(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.

(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.

(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.

(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.

(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.

(number[] or Tensor, optional): If the model is an encoder-decoder model, this argument is used to pass the decoder_input_ids.

(DynamicCache, optional): A cache object that stores previously computed key/value states. When provided, the model will use these cached states to avoid recomputing them, significantly speeding up sequential generation.