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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 KTOConfig(TrainingArguments):
r"""
Configuration class for the [`KTOTrainer`].
This class includes only the parameters that are specific to KTO 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:
max_length (`int` or `None`, *optional*, defaults to `1024`):
Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want
to use the default data collator.
max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
Maximum length of the prompt. This argument is required if you want to use the default data collator.
max_completion_length (`int` or `None`, *optional*, defaults to `None`):
Maximum length of the completion. This argument is required if you want to use the default data collator
and your model is an encoder-decoder.
beta (`float`, *optional*, defaults to `0.1`):
Parameter controlling the deviation from the reference model. Higher β means less deviation from the
reference model.
loss_type (`str`, *optional*, defaults to `"kto"`):
Type of loss to use. Possible values are:
- `"kto"`: KTO loss from the [KTO](https://huggingface.co/papers/2402.01306) paper.
- `"apo_zero_unpaired"`: Unpaired variant of APO-zero loss from the [APO](https://huggingface.co/papers/2408.06266) paper.
desirable_weight (`float`, *optional*, defaults to `1.0`):
Desirable losses are weighed by this factor to counter unequal number of desirable and undesirable paris.
undesirable_weight (`float`, *optional*, defaults to `1.0`):
Undesirable losses are weighed by this factor to counter unequal number of desirable and undesirable pairs.
label_pad_token_id (`int`, *optional*, defaults to `-100`):
Label pad token id. This argument is required if you want to use the default data collator.
padding_value (`int` or `None`, *optional*, defaults to `None`):
Padding value to use. If `None`, the padding value of the tokenizer is used.
truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`.
This argument is required if you want to use the default data collator.
generate_during_eval (`bool`, *optional*, defaults to `False`):
If `True`, generates and logs completions from both the model and the reference model to W&B or Comet during
evaluation.
is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`):
When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument,
you need to specify if the model returned by the callable is an encoder-decoder model.
precompute_ref_log_probs (`bool`, *optional*, defaults to `False`):
Whether to precompute reference model log probabilities for training and evaluation datasets. This is
useful when training without the reference model to reduce the total GPU memory needed.
model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
string.
ref_model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the reference model
from a string.
dataset_num_proc: (`int` or `None`, *optional*, defaults to `None`):
Number of processes to use for processing the dataset.
disable_dropout (`bool`, *optional*, defaults to `True`):
Whether to disable dropout in the model and reference model.
use_liger_loss (`bool`, *optional*, defaults to `False`):
Whether to use Liger loss. It requires liger-kernel to be installed.
base_model_attribute_name (`str`, *optional*, defaults to `"model"`):
Name of the attribute in the model that contains the base model. This is used to get the base model from
the model when the model does not have a `get_decoder` method in the case when `use_liger_loss` is `True`.
"""
_VALID_DICT_FIELDS = TrainingArguments._VALID_DICT_FIELDS + ["model_init_kwargs", "ref_model_init_kwargs"]
# Parameters whose default values are overridden from TrainingArguments
learning_rate: float = field(
default=1e-6,
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."
)
},
)
max_length: Optional[int] = field(
default=1024,
metadata={"help": "Maximum length of the sequences (prompt + completion) in the batch."},
)
max_prompt_length: Optional[int] = field(
default=512,
metadata={
"help": "Maximum length of the prompt. This argument is required if you want to use the default data "
"collator and your model is an encoder-decoder."
},
)
max_completion_length: Optional[int] = field(
default=None,
metadata={
"help": "Maximum length of the completion. This argument is required if you want to use the default data "
"collator and your model is an encoder-decoder."
},
)
beta: float = field(
default=0.1,
metadata={
"help": "Parameter controlling the deviation from the reference model. Higher β means less deviation from "
"the reference model."
},
)
loss_type: str = field(
default="kto",
metadata={
"help": "Type of loss to use.",
"choices": ["kto", "apo_zero_unpaired"],
},
)
desirable_weight: float = field(
default=1.0,
metadata={
"help": "Desirable losses are weighed by this factor to counter unequal number of desirable and "
"undesirable pairs.",
},
)
undesirable_weight: float = field(
default=1.0,
metadata={
"help": "Undesirable losses are weighed by this factor to counter unequal number of desirable and "
"undesirable pairs.",
},
)
label_pad_token_id: int = field(
default=-100,
metadata={
"help": "Label pad token id. This argument is required if you want to use the default data collator."
},
)
padding_value: Optional[int] = field(
default=None,
metadata={"help": "Padding value to use. If `None`, the padding value of the tokenizer is used."},
)
truncation_mode: str = field(
default="keep_end",
metadata={
"help": "Truncation mode to use when the prompt is too long.",
"choices": ["keep_end", "keep_start"],
},
)
generate_during_eval: bool = field(
default=False,
metadata={
"help": "If `True`, generates and logs completions from both the model and the reference model to W&B "
"during evaluation."
},
)
is_encoder_decoder: Optional[bool] = field(
default=None,
metadata={
"help": "When using the `model_init` argument (callable) to instantiate the model instead of the `model` "
"argument, you need to specify if the model returned by the callable is an encoder-decoder model."
},
)
disable_dropout: bool = field(
default=True,
metadata={"help": "Whether to disable dropout in the model."},
)
precompute_ref_log_probs: bool = field(
default=False,
metadata={
"help": "Whether to precompute reference model log probabilities for training and evaluation datasets. "
"This is useful when training without the reference model to reduce the total GPU memory needed."
},
)
model_init_kwargs: Optional[dict[str, Any]] = field(
default=None,
metadata={
"help": "Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model "
"from a string."
},
)
ref_model_init_kwargs: Optional[dict[str, Any]] = field(
default=None,
metadata={
"help": "Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the "
"reference model from a string."
},
)
dataset_num_proc: Optional[int] = field(
default=None,
metadata={"help": "Number of processes to use for processing the dataset."},
)
use_liger_loss: bool = field(
default=False,
metadata={"help": "Whether to use Liger loss. It requires liger-kernel to be installed."},
)
base_model_attribute_name: str = field(
default="model",
metadata={
"help": "Name of the attribute in the model that contains the base model. This is used to get the base "
"model from the model when the model does not have a `get_decoder` method in the case when "
"`use_liger_loss` is `True`."
},
)
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