biPddlmZmZddlmZmZddlmZeGddeZy)) dataclassfield)AnyOptional)TrainingArgumentsc,eZdZUdZej dgzZedddiZee d<eddd iZ e e e d <eddd iZ e eeefe d<eddd iZe ee d <edddiZee d<edddiZe e d<fdZxZS)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_cachec|j |j n |j|_t| |jdvrt d|jy)N)r keep_startz?truncation_mode must be either 'keep_end' or 'keep_start', got )rfp16super __post_init__r ValueError)self __class__s [/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/trl/trainer/iterative_sft_config.pyrz IterativeSFTConfig.__post_init___sZ'+yy'8Odii     'A A^_c_s_s^tuv v B)__name__ __module__ __qualname____doc__r_VALID_DICT_FIELDSrrfloat__annotations__rrboolr dictstrrrintrrr __classcell__)rs@rr r s 8+==AT@UU! D M5! ! D(4.38 A 3xS#X/!& q !J  !Z[OS#(no#4 wwrr N) dataclassesrrtypingrr transformersrr rrr/s2) * Ow*Ow Owr