This model is a finetuned version run by the finetune.py in github repository tolen/alpaca-lora with the following parameters, notice that the training dataset can be found in repository:https://github.com/ddzipp/AutoAudit_LLM_Dataset 

    # model/data params
    base_model: str = "yahma/llama-7b-hf",  
    data_path: str = "",  # dataset see repository https://github.com/ddzipp/AutoAudit_LLM_Dataset/tree/v0.0.1
    output_dir: str = "./autoaudit_20230703_attempt1",
    # training hyperparams
    batch_size: int = 4,
    micro_batch_size: int = 1,
    num_epochs: int = 14,
    learning_rate: float = 3e-4,
    cutoff_len: int = 512,
    val_set_size: int = 400,
    # lora hyperparams
    lora_r: int = 16,
    lora_alpha: int = 16,
    lora_dropout: float = 0.05,
    lora_target_modules: List[str] = [
        "q_proj",
        "k_proj",
        "v_proj",
        "o_proj"
    ],
    # llm hyperparams
    train_on_inputs: bool = True,  # if False, masks out inputs in loss
    add_eos_token: bool = False,
    group_by_length: bool = False,  # faster, but produces an odd training loss curve