o Ti@sDddlmZmZddlmZmZddlmZeGdddeZdS)) dataclassfield)AnyOptional)TrainingArgumentscseZdZUdZejdgZedddidZee d<eddd idZ e e e d <eddd idZ e eeefe d<eddd idZe ee d <edddidZee d<edddidZe e d<fddZZS)IterativeSFTConfiga Configuration class for the [`IterativeSFTTrainer`]. This class includes only the parameters that are specific to Iterative 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 [`IterativeSFTTrainer`] is provided as a string. > Parameters that control the data preprocessing max_length (`int` or `None`, *optional*, defaults to `None`): Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated. truncation_mode (`str`, *optional*, defaults to `"keep_end"`): The truncation mode to use, either `"keep_end"` or `"keep_start"`. optimize_device_cache (`bool`, *optional*, defaults to `False`): Whether to optimize accelerator cache for slightly more memory-efficient training. model_init_kwargs helpzLog 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.)defaultmetadata 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.bf16zKeyword arguments for `AutoModelForCausalLM.from_pretrained`, used when the `model` argument of the `IterativeSFTTrainer` is provided as a string.z[Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated. max_lengthkeep_endz>The truncation mode to use, either 'keep_end' or 'keep_start'.truncation_modeFzRWhether to optimize accelerator cache for slightly more memory-efficient training.optimize_device_cachecsB|jdur |j n|j|_t|jdvrtd|jdS)N)r keep_startz?truncation_mode must be either 'keep_end' or 'keep_start', got )rfp16super __post_init__r ValueError)self __class__T/home/ubuntu/.local/lib/python3.10/site-packages/trl/trainer/iterative_sft_config.pyr_s   z IterativeSFTConfig.__post_init__)__name__ __module__ __qualname____doc__r_VALID_DICT_FIELDSrr float__annotations__rrboolrdictstrrrintrrr __classcell__rrrrrs@    rN) dataclassesrrtypingrr transformersrrrrrrs