mirror of
https://github.com/wassname/SimPO.git
synced 2026-06-27 21:35:33 +08:00
Yu Meng
73 lines
3.5 KiB
Python
73 lines
3.5 KiB
Python
from dataclasses import dataclass
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from typing import Dict, Literal, Optional
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from transformers import TrainingArguments
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@dataclass
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class SimPOConfig(TrainingArguments):
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r"""
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SimPOConfig collects all training arguments related to the [`SimPOTrainer`] class.
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Using [`HfArgumentParser`] we can turn this class into
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[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
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command line.
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Parameters:
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max_length (`int`, defaults to `None`):
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The maximum length of the sequences in the batch. This argument is required if you want to use the default data collator.
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max_prompt_length (`int`, defaults to `None`):
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The maximum length of the prompt. This argument is required if you want to use the default data collator.
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max_target_length (`int`, defaults to `None`):
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The maximum length of the target. This argument is required if you want to use the default data collator and your model is an encoder-decoder.
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beta (`float`, defaults to 2.0):
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The beta factor in SimPO loss.
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gamma_beta_ratio (`float`, defaults to 0.25):
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The ratio between the target reward margin (gamma) and beta in SimPO loss.
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sft_weight (`float`, defaults to 0.0):
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SFT loss weight added to the SimPO loss (0.0 is not using SFT).
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label_smoothing (`float`, defaults to 0):
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The label smoothing factor. This argument is required if you want to use the default data collator.
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loss_type (`str`, defaults to `sigmoid`):
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The type of loss to use. This argument is required if you want to use the default data collator.
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label_pad_token_id (`int`, defaults to `-100`):
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The label pad token id. This argument is required if you want to use the default data collator.
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padding_value (`int`, defaults to `None`):
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The padding value if it is different to the tokenizer's pad_token_id.
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truncation_mode (`str`, defaults to `keep_end`):
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The truncation mode to use, either `keep_end` or `keep_start`. This argument is required if you want to use the default data collator.
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generate_during_eval (`bool`, defaults to `False`):
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Whether to sample and log generations during evaluation step.
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is_encoder_decoder (`Optional[bool]`, `optional`, defaults to `None`):
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If no model is provided, we need to know if the model_init returns an encoder-decoder.
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disable_dropout (`bool`, defaults to `True`):
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Whether or not to disable dropouts in `model`.
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model_init_kwargs (`Optional[Dict]`, *optional*):
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Dict of Optional kwargs to pass when instantiating the model from a string
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dataset_num_proc (`Optional[int]`, *optional*):
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The number of workers to use to tokenize the data. Defaults to None.
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"""
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max_length: Optional[int] = None
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max_prompt_length: Optional[int] = None
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max_completion_length: Optional[int] = None
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max_target_length: Optional[int] = None
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beta: float = 2.0
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gamma_beta_ratio: float = 0.25
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sft_weight: float = 0.0
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label_smoothing: float = 0
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loss_type: Literal["sigmoid", "hinge"] = "sigmoid"
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disable_dropout: bool = True
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label_pad_token_id: int = -100
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padding_value: int = None
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truncation_mode: str = "keep_end"
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generate_during_eval: bool = False
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is_encoder_decoder: Optional[bool] = None
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model_init_kwargs: Optional[Dict] = None
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dataset_num_proc: Optional[int] = None
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attn_implementation: str = None
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