eneration will still finish
            the current pass after allocated time has been passed.

        > Parameters that control the generation strategy used

        do_sample (`bool`, *optional*, defaults to `False`):
            Whether or not to use sampling ; use greedy decoding otherwise.
        num_beams (`int`, *optional*, defaults to 1):
            Number of beams for beam search. 1 means no beam search.
        num_beam_groups (`int`, *optional*, defaults to 1):
            Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams.
            [this paper](https://arxiv.org/pdf/1610.02424.pdf) for more details.
        penalty_alpha (`float`, *optional*):
            The values balance the model confidence and the degeneration penalty in contrastive search decoding.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should use the past last key/values attentions (if applicable to the model) to
            speed up decoding.

        > Parameters for manipulation of the model output logits

        temperature (`float`, *optional*, defaults to 1.0):
            The value used to modulate the next token probabilities.
        top_k (`int`, *optional*, defaults to 50):
            The number of highest probability vocabulary tokens to keep for top-k-filtering.
        top_p (`float`, *optional*, defaults to 1.0):
            If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to
            `top_p` or higher are kept for generation.
        typical_p (`float`, *optional*, defaults to 1.0):
            Local typicality measures how similar the conditional probability of predicting a target token next is to
            the expected conditional probability of predicting a random token next, given the partial text already
            generated. If set to float < 1, the smallest set of the most locally typical tokens with probabilities that
            add up to `typical_p` or higher are kept for generation. See [this
            paper](https://arxiv.org/pdf/2202.00666.pdf) for more details.
        epsilon_cutoff (`float`, *optional*, defaults to 0.0):
            If set to float strictly between 0 and 1, only tokens with a conditional probability greater than
            `epsilon_cutoff` will be sampled. In the paper, suggested values range from 3e-4 to 9e-4, depending on the
            size of the model. See [Truncation Sampling as Language Model
            Desmoothing](https://arxiv.org/abs/2210.15191) for more details.
        eta_cutoff (`float`, *optional*, defaults to 0.0):
            Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to float strictly between
            0 and 1, a token is only considered if it is greater than either `eta_cutoff` or `sqrt(eta_cutoff) *
            exp(-entropy(softmax(next_token_logits)))`. The latter term is intuitively the expected next token
            probability, scaled by `sqrt(eta_cutoff)`. In the paper, suggested values range from 3e-4 to 2e-3,
            depending on the size of the model. See [Truncation Sampling as Language Model
            Desmoothing](https://arxiv.org/abs/2210.15191) for more details.
        diversity_penalty (`float`, *optional*, defaults to 0.0):
            This value is subtracted from a beam's score if it generates a token same as any beam from other group at a
            particular time. Note that `diversity_penalty` is only effective if `group beam search` is enabled.
        repetition_penalty (`float`, *optional*, defaults to 1.0):
            The parameter for repetition penalty. 1.0 means no penalty. See [this
            paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
        encoder_repetition_penalty (`float`, *optional*, defaults to 1.0):
            The paramater for encoder_repetition_penalty. An exponential penalty on sequences that are not in the
            original input. 1.0 means no penalty.
        length_penalty (`float`, *optional*, defaults to 1.0):
            Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
            the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
            likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while
            `length_penalty` < 0.0 encourages shorter sequences.
        no_repeat_ngram_size (`int`, *optional*, defaults to 0):
            If set to int > 0, all ngrams of that size can only occur once.
        bad_words_ids(`List[List[int]]`, *optional*):
            List of list of token ids that are not allowed to be generated. Check
            [`~generation.NoBadWordsLogitsProcessor`] for further documentation and examples.
        force_words_ids(`List[List[int]]` or `List[List[List[int]]]`, *optional*):
            List of token ids that must be generated. If given a `List[List[int]]`, this is treated as a simple list of
            words that must be included, the opposite to `bad_words_ids`. If given `List[List[List[int]]]`, this
            triggers a [disjunctive constraint](https://github.com/huggingface/transformers/issues/14081), where one
            can allow different forms of each word.
        renormalize_logits (`bool`, *optional*, defaults to `False`):
            Whether to renormalize the logits after applying all the logits processors or warpers (including the custom
            ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the score logits
            are normalized but some logit processors or warpers break the normalization.
        constraints (`List[Constraint]`, *optional*):
            Custom constraints that can be added to the generation to ensure that the output will contain the use of
            certain tokens as defined by `Constraint` objects, in the most sensible way possible.
        forced_bos_token_id (`int`, *optional*, defaults to `model.config.forced_bos_token_id`):
            The id of the token to force as the first generated token after the `decoder_start_token_id`. Useful for
            multilingual models like [mBART](../model_doc/mbart) where the first generated token needs to be the target
            language token.
        forced_eos_token_id (`Union[int, List[int]]`, *optional*, defaults to `model.config.forced_eos_token_id`):
            The id of the token to force as the last generated token when `max_length` is reached. Optionally, use a
            list to set multiple *end-of-sequence* tokens.
        remove_invalid_values (`bool`, *optional*, defaults to `model.config.remove_invalid_values`):
            Whether to remove possible *nan* and *inf* outputs of the model to prevent the generation method to crash.
            Note that using `remove_invalid_values` can slow down generation.
        exponential_decay_length_penalty (`tuple(int, float)`, *optional*):
            This Tuple adds an exponentially increasing length penalty, after a certain amount of tokens have been
            generated. The tuple shall consist of: `(start_index, decay_factor)` where `start_index` indicates where
            penalty starts and `decay_factor` represents the factor of exponential decay
        suppress_tokens  (`List[int]`, *optional*):
            A list of tokens that will be suppressed at generation. The `SupressTokens` logit processor will set their
            log probs to `-inf` so that they are not sampled.
        begin_suppress_tokens  (`List[int]`, *optional*):
            A list of tokens that will be suppressed at the beginning of the generation. The `SupressBeginTokens` logit
            processor will set their log probs to `-inf` so that they are not sampled.
        forced_decoder_ids (`List[List[int]]`, *optional*):
            A list of pairs of integers which indicates a mapping from generation indices to token indices that will be
            forced before sampling. For example, `[[1, 123]]` means the second generated token will always be a token
            of index 123.
        sequence_bias (`Dict[Tuple[int], float]`, *optional*)):
            Dictionary that maps a sequence of tokens to its bias term. Positive biases increase the odds of the
            sequence being selected, while negative biases do the opposite. Check
            [`~generation.SequenceBiasLogitsProcessor`] for further documentation and examples.
        guidance_scale (`float`, *optional*):
            The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale > 1`.
            Higher guidance scale encourages the model to generate samples that are more closely linked to the input
            prompt, usually at the expense of poorer quality.
        low_memory (`bool`, *optional*):
            Switch to sequential topk for contrastive search to reduce peak memory. Used with contrastive search.


        > Parameters that define the output variables of `generate`

        num_return_sequences(`int`, *optional*, defaults to 1):
            The number of independently computed returned sequences for each element in the batch.
        output_attentions (`bool`, *optional*, defaults to `False`):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more details.
        output_hidden_states (`bool`, *optional*, defaults to `False`):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more details.
        output_scores (`bool`, *optional*, defaults to `False`):
            Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
        return_dict_in_generate (`bool`, *optional*, defaults to `False`):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

        > Special tokens that can be used at generation time

        pad_token_id (`int`, *optional*):
            The id of the *padding* token.
        bos_token_id (`int`, *optional*):
            The id of the *beginning-of-sequence* token.
        eos_token_id (`Union[int, List[int]]`, *optional*):
            The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.

        > Generation parameters exclusive to encoder-decoder models

        encoder_no_repeat_ngram_size (`int`, *optional*, defaults to 0):
            If set to int > 0, all ngrams of that size that occur in the `encoder_input_ids` cannot occur in the
            `decoder_input_ids`.
        decoder_start_token_id (`int`, *optional*):
            If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token.

        > Wild card

        generation_kwargs:
            Additional generation kwargs will be forwarded to the `generate` function of the model. Kwargs that are not
            present in `generate`'s signature will be used in the model forward pass.
    c