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https://github.com/hiyouga/LLaMA-Factory.git
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41 lines
1.1 KiB
Bash
41 lines
1.1 KiB
Bash
#!/bin/bash
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pip install "transformers>=4.39.1"
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pip install "accelerate>=0.28.0"
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pip install "bitsandbytes>=0.43.0"
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CUDA_VISIBLE_DEVICES=0,1 accelerate launch \
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--config_file ../../accelerate/fsdp_config.yaml \
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../../../src/train_bash.py \
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--stage sft \
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--do_train \
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--model_name_or_path meta-llama/Llama-2-70b-hf \
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--dataset alpaca_gpt4_en,glaive_toolcall \
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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 q_proj,v_proj \
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--output_dir ../../../saves/LLaMA2-70B/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 1 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 4 \
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--lr_scheduler_type cosine \
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--logging_steps 10 \
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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 3.0 \
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--max_samples 3000 \
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--val_size 0.1 \
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--ddp_timeout 180000000 \
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--quantization_bit 4 \
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--plot_loss \
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--fp16
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