mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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32 lines
890 B
Bash
32 lines
890 B
Bash
#!/bin/bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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--stage sft_mm \
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--do_train \
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--model_name_or_path llava-hf/llava-1.5-7b-hf \
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--dataset mllm_instruct_example \
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--dataset_dir data \
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--template default \
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--finetuning_type lora \
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--lora_target all \
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--output_dir saves/llava-1.5-7b/lora/sft \
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--overwrite_cache \
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--overwrite_output_dir \
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--cutoff_len 1024 \
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--preprocessing_num_workers 16 \
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--per_device_train_batch_size 3 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 1 \
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--lr_scheduler_type cosine \
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--logging_steps 1 \
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--warmup_steps 20 \
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--save_steps 100 \
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--eval_steps 100 \
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--evaluation_strategy steps \
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--load_best_model_at_end \
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--learning_rate 5e-5 \
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--num_train_epochs 100 \
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--max_samples 3000 \
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--val_size 0.1 \
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--plot_loss \
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--bf16 |