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### model
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model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
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### method
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stage: sft
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do_train: true
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finetuning_type: lora
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lora_target: q_proj,v_proj
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loraplus_lr_ratio: 16.0
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### dataset
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dataset: identity,alpaca_gpt4_en
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template: llama3
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cutoff_len: 1024
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max_samples: 1000
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overwrite_cache: true
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preprocessing_num_workers: 16
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### output
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output_dir: saves/llama3-8b/lora/sft
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logging_steps: 10
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save_steps: 500
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plot_loss: true
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overwrite_output_dir: true
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### train
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 8
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learning_rate: 0.0001
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num_train_epochs: 3.0
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lr_scheduler_type: cosine
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warmup_steps: 0.1
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fp16: true
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### eval
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val_size: 0.1
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per_device_eval_batch_size: 1
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evaluation_strategy: steps
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eval_steps: 500
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