# Copyright 2020-2026 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from dataclasses import dataclass, field from typing import Any from .base_config import _BaseConfig @dataclass class RewardConfig(_BaseConfig): # docstyle-ignore r""" Configuration class for the [`RewardTrainer`]. This class includes only the parameters that are specific to Reward training. For a full list of training arguments, please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may differ from those in [`~transformers.TrainingArguments`]. Using [`~transformers.HfArgumentParser`] we can turn this class into [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the command line. Parameters: > Parameters that control the model model_init_kwargs (`dict[str, Any]`, *optional*): Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model` argument of the [`RewardTrainer`] is provided as a string. If you're training a MoE architecture and want to include the load balancing/auxiliary loss as a part of the final loss, remember to set `output_router_logits=True` in this dictionary. chat_template_path (`str`, *optional*): If specified, sets the model's chat template. This can either be the path to a tokenizer (local directory or Hugging Face Hub model) or a direct path to a Jinja template file. When using a Jinja file, you must ensure that any special tokens referenced in the template are added to the tokenizer and that the model's embedding layer is resized accordingly. disable_dropout (`bool`, *optional*, defaults to `True`): Whether to disable dropout in the model. > Parameters that control the data preprocessing dataset_num_proc (`int`, *optional*): Number of processes to use for processing the dataset. eos_token (`str`, *optional*): Token used to indicate the end of a turn or sequence. If `None`, it defaults to `processing_class.eos_token`. max_length (`int` or `None`, *optional*, defaults to `1024`): Maximum length of the tokenized sequence. Samples are filtered out if either chosen or rejected sequence exceeds this value. If `None`, no filtering is applied. pad_to_multiple_of (`int`, *optional*): If set, the sequences will be padded to a multiple of this value. > Parameters that control the training center_rewards_coefficient (`float`, *optional*): Coefficient to incentivize the reward model to output mean-zero rewards (proposed by https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`. activation_offloading (`bool`, *optional*, defaults to `False`): Whether to offload the activations to the CPU. > Deprecated parameters pad_token: Parameter `pad_token` is deprecated and will be removed in version v2.0.0. Set `tokenizer.pad_token` directly and pass it as `processing_class` to the trainer instead. > [!NOTE] > These parameters have default values different from [`~transformers.TrainingArguments`]: > - `logging_steps`: Defaults to `10` instead of `500`. > - `gradient_checkpointing`: Defaults to `True` instead of `False`. > - `bf16`: Defaults to `True` if `fp16` is not set, instead of `False`. > - `learning_rate`: Defaults to `1e-4` instead of `5e-5`. """ _VALID_DICT_FIELDS = _BaseConfig._VALID_DICT_FIELDS + ["model_init_kwargs"] # Parameters whose default values are overridden from TrainingArguments learning_rate: float = field( default=1e-4, metadata={"help": "The initial learning rate for AdamW."}, ) # Parameters that control the model model_init_kwargs: dict[str, Any] | str | None = field( default=None, metadata={ "help": "Keyword arguments for `AutoModelForCausalLM.from_pretrained`, used when the `model` argument of " "the `RewardTrainer` is provided as a string. If you're training a MoE architecture and want to include " "the load balancing/auxiliary loss as a part of the final loss, remember to set " "`output_router_logits=True` in this dictionary." }, ) chat_template_path: str | None = field( default=None, metadata={ "help": "If specified, sets the model's chat template. This can either be the path to a tokenizer (local " "directory or Hugging Face Hub model) or a direct path to a Jinja template file. When using a Jinja file, " "you must ensure that any special tokens referenced in the template are added to the tokenizer and " "that the model's embedding layer is resized accordingly." }, ) disable_dropout: bool = field( default=True, metadata={"help": "Whether to disable dropout in the model."}, ) # Parameters that control the data preprocessing dataset_num_proc: int | None = field( default=None, metadata={"help": "Number of processes to use for processing the dataset."}, ) eos_token: str | None = field( default=None, metadata={ "help": "Token used to indicate the end of a turn or sequence. If `None`, it defaults to `processing_class.eos_token`." }, ) max_length: int | None = field( default=1024, metadata={ "help": "Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated from " "the right. If `None`, no truncation is applied." }, ) pad_to_multiple_of: int | None = field( default=None, metadata={"help": "If set, the sequences will be padded to a multiple of this value."}, ) # Parameters that control the training center_rewards_coefficient: float | None = field( default=None, metadata={ "help": "Coefficient to incentivize the reward model to output mean-zero rewards (proposed by " "https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`." }, ) activation_offloading: bool = field( default=False, metadata={"help": "Whether to offload the activations to the CPU."}, ) # Deprecated parameters pad_token: str | None = field( default=None, metadata={ "help": "Deprecated. Set `tokenizer.pad_token` directly and pass it as `processing_class` to the trainer instead." }, ) def __post_init__(self): super().__post_init__() if self.pad_token is not None: warnings.warn( "`pad_token` is deprecated and will be removed in v2.0.0. " "Set `tokenizer.pad_token` directly and pass it as `processing_class` to the trainer instead.", FutureWarning, stacklevel=3, )