autoaudit_20230714_attempt2

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_attempt2",
# training hyperparams
batch_size: int = 4,
micro_batch_size: int = 1,
num_epochs: int = 28,
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