swift sft \
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--model Qwen/Qwen2.5-VL-7B-Instruct \
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--tuner_type lora \
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--dataset 'AI-ModelScope/LaTeX_OCR#20000' \
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--load_from_cache_file true \
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--split_dataset_ratio 0.01 \
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--torch_dtype bfloat16 \
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--attn_impl flash_attn \
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--packing true \
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--num_train_epochs 3 \
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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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--target_modules all-linear \
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--gradient_accumulation_steps 1 \
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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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--max_length 8192 \
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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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--dataset_num_proc 8 \
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--deepspeed zero2
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