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# generation/parameters
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## `generation/parameters~GenerationFunctionParameters` : Object
**Kind**: inner typedef of [generation/parameters](#module_generation/parameters)
ParamTypeDescription
[kwargs]any(Dict[str, any], optional):
**Properties**
NameTypeDefaultDescription
[inputs]*(Tensor of varying shape depending on the modality, optional):
The sequence used as a prompt for the generation or as model inputs to the encoder. If null the
method initializes it with bos_token_id and a batch size of 1. For decoder-only models inputs
should be in the format of input_ids. For encoder-decoder models inputs can represent any of
input_ids, input_values, input_features, or pixel_values.
[generation_config]*(GenerationConfig, optional):
The generation configuration to be used as base parametrization for the generation call.
**kwargs passed to generate matching the attributes of generation_config will override them.
If generation_config is not provided, the default will be used, which has the following loading
priority:
(1) from the generation_config.json model file, if it exists;
(2) from the model configuration. Please note that unspecified parameters will inherit [GenerationConfig]'s
default values, whose documentation should be checked to parameterize generation.
[logits_processor]*(LogitsProcessorList, optional):
Custom logits processors that complement the default logits processors built from arguments and
generation config. If a logit processor is passed that is already created with the arguments or a
generation config an error is thrown. This feature is intended for advanced users.
[stopping_criteria]*(StoppingCriteriaList, optional):
Custom stopping criteria that complements the default stopping criteria built from arguments and a
generation config. If a stopping criteria is passed that is already created with the arguments or a
generation config an error is thrown. This feature is intended for advanced users.
[streamer]*(BaseStreamer, optional):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through streamer.put(token_ids) and the streamer is responsible for any further processing.
[decoder_input_ids]Array.<number>(number[], optional):
If the model is an encoder-decoder model, this argument is used to pass the decoder_input_ids.
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