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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 BCOConfig(TrainingArguments):
r"""
Configuration class for the [`BCOTrainer`].
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 Ξ² means less deviation from the
reference model.
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.
disable_dropout (`bool`, *optional*, defaults to `True`):
Whether to disable dropout in the model and reference model.
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.
prompt_sample_size (`int`, *optional*, defaults to `1024`):
Number of prompts that are fed to density ratio classifier.
min_density_ratio (`float`, *optional*, defaults to `0.5`):
Minimum value of the density ratio. The estimated density ratio is clamped to this value.
max_density_ratio (`float`, *optional*, defaults to `10.0`):
Maximum value of the density ratio. The estimated density ratio is clamped to this value.
"""
max_length: Optional[int] = field(
default=1024,
metadata={
"help": "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: 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."
},
)
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 Ξ² means less deviation from the reference model."
},
)
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. Possible values are "
"`keep_end` or `keep_start`. This argument is required if you want to use the "
"default data collator."
},
)
disable_dropout: bool = field(
default=True,
metadata={"help": "Whether to disable dropout in the model and reference model."},
)
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."
},
)
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."},
)
prompt_sample_size: int = field(
default=1024,
metadata={"help": "Number of prompts that are fed to density ratio classifier."},
)
min_density_ratio: float = field(
default=0.5,
metadata={"help": "Minimum value of the density ratio. The estimated density ratio is clamped to this value."},
)
max_density_ratio: float = field(
default=10.0,
metadata={"help": "Maximum value of the density ratio. The estimated density ratio is clamped to this value."},
)
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