LLaMA-Factory/src/llamafactory/hparams/generating_args.py
hoshi-hiyouga a710d97748 Update generating_args.py
Former-commit-id: a1fa7aa63b9b3fade3de6bd27395c1b94068b6d2
2024-05-20 00:29:31 +08:00

61 lines
2.0 KiB
Python

from dataclasses import asdict, dataclass, field
from typing import Any, Dict, Optional
@dataclass
class GeneratingArguments:
r"""
Arguments pertaining to specify the decoding parameters.
"""
do_sample: bool = field(
default=True,
metadata={"help": "Whether or not to use sampling, use greedy decoding otherwise."},
)
temperature: float = field(
default=0.95,
metadata={"help": "The value used to modulate the next token probabilities."},
)
top_p: float = field(
default=0.7,
metadata={
"help": "The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept."
},
)
top_k: int = field(
default=50,
metadata={"help": "The number of highest probability vocabulary tokens to keep for top-k filtering."},
)
num_beams: int = field(
default=1,
metadata={"help": "Number of beams for beam search. 1 means no beam search."},
)
max_length: int = field(
default=1024,
metadata={"help": "The maximum length the generated tokens can have. It can be overridden by max_new_tokens."},
)
max_new_tokens: int = field(
default=1024,
metadata={"help": "The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt."},
)
repetition_penalty: float = field(
default=1.0,
metadata={"help": "The parameter for repetition penalty. 1.0 means no penalty."},
)
length_penalty: float = field(
default=1.0,
metadata={"help": "Exponential penalty to the length that is used with beam-based generation."},
)
default_system: Optional[str] = field(
default=None,
metadata={"help": "Default system message to use in chat completion."},
)
def to_dict(self) -> Dict[str, Any]:
args = asdict(self)
if args.get("max_new_tokens", -1) > 0:
args.pop("max_length", None)
else:
args.pop("max_new_tokens", None)
return args