detector_benchmark.generation.generator
=======================================

.. py:module:: detector_benchmark.generation.generator


Classes
-------

.. autoapisummary::

   detector_benchmark.generation.generator.LLMGenerator


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

.. py:class:: LLMGenerator(model: transformers.AutoModelForCausalLM, model_config: detector_benchmark.utils.configs.ModelConfig)

   Bases: :py:obj:`torch.nn.Module`


   .. py:attribute:: generator


   .. py:attribute:: tokenizer


   .. py:attribute:: device


   .. py:attribute:: gen_params


   .. py:method:: forward(samples: list, batch_size: int = 1, watermarking_scheme: Optional[detector_benchmark.watermark.auto_watermark.AutoWatermark] = None) -> list[str]

      Generate text from a list of input contexts.

      Parameters:
      ----------
          samples: list
              A list of input contexts for text generation.
          batch_size: int
              The batch size to use for generation. Defaults to 1.
          watermarking_scheme: AutoWatermark
              The watermarking scheme to use for generation.
              If provided, it should be an instance of LogitsProcessor. Defaults to None.

      Returns:
      -------
          list[str]
              A list of generated texts.



   .. py:method:: forward_debug(samples: list, batch_size: int = 1, watermarking_scheme: Optional[detector_benchmark.watermark.auto_watermark.AutoWatermark] = None) -> list[str]

      Takes a list of input contexts and generates text using the model.

      Parameters:
      ----------
          samples: list
              A list of input contexts for text generation.
          batch_size: int
              The batch size to use for generation.
          watermarking_scheme: LogitsProcessor
              The watermarking scheme to use for generation.

      Returns:
      -------
          tuple
              A tuple containing the generated texts, the raw logits, and the processed logits.