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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.
import warnings
from dataclasses import dataclass, field
from typing import Any, Optional
from transformers import TrainingArguments
@dataclass
class SFTConfig(TrainingArguments):
r"""
Configuration class for the [`SFTTrainer`].
This class includes only the parameters that are specific to 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 [`SFTTrainer`] is provided as a string.
> Parameters that control the data preprocessing
dataset_text_field (`str`, *optional*, defaults to `"text"`):
Name of the column that contains text data in the dataset.
dataset_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
Dictionary of optional keyword arguments for the dataset preparation. The only supported key is
`skip_prepare_dataset`.
dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
Number of processes to use for processing the dataset.
eos_token (`str` or `None`, *optional*, defaults to `None`):
Token used to indicate the end of a turn or sequence. If `None`, it defaults to `processing_class.eos_token`.
pad_token (`int` or `None`, *optional*, defaults to `None`):
Token used for padding. If `None`, it defaults to `processing_class.pad_token`, or if that is also `None`,
it falls back to `processing_class.eos_token`.
max_length (`int` or `None`, *optional*, defaults to `1024`):
Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated from the right.
If `None`, no truncation is applied. When packing is enabled, this value sets the sequence length.
packing (`bool`, *optional*, defaults to `False`):
Whether to group multiple sequences into fixed-length blocks to improve computational efficiency and reduce
padding. Uses `max_length` to define sequence length.
packing_strategy (`str`, *optional*, defaults to `"ffd"`):
Strategy for packing sequences. Can be either `"ffd"` (first-fit decreasing, default), or `"wrapped"`.
padding_free (`bool`, *optional*, defaults to `False`):
Whether to perform forward passes without padding by flattening all sequences in the batch into a single
continuous sequence. This reduces memory usage by eliminating padding overhead. Currently, this is only
supported with the `flash_attention_2` attention implementation, which can efficiently handle the flattened
batch structure. When packing is enabled with strategy `"ffd"`, padding-free is enabled, regardless of the
value of this parameter.
pad_to_multiple_of (`int` or `None`, *optional*, defaults to `None`):
If set, the sequences will be padded to a multiple of this value.
eval_packing (`bool` or `None`, *optional*, defaults to `None`):
Whether to pack the eval dataset. If `None`, uses the same value as `packing`.
> Parameters that control the training
completion_only_loss (`bool` or `None`, *optional*, defaults to `None`):
Whether to compute loss only on the completion part of the sequence. If set to `True`, loss is computed
only on the completion, which is supported only for [prompt-completion](#prompt-completion) datasets. If
`False`, loss is computed on the entire sequence. If `None` (default), the behavior depends on the dataset:
loss is computed on the completion for [prompt-completion](#prompt-completion) datasets, and on
the full sequence for [language modeling](#language-modeling) datasets.
activation_offloading (`bool`, *optional*, defaults to `False`):
Whether to offload the activations to the CPU.
"""
_VALID_DICT_FIELDS = TrainingArguments._VALID_DICT_FIELDS + ["model_init_kwargs"]
# Parameters whose default values are overridden from TrainingArguments
learning_rate: float = field(
default=2e-5,
metadata={"help": "The initial learning rate for AdamW."},
)
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."
)
},
)
average_tokens_across_devices: bool = field(
default=True,
metadata={
"help": "Whether or not to average tokens across devices. If enabled, will use all_reduce to synchronize "
"num_tokens_in_batch for precise loss calculation. Reference: https://github.com/huggingface/transformers/issues/34242 "
},
)
# 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 `SFTTrainer` is provided as a string."
},
)
# Parameters that control the data preprocessing
dataset_text_field: str = field(
default="text",
metadata={"help": "Name of the column that contains text data in the dataset."},
)
dataset_kwargs: Optional[dict[str, Any]] = field(
default=None,
metadata={
"help": "Dictionary of optional keyword arguments for the dataset preparation. The only supported key is "
"`skip_prepare_dataset`."
},
)
dataset_num_proc: Optional[int] = field(
default=None,
metadata={"help": "Number of processes to use for processing the dataset."},
)
eos_token: Optional[str] = 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`."
},
)
pad_token: Optional[str] = field(
default=None,
metadata={
"help": "Token used for padding. If `None`, it defaults to `processing_class.pad_token`, or if that "
"is also `None`, it falls back to `processing_class.eos_token`."
},
)
max_length: Optional[int] = 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. When packing is enabled, this value sets the "
"sequence length."
},
)
packing: bool = field(
default=False,
metadata={
"help": "Whether to group multiple sequences into fixed-length blocks to improve computational efficiency "
"and reduce padding. Uses `max_length` to define sequence length."
},
)
packing_strategy: str = field(
default="ffd",
metadata={
"help": "Strategy for packing sequences. Can be either `'ffd'` (first-fit decreasing, default), or "
"`'wrapped'`."
},
)
padding_free: bool = field(
default=False,
metadata={
"help": "Whether to perform forward passes without padding by flattening all sequences in the batch into "
"a single continuous sequence. This reduces memory usage by eliminating padding overhead. Currently, "
"this is only supported with the `flash_attention_2` attention implementation, which can efficiently "
"handle the flattened batch structure. When packing is enabled with strategy `'ffd'`, padding-free is "
"enabled, regardless of the value of this parameter."
},
)
pad_to_multiple_of: Optional[int] = field(
default=None,
metadata={"help": "If set, the sequences will be padded to a multiple of this value."},
)
eval_packing: Optional[bool] = field(
default=None,
metadata={"help": "Whether to pack the eval dataset. If `None`, uses the same value as `packing`."},
)
# Parameters that control the training
completion_only_loss: Optional[bool] = field(
default=None,
metadata={
"help": (
"Whether to compute loss only on the completion part of the sequence. If set to `True`, loss is "
"computed only on the completion, which is supported only for prompt-completion datasets. If `False`, "
"loss is computed on the entire sequence. If `None` (default), the behavior depends on the dataset: "
"loss is computed on the completion for prompt-completion datasets, and on the full sequence for "
"language modeling datasets."
)
},
)
activation_offloading: bool = field(
default=False,
metadata={"help": "Whether to offload the activations to the CPU."},
)
# Deprecated parameters
max_seq_length: Optional[int] = field(
default=None,
metadata={
"help": "This parameter is deprecated and will be removed in version 0.20.0. Use `max_length` instead."
},
)
def __post_init__(self):
super().__post_init__()
if self.max_seq_length is not None:
warnings.warn(
"`max_seq_length` is deprecated and will be removed in version 0.20.0. Use `max_length` instead.",
DeprecationWarning,
)
self.max_length = self.max_seq_length
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