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# 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](../generation_strategies). 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