# 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 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. If you're training a MoE architecture and want to include the load balancing/auxilliary loss as a part of the final loss, remember to set `output_router_logits=True` in this dictionary. chat_template_path (`str` or `None`, *optional*, defaults to `None`): 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. > 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`. When the model is a VLM, `skip_prepare_dataset` is automatically treated as `True` regardless of the provided value, since preprocessing is done on the fly. 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 `"bfd"`): Strategy for packing sequences. Can be either `"bfd"` (best-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 FlashAttention 2 or 3, which can efficiently handle the flattened batch structure. When packing is enabled with strategy `"bfd"`, 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. assistant_only_loss (`bool`, *optional*, defaults to `False`): Whether to compute loss only on the assistant part of the sequence. If set to `True`, loss is computed only on the assistant responses, which is supported only for [conversational](#conversational) datasets. If `False`, loss is computed on the entire sequence. loss_type (`str`, *optional*, defaults to `"nll"`): Type of loss to use. Possible values are `"nll"` (negative log-likelihood, default) and `"dft"` (Dynamic Fine-Tuning, as described in [this paper](https://huggingface.co/papers/2508.05629)). 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." }, ) gradient_checkpointing: bool = field( default=True, metadata={ "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass." }, ) bf16: Optional[bool] = field( default=None, metadata={ "help": "Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA " "architecture or Intel XPU or using CPU (use_cpu) or Ascend NPU. If not set, it defaults to `True` if " "`fp16` is not set." }, ) # 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. If you're training a MoE architecture and want to include the " "load balancing/auxilliary loss as a part of the final loss, remember to set `output_router_logits=True` " "in this dictionary." }, ) chat_template_path: Optional[str] = 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." }, ) # 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`. If the model is a VLM, `skip_prepare_dataset` value is ignored. When the model " "is a VLM, `skip_prepare_dataset` is automatically treated as `True` regardless of the provided value, " "since preprocessing is done on the fly." }, ) 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="bfd", metadata={ "help": "Strategy for packing sequences. Can be either `'bfd'` (best-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 FlashAttention 2 or 3, which can efficiently handle the flattened batch " "structure. When packing is enabled with strategy `'bfd'`, 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." ) }, ) assistant_only_loss: bool = field( default=False, metadata={ "help": ( "Whether to compute loss only on the assistant part of the sequence. If set to `True`, loss is " "computed only on the assistant responses, which is supported only for conversational datasets. If `False`, " "loss is computed on the entire sequence." ) }, ) loss_type: str = field( default="nll", metadata={ "help": ( 'Type of loss to use. Possible values are `"nll"` (negative log-likelihood, default) and `"dft"` ' "(Dynamic Fine-Tuning, as described in https://huggingface.co/papers/2508.05629)." ) }, ) activation_offloading: bool = field( default=False, metadata={"help": "Whether to offload the activations to the CPU."}, ) def __post_init__(self): self.bf16 = not (self.fp16) if self.bf16 is None else self.bf16 super().__post_init__()