LLaMA-Factory/examples/extras/llama_pro/llama3_freeze_sft.yaml

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### model
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model_name_or_path: models/llama3-8b-instruct-pro
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### method
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stage: sft
do_train: true
finetuning_type: freeze
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freeze_trainable_layers: 8
freeze_trainable_modules: all
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use_llama_pro: true
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### dataset
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dataset: identity,alpaca_gpt4_en
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
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### output
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output_dir: saves/llama3-8b-instruct-pro/freeze/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
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### train
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per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
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
evaluation_strategy: steps
eval_steps: 500