| 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 | |
| GenerationMixin | |
| [[autodoc]] generation.GenerationMixin | |
| - generate | |
| - compute_transition_scores | |
| - greedy_search | |
| - sample | |
| - beam_search | |
| - beam_sample | |
| - contrastive_search | |
| - group_beam_search | |
| - constrained_beam_search | |
| TFGenerationMixin | |
| [[autodoc]] generation.TFGenerationMixin | |
| - generate | |
| - compute_transition_scores | |
| FlaxGenerationMixin | |
| [[autodoc]] generation.FlaxGenerationMixin | |
| - generate |