lmflow.models.hf_text_regression_model
======================================

.. py:module:: lmflow.models.hf_text_regression_model


Attributes
----------

.. autoapisummary::

   lmflow.models.hf_text_regression_model.logger


Classes
-------

.. autoapisummary::

   lmflow.models.hf_text_regression_model.HFTextRegressionModel


Module Contents
---------------

.. py:data:: logger

.. py:class:: HFTextRegressionModel(model_args: lmflow.args.ModelArguments, do_train: bool = False, device='gpu', **kwargs)

   Bases: :py:obj:`lmflow.models.text_regression_model.TextRegressionModel`, :py:obj:`lmflow.models.hf_model_mixin.HFModelMixin`, :py:obj:`lmflow.models.interfaces.tunable.Tunable`


   Initializes a HFTextRegressionModel instance.

   :param model_args: Model arguments such as model name, path, revision, etc.
   :param do_train: Determines whether to prepare the model for training, including distribtued env, model placement, quantization,
                    lora, etc.
   :type do_train: bool, default True
   :param args: Positional arguments.
   :type args: Optional.
   :param kwargs: Keyword arguments.
   :type kwargs: Optional.


   .. py:method:: tokenize(dataset: lmflow.datasets.dataset.Dataset, add_special_tokens=True, *args, **kwargs)

      Tokenize the full dataset.

      :param dataset:
      :type dataset: lmflow.datasets.Dataset.
      :param args: Positional arguments.
      :type args: Optional.
      :param kwargs: Keyword arguments.
      :type kwargs: Optional.

      :returns: The tokenized dataset, without any leading or trailing special
                tokens (normally they are Begin-Of-Sentence or End-Of-Sentence
                tokens).
      :rtype: tokenized_datasets



   .. py:method:: inference(inputs, release_gpu: bool = False, use_vllm: bool = False, **kwargs) -> Union[list[float], transformers.modeling_outputs.SequenceClassifierOutputWithPast]

      Perform generation process of the model.

      :param inputs: The sequence used as a prompt for the generation or as model inputs to the model.
                     When using vllm inference, this should be a string or a list of strings.
                     When using normal inference, this should be a tensor.
      :param release_gpu: Whether to release the GPU resource after inference, by default False.
      :type release_gpu: bool, optional
      :param use_vllm: Whether to use VLLM for inference, by default False.
      :type use_vllm: bool, optional
      :param kwargs: Keyword arguments.
      :type kwargs: Optional.

      :returns: The generated sequence output
      :rtype: outputs



   .. py:method:: prepare_inputs_for_inference(dataset: lmflow.datasets.dataset.Dataset, enable_distributed_inference: bool = False, use_vllm: bool = False, **kwargs) -> Union[lmflow.datasets.dataset.Dataset, ray.data.Dataset]


   .. py:method:: postprocess_inference_outputs(dataset: lmflow.datasets.dataset.Dataset, scores: Union[list[float], list[list[float]]])
      :staticmethod:



   .. py:method:: postprocess_distributed_inference_outputs(dataset: lmflow.datasets.dataset.Dataset, inference_result: list[lmflow.utils.data_utils.RewardModelInferenceResultWithInput])
      :staticmethod:



   .. py:method:: save(dir, *args, **kwargs)

      Perform generation process of the model.

      :param dir: The directory to save model and tokenizer
      :param kwargs: Keyword arguments.
      :type kwargs: Optional.