biLddlmZmZddlmZddlmZeGddeZy)) dataclassfield)Optional)TrainingArgumentsceZdZUdZedddiZeed<edddiZeed <ed dd iZ e e ed <ed ddiZ e ed<edddiZ e eed<edddiZe eed<ed ddiZe eed<ed ddiZe ed<edddiZeed<edddiZe ed<ed dd iZe eed!<fd"ZxZS)# PRMConfiga1 Configuration class for the [`PRMTrainer`]. This class includes only the parameters that are specific to PRM 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) used for truncation. max_prompt_length (`int` or `None`, *optional*, defaults to `512`): Maximum length of the prompt used for truncation. max_completion_length (`int` or `None`, *optional*, defaults to `None`): Maximum length of the completion used for truncation. The completion is the concatenation of the steps. disable_dropout (`bool`, *optional*, defaults to `True`): Whether to disable dropout in the model. step_separator (`str`, *optional*, defaults to `"\n"`): Separator used to separate each step of the reasoning process. train_on_last_step_only (`bool`, *optional*, defaults to `False`): Whether to train only on the last step. dataset_num_proc (`int`, *optional*, defaults to `None`): Number of processes to use for processing the dataset. gh㈵>helpz$The initial learning rate for AdamW.)defaultmetadata learning_rate zLog 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. logging_stepsNzWhether 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.bf16TzWhether 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 average_tokens_across_devicesizJMaximum length of the sequences (prompt + completion) used for truncation. max_lengthiz1Maximum length of the prompt used for truncation.max_prompt_lengthzgMaximum length of the completion used for truncation. The completion is the concatenation of the steps.max_completion_lengthzSeparator used to separate each step of the reasoning process.step_separatorFz'Whether to train only on the last step.train_on_last_step_onlyz6Number of processes to use for processing the dataset.dataset_num_proccv|j |j n |j|_t| y)N)rfp16super __post_init__)self __class__s Q/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/trl/trainer/prm_config.pyrzPRMConfig.__post_init__os*'+yy'8Odii  )__name__ __module__ __qualname____doc__rr float__annotations__rrrboolrrintrrrrstrrrr __classcell__)rs@rrrs:!@AM5! D M5! ! D(4.+0 E +!4!&fg!J (-MN(x},1  ,8C="XYOT Z[NC%*CD%T',RS'hsm   r rN) dataclassesrrtypingr transformersrrr rr/s/)* \ !\  \ r