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from dataclasses import dataclass, field
@dataclass
class EvalArguments:
model_name_or_path: str = field(
default="mistralai/Mistral-7B-v0.1", metadata={"help": "Name to a huggingface native pretrained model or path to a model on disk."})
model_pretrained_lora_weights: str = field(
default=None, metadata={"help": "Path to a checkpoint directory."})
output_filepath: str = field(
default="rewards_examples_idan_mini.json", metadata={"help": "Path to the decode result or to a dir containing such files."}) # ADD output filepath
result_filename: str = field(
default=None, metadata={"help": "The path to the result json file. If not provided, will automatically create one. "})
per_device_batch_size: int = field(
default=12, metadata={"help": "The path to the output json file."})
flash_attn: bool = field(default=False, metadata={"help": "If True, uses Flash Attention."})
bfloat16: bool = field(
default=False, metadata={"help": "If True, uses bfloat16. If lora and four_bits are True, bfloat16 is used for the lora weights."})
# peft / quantization
use_lora: bool = field(default=False, metadata={"help": "If True, uses LoRA."})
load_in_4bit: bool = field(default=False, metadata={"help": "If True, uses 4-bit quantization."})
load_in_8bit: bool = field(default=False, metadata={"help": "If True, uses 8-bit quantization."})
# reward model specific args
reward_output_fmt: str = field(default=None, metadata={"help": "If 0, takes the softmax-ed output at index 0. If 1-0, takes the softmax-ed output at index 1 - index 0. Otherwise, just takes the raw output."})
soft_preference: bool = field(default=False, metadata={"help": "If True, uses soft preference."})
apply_sigmoid_to_reward: bool = field(default=False, metadata={"help": "If True, applies sigmoid to the reward."})
transformer_cache_dir: str = field(
default=None,
metadata={
"help": "Path to a directory where transformers will cache the model. "
"If None, transformers will use the default cache directory."
},)
use_fast_tokenizer: bool = field(
default=True,
metadata={
"help": "Use fast tokenizer if True. "
"Fast LLaMA tokenizer forces protobuf downgrade to 3.20.3. "
"Use fast tokenizer only if you can live with that."
},
)
trust_remote_code: bool = field(default=False, metadata={"help": "If True, enables unpickling of arbitrary code in AutoModelForCausalLM#from_pretrained."})
def __post_init__(self):
# separate multiple model names or paths by comma
if self.model_name_or_path is not None:
self.model_name_or_path = self.model_name_or_path.split(',')
# if loading 1 model, convert to string like normal
if len(self.model_name_or_path) == 1:
self.model_name_or_path = self.model_name_or_path[0]
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