mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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1fee69f874
* fix llamapro script * change year Former-commit-id: e2dc5b952aa22835d5220ba624f44676138b65ac
88 lines
3.0 KiB
Python
88 lines
3.0 KiB
Python
# Copyright 2025 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import asdict, dataclass, field
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from typing import Any, Dict, Optional
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from transformers import GenerationConfig
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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=1024,
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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=1024,
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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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default_system: Optional[str] = field(
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default=None,
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metadata={"help": "Default system message to use in chat completion."},
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)
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skip_special_tokens: bool = field(
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default=True,
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metadata={"help": "Whether or not to remove special tokens in the decoding."},
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)
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def to_dict(self, obey_generation_config: bool = False) -> 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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if obey_generation_config:
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generation_config = GenerationConfig()
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for key in list(args.keys()):
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if not hasattr(generation_config, key):
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args.pop(key)
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return args
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