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# Copyright 2020-2025 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.
from dataclasses import dataclass, field
from typing import Any, Optional
from transformers import TrainingArguments
@dataclass
class IterativeSFTConfig(TrainingArguments):
r"""
Configuration class for the [`IterativeSFTTrainer`].
This class includes only the parameters that are specific to Iterative SFT 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]` or `None`, *optional*, defaults to `None`):
Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
argument of the [`IterativeSFTTrainer`] is provided as a string.
> Parameters that control the data preprocessing
max_length (`int` or `None`, *optional*, defaults to `None`):
Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated.
truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
The truncation mode to use, either `"keep_end"` or `"keep_start"`.
optimize_device_cache (`bool`, *optional*, defaults to `False`):
Whether to optimize accelerator cache for slightly more memory-efficient training.
"""
_VALID_DICT_FIELDS = TrainingArguments._VALID_DICT_FIELDS + ["model_init_kwargs"]
# Parameters whose default values are overridden from TrainingArguments
logging_steps: float = field(
default=10,
metadata={
"help": (
"Log every X updates steps. Should be an integer or a float in range `[0,1)`. "
"If smaller than 1, will be interpreted as ratio of total training steps."
)
},
)
bf16: bool = field(
default=True,
metadata={
"help": (
"Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA "
"architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change."
)
},
)
# Parameters that control the model
model_init_kwargs: Optional[dict[str, Any]] = field(
default=None,
metadata={
"help": "Keyword arguments for `AutoModelForCausalLM.from_pretrained`, used when the `model` argument of "
"the `IterativeSFTTrainer` is provided as a string."
},
)
# Parameters that control the data preprocessing
max_length: Optional[int] = field(
default=None,
metadata={
"help": "Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated."
},
)
truncation_mode: str = field(
default="keep_end",
metadata={"help": "The truncation mode to use, either 'keep_end' or 'keep_start'."},
)
optimize_device_cache: bool = field(
default=False,
metadata={"help": "Whether to optimize accelerator cache for slightly more memory-efficient training."},
)
def __post_init__(self):
super().__post_init__()
if self.truncation_mode not in ["keep_end", "keep_start"]:
raise ValueError(f"truncation_mode must be either 'keep_end' or 'keep_start', got {self.truncation_mode}")
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