LLaMA-Factory-310P3/prepare_yaml_file.py

import sys
import yaml

def main():
    run_type = sys.argv[1]
    model = sys.argv[2]
    max_steps = sys.argv[3]
    run_name = sys.argv[4]
    output_dir = sys.argv[5]

    if run_type == "lora_sft":
        yaml_file = './results/lora_sft_template.yaml'
    elif run_type == "inference":
        yaml_file = './results/predict_template.yaml'
    
    model_name_or_path = ""
    template = ""
    if model == "9g-8B":
        model_name_or_path = "ms_cache/hub/qy/sft_8b_v2"
        template = "default"
    elif model == "Baichuan2-7B":
        model_name_or_path = "ms_cache/hub/baichuan-inc/Baichuan2-7B-Base"
        template = "baichuan2"
    elif model == "ChatGLM2-6B":
        model_name_or_path = "ms_cache/hub/zhipuai/chatglm2-6b"
        template = "chatglm2"
    elif model == "Llama2-7B":
        model_name_or_path = "ms_cache/hub/modelscope/llama-2-7b-ms"
        template = "llama2"
    elif model == "Qwen-7B":
        model_name_or_path = "../../../models/qwen"
        template = "qwen"
    else:
        print("ERROR: model not supported.")
        sys.exit()
        
    config = None
    with open(yaml_file, 'r', encoding='utf-8') as f:
        config = yaml.load(f.read(), Loader=yaml.FullLoader)
    
    
    config['model_name_or_path'] = model_name_or_path
    config['template'] = template
    config['output_dir'] = output_dir
    if run_type == "lora_sft":
        config['max_steps'] = int(max_steps)

    with open(f'{output_dir}/{run_name}.yaml', 'w', encoding='utf-8') as f:
        yaml.dump(data=config, stream=f, allow_unicode=True)
    
    print(f"yaml file saved to {output_dir}/{run_name}.yaml")

if __name__ == "__main__":
    main()