| | 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: Optional[bool] = field( |
| | default=True, |
| | metadata={"help": "Whether or not to use sampling, use greedy decoding otherwise."}, |
| | ) |
| | temperature: Optional[float] = field( |
| | default=0.95, |
| | metadata={"help": "The value used to modulate the next token probabilities."}, |
| | ) |
| | top_p: Optional[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: Optional[int] = field( |
| | default=50, |
| | metadata={"help": "The number of highest probability vocabulary tokens to keep for top-k filtering."}, |
| | ) |
| | num_beams: Optional[int] = field( |
| | default=1, |
| | metadata={"help": "Number of beams for beam search. 1 means no beam search."}, |
| | ) |
| | max_length: Optional[int] = field( |
| | default=512, |
| | metadata={"help": "The maximum length the generated tokens can have. It can be overridden by max_new_tokens."}, |
| | ) |
| | max_new_tokens: Optional[int] = field( |
| | default=512, |
| | metadata={"help": "The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt."}, |
| | ) |
| | repetition_penalty: Optional[float] = field( |
| | default=1.0, |
| | metadata={"help": "The parameter for repetition penalty. 1.0 means no penalty."}, |
| | ) |
| | length_penalty: Optional[float] = field( |
| | default=1.0, |
| | metadata={"help": "Exponential penalty to the length that is used with beam-based generation."}, |
| | ) |
| |
|
| | 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 |
| |
|