5ad960f1f4
* add watermarking processor * remove the other hashing (context width=1 always) * make style * Update src/transformers/generation/logits_process.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/generation/logits_process.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/generation/logits_process.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/generation/configuration_utils.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * update watermarking process * add detector * update tests to use detector * fix failing tests * rename `input_seq` * make style * doc for processor * minor fixes * docs * make quality * Update src/transformers/generation/configuration_utils.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/generation/logits_process.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/generation/watermarking.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/generation/watermarking.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/generation/watermarking.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * add PR suggestions * let's use lru_cache's default max size (128) * import processor if torch available * maybe like this * lets move the config to torch independet file * add docs * tiny docs fix to make the test happy * Update src/transformers/generation/configuration_utils.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/generation/watermarking.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * PR suggestions * add docs * fix test * fix docs * address pr comments * style * Revert "style" This reverts commit 7f33cc34ff08b414f8e7f90060889877606b43b2. * correct style * make doctest green --------- Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
2.3 KiB
Generation
Each framework has a generate method for text generation implemented in their respective GenerationMixin class:
- PyTorch [
~generation.GenerationMixin.generate] is implemented in [~generation.GenerationMixin]. - TensorFlow [
~generation.TFGenerationMixin.generate] is implemented in [~generation.TFGenerationMixin]. - Flax/JAX [
~generation.FlaxGenerationMixin.generate] is implemented in [~generation.FlaxGenerationMixin].
Regardless of your framework of choice, you can parameterize the generate method with a [~generation.GenerationConfig]
class instance. Please refer to this class for the complete list of generation parameters, which control the behavior
of the generation method.
To learn how to inspect a model's generation configuration, what are the defaults, how to change the parameters ad hoc, and how to create and save a customized generation configuration, refer to the text generation strategies guide. The guide also explains how to use related features, like token streaming.
GenerationConfig
autodoc generation.GenerationConfig - from_pretrained - from_model_config - save_pretrained - update - validate - get_generation_mode
autodoc generation.WatermarkingConfig
GenerationMixin
autodoc generation.GenerationMixin - generate - compute_transition_scores
TFGenerationMixin
autodoc generation.TFGenerationMixin - generate - compute_transition_scores
FlaxGenerationMixin
autodoc generation.FlaxGenerationMixin - generate