mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-05 13:12:53 +08:00
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
from dataclasses import asdict, dataclass, field
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from typing import Any, Dict
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@dataclass
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class GeneratingArguments:
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r"""
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Arguments pertaining to specify the decoding parameters.
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"""
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do_sample: bool = field(
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default=True,
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metadata={"help": "Whether or not to use sampling, use greedy decoding otherwise."},
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)
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temperature: float = field(
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default=0.95,
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metadata={"help": "The value used to modulate the next token probabilities."},
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)
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top_p: float = field(
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default=0.7,
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metadata={
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"help": "The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept."
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},
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)
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top_k: int = field(
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default=50,
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metadata={"help": "The number of highest probability vocabulary tokens to keep for top-k filtering."},
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)
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num_beams: int = field(
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default=1,
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metadata={"help": "Number of beams for beam search. 1 means no beam search."},
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)
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max_length: int = field(
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default=512,
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metadata={"help": "The maximum length the generated tokens can have. It can be overridden by max_new_tokens."},
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)
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max_new_tokens: int = field(
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default=512,
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metadata={"help": "The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt."},
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)
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repetition_penalty: float = field(
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default=1.0,
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metadata={"help": "The parameter for repetition penalty. 1.0 means no penalty."},
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)
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length_penalty: float = field(
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default=1.0,
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metadata={"help": "Exponential penalty to the length that is used with beam-based generation."},
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)
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def to_dict(self) -> Dict[str, Any]:
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args = asdict(self)
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if args.get("max_new_tokens", -1) > 0:
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args.pop("max_length", None)
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else:
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args.pop("max_new_tokens", None)
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return args
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