#
Generation

各フレームワークには、それぞれの `GenerationMixin` クラスに実装されたテキスト生成のための Generate メソッドがあります。

- PyTorch generate() は GenerationMixin に実装されています。
- TensorFlow generate() は TFGenerationMixin に実装されています。
- Flax/JAX generate() は FlaxGenerationMixin に実装されています。

選択したフレームワークに関係なく、GenerationConfig を使用して生成メソッドをパラメータ化できます。 クラスインスタンス。動作を制御する生成パラメータの完全なリストについては、このクラスを参照してください。 生成方法のこと。

モデルの生成構成を検査する方法、デフォルトとは何か、パラメーターをアドホックに変更する方法を学習するには、 カスタマイズされた生成構成を作成して保存する方法については、「 テキスト生成戦略ガイド。このガイドでは、関連機能の使用方法についても説明しています。 トークンストリーミングのような。

##
GenerationConfig

###
class transformers.GenerationConfig

< source >

( **kwargs )

####
from_pretrained

< source >

( pretrained_model_name: Union config_file_name: Union = None cache_dir: Union = None force_download: bool = False local_files_only: bool = False token: Union = None revision: str = 'main' **kwargs ) →
GenerationConfig

Parameters

- pretrained_model_name (`str` or `os.PathLike`) — This can be either:
  - a string, the model id of a pretrained model configuration hosted inside a model repo on huggingface.co.
  - a path to a directory containing a configuration file saved using the save_pretrained() method, e.g., `./my_model_directory/`.
- config_file_name (`str` or `os.PathLike`, optional, defaults to `"generation_config.json"`) — Name of the generation configuration JSON file to be loaded from `pretrained_model_name`.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force to (re-)download the configuration files and override the cached versions if they exist. resume_download — Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
- token (`str` or `bool`, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.

  To test a pull request you made on the Hub, you can pass `revision=“refs/pr/“.

- return_unused_kwargs (`bool`, optional, defaults to `False`) — If `False`, then this function returns just the final configuration object.

  If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where unused_kwargs is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the part of `kwargs` which has not been used to update `config` and is otherwise ignored.

- subfolder (`str`, optional, defaults to `""`) — In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here.
- kwargs (`Dict[str, Any]`, optional) — The values in kwargs of any keys which are configuration attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are not configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.

Returns

GenerationConfig

The configuration object instantiated from this pretrained model.

Instantiate a GenerationConfig from a generation configuration file.

Examples:

    >>> from transformers import GenerationConfig

    >>> # Download configuration from huggingface.co and cache.
    >>> generation_config = GenerationConfig.from_pretrained("openai-community/gpt2")

    >>> # E.g. config was saved using *save_pretrained('./test/saved_model/')*
    >>> generation_config.save_pretrained("./test/saved_model/")
    >>> generation_config = GenerationConfig.from_pretrained("./test/saved_model/")

    >>> # You can also specify configuration names to your generation configuration file
    >>> generation_config.save_pretrained("./test/saved_model/", config_file_name="my_configuration.json")
    >>> generation_config = GenerationConfig.from_pretrained("./test/saved_model/", "my_configuration.json")

    >>> # If you'd like to try a minor variation to an existing configuration, you can also pass generation
    >>> # arguments to `.from_pretrained()`. Be mindful that typos and unused arguments will be ignored
    >>> generation_config, unused_kwargs = GenerationConfig.from_pretrained(
    ...     "openai-community/gpt2", top_k=1, foo=False, do_sample=True, return_unused_kwargs=True
    ... )
    >>> generation_config.top_k
    1

    >>> unused_kwargs
    {'foo': False}

####
from_model_config

< source >

( model_config: PretrainedConfig ) →
GenerationConfig

Parameters

- model_config (`PretrainedConfig`) — The model config that will be used to instantiate the generation config.

Returns

GenerationConfig

The configuration object instantiated from those parameters.

Instantiates a GenerationConfig from a PretrainedConfig. This function is useful to convert legacy PretrainedConfig objects, which may contain generation parameters, into a stand-alone GenerationConfig.

####
save_pretrained

< source >

( save_directory: Union config_file_name: Union = None push_to_hub: bool = False **kwargs )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory where the configuration JSON file will be saved (will be created if it does not exist).
- config_file_name (`str` or `os.PathLike`, optional, defaults to `"generation_config.json"`) — Name of the generation configuration JSON file to be saved in `save_directory`.
- push_to_hub (`bool`, optional, defaults to `False`) — Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace).
- kwargs (`Dict[str, Any]`, optional) — Additional key word arguments passed along to the push_to_hub() method.

Save a generation configuration object to the directory `save_directory`, so that it can be re-loaded using the from_pretrained() class method.

##
GenerationMixin

###
class transformers.GenerationMixin

< source >

( )

A class containing all functions for auto-regressive text generation, to be used as a mixin in PreTrainedModel.

The class exposes generate(), which can be used for:

- greedy decoding if `num_beams=1` and `do_sample=False`
- contrastive search if `penalty_alpha>0` and `top_k>1`
- multinomial sampling if `num_beams=1` and `do_sample=True`
- beam-search decoding if `num_beams>1` and `do_sample=False`
- beam-search multinomial sampling if `num_beams>1` and `do_sample=True`
- diverse beam-search decoding if `num_beams>1` and `num_beam_groups>1`
- constrained beam-search decoding if `constraints!=None` or `force_words_ids!=None`
- assisted decoding if `assistant_model` or `prompt_lookup_num_tokens` is passed to `.generate()`

To learn more about decoding strategies refer to the text generation strategies guide.

####
generate

< source >

( inputs: Optional = None generation_config: Optional = None logits_processor: Optional = None stopping_criteria: Optional = None prefix_allowed_tokens_fn: Optional = None synced_gpus: Optional = None assistant_model: Optional = None streamer: Optional = None negative_prompt_ids: Optional = None negative_prompt_attention_mask: Optional = None **kwargs ) →
ModelOutput or `torch.LongTensor`

Parameters

- inputs (`torch.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 `None` 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. If your stopping criteria depends on the `scores` input, make sure you pass `return_dict_in_generate=True, output_scores=True` to `generate`. This feature is intended for advanced users.
- prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, optional) — If provided, this function constraints the beam search to allowed tokens only at each step. If not provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful for constrained generation conditioned on the prefix, as described in Autoregressive Entity Retrieval.
- synced_gpus (`bool`, optional) — Whether to continue running the while loop until max_length. Unless overridden this flag will be set to `True` under DeepSpeed ZeRO Stage 3 multiple GPUs environment to avoid hanging if one GPU finished generating before other GPUs. Otherwise it’ll be set to `False`.
- assistant_model (`PreTrainedModel`, optional) — An assistant model that can be used to accelerate generation. The assistant model must have the exact same tokenizer. The acceleration is achieved when forecasting candidate tokens with the assistent model is much faster than running generation with the model you’re calling generate from. As such, the assistant model should be much smaller.
- 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.
- negative_prompt_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — The negative prompt needed for some processors such as CFG. The batch size must match the input batch size. This is an experimental feature, subject to breaking API changes in future versions.
- negative_prompt_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Attention_mask for `negative_prompt_ids`.
- kwargs (`Dict[str, Any]`, optional) — Ad hoc parametrization of `generation_config` and/or additional model-specific kwargs that will be forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with decoder_.

Returns

ModelOutput or `torch.LongTensor`

A ModelOutput (if `return_dict_in_generate=True` or when `config.return_dict_in_generate=True`) or a `torch.LongTensor`.

If the model is not an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible ModelOutput types are:

- GenerateDecoderOnlyOutput,
- GenerateBeamDecoderOnlyOutput

If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible ModelOutput types are:

- GenerateEncoderDecoderOutput,
- GenerateBeamEncoderDecoderOutput

Generates sequences of token ids for models with a language modeling head.

Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the model’s default generation configuration. You can override any `generation_config` by passing the corresponding parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.

For an overview of generation strategies and code examples, check out the following guide.

####
compute_transition_scores

< source >

( sequences: Tensor scores: Tuple beam_indices: Optional = None normalize_logits: bool = False ) →
`torch.Tensor`

Parameters

- sequences (`torch.LongTensor`) — The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.
- scores (`tuple(torch.FloatTensor)`) — Transition scores for each vocabulary token at each generation step. Beam transition scores consisting of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
- beam_indices (`torch.LongTensor`, optional) — Beam indices of generated token id at each generation step. `torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`. Only required if a `num_beams>1` at generate-time.
- normalize_logits (`bool`, optional, defaults to `False`) — Whether to normalize the logits (which, for legacy reasons, may be unnormalized).

Returns

`torch.Tensor`

A `torch.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)` containing the transition scores (logits)

Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used). This is a convenient method to quicky obtain the scores of the selected tokens at generation time.

Examples:

    >>> from transformers import GPT2Tokenizer, AutoModelForCausalLM
    >>> import numpy as np

    >>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
    >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
    >>> tokenizer.pad_token_id = tokenizer.eos_token_id
    >>> inputs = tokenizer(["Today is"], return_tensors="pt")

    >>> # Example 1: Print the scores for each token generated with Greedy Search
    >>> outputs = model.generate(**inputs, max_new_tokens=5, return_dict_in_generate=True, output_scores=True)
    >>> transition_scores = model.compute_transition_scores(
    ...     outputs.sequences, outputs.scores, normalize_logits=True
    ... )
    >>> # input_length is the length of the input prompt for decoder-only models, like the GPT family, and 1 for
    >>> # encoder-decoder models, like BART or T5.
    >>> input_length = 1 if model.config.is_encoder_decoder else inputs.input_ids.shape[1]
    >>> generated_tokens = outputs.sequences[:, input_length:]
    >>> for tok, score in zip(generated_tokens[0], transition_scores[0]):
    ...     # | token | token string | log probability | probability
    ...     print(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}")
    |   262 |  the     | -1.414 | 24.33%
    |  1110 |  day     | -2.609 | 7.36%
    |   618 |  when    | -2.010 | 13.40%
    |   356 |  we      | -1.859 | 15.58%
    |   460 |  can     | -2.508 | 8.14%

    >>> # Example 2: Reconstruct the sequence scores from Beam Search
    >>> outputs = model.generate(
    ...     **inputs,
    ...     max_new_tokens=5,
    ...     num_beams=4,
    ...     num_return_sequences=4,
    ...     return_dict_in_generate=True,
    ...     output_scores=True,
    ... )
    >>> transition_scores = model.compute_transition_scores(
    ...     outputs.sequences, outputs.scores, outputs.beam_indices, normalize_logits=False
    ... )
    >>> # If you sum the generated tokens' scores and apply the length penalty, you'll get the sequence scores.
    >>> # Tip 1: recomputing the scores is only guaranteed to match with `normalize_logits=False`. Depending on the
    >>> # use case, you might want to recompute it with `normalize_logits=True`.
    >>> # Tip 2: the output length does NOT include the input length
    >>> output_length = np.sum(transition_scores.numpy() < 0, axis=1)
    >>> length_penalty = model.generation_config.length_penalty
    >>> reconstructed_scores = transition_scores.sum(axis=1) / (output_length**length_penalty)
    >>> print(np.allclose(outputs.sequences_scores, reconstructed_scores))
    True

##
TFGenerationMixin

###
class transformers.TFGenerationMixin

< source >

( )

A class containing all of the functions supporting generation, to be used as a mixin in TFPreTrainedModel.

The class exposes generate(), which can be used for:

- greedy decoding by calling `greedy_search()` if `num_beams=1` and `do_sample=False`
- contrastive search by calling `contrastive_search()` if `penalty_alpha>0` and `top_k>1`
- multinomial sampling by calling `sample()` if `num_beams=1` and `do_sample=True`
- beam-search decoding by calling `beam_search()` if `num_beams>1`

You do not need to call any of the above methods directly. Pass custom parameter values to ‘generate’ instead. To learn more about decoding strategies refer to the text generation strategies guide.

####
generate

< source >

( inputs: Optional = None generation_config: Optional = None logits_processor: Optional = None seed = None **kwargs ) →
ModelOutput or `tf.Tensor`

Parameters

- inputs (`tf.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 `None` the method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs` should of 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 (`~generation.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 had 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.
- seed (`List[int]`, optional) — Random seed to control sampling, containing two integers, used when `do_sample` is `True`. See the `seed` argument from stateless functions in `tf.random`.
- kwargs (`Dict[str, Any]`, optional) — Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with decoder_.

Returns

ModelOutput or `tf.Tensor`

A ModelOutput (if `return_dict_in_generate=True` or when `config.return_dict_in_generate=True`) or a `tf.Tensor`.

If the model is not an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible ModelOutput types are:

- TFGreedySearchDecoderOnlyOutput,
- TFSampleDecoderOnlyOutput,
- TFBeamSearchDecoderOnlyOutput,
- TFBeamSampleDecoderOnlyOutput

If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible ModelOutput types are:

- TFGreedySearchEncoderDecoderOutput,
- TFSampleEncoderDecoderOutput,
- TFBeamSearchEncoderDecoderOutput,
- TFBeamSampleEncoderDecoderOutput

Generates sequences of token ids for models with a language modeling head.

Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the model’s default generation configuration. You can override any `generation_config` by passing the corresponding parameters to generate, e.g. `.generate(inputs, num_beams=4, do_sample=True)`.

For an overview of generation strategies and code examples, check out the following guide.

####
compute_transition_scores

< source >

( sequences: Tensor scores: Tuple beam_indices: Optional = None normalize_logits: bool = False ) →
`tf.Tensor`

Parameters

- sequences (`tf.Tensor`) — The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished early due to the `eos_token_id`.
- scores (`tuple(tf.Tensor)`) — Transition scores for each vocabulary token at each generation step. Beam transition scores consisting of log probabilities of tokens conditioned on log softmax of previously generated tokens Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token), with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
- beam_indices (`tf.Tensor`, optional) — Beam indices of generated token id at each generation step. `tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`. Only required if a `num_beams>1` at generate-time.
- normalize_logits (`bool`, optional, defaults to `False`) — Whether to normalize the logits (which, for legacy reasons, may be unnormalized).

Returns

`tf.Tensor`

A `tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)` containing the transition scores (logits)

Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used). This is a convenient method to quicky obtain the scores of the selected tokens at generation time.

Examples:

    >>> from transformers import GPT2Tokenizer, TFAutoModelForCausalLM
    >>> import numpy as np

    >>> tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
    >>> model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
    >>> tokenizer.pad_token_id = tokenizer.eos_token_id
    >>> inputs = tokenizer(["Today is"], return_tensors="tf")

    >>> # Example 1: Print the scores for each token generated with Greedy Search
    >>> outputs = model.generate(**inputs, max_new_tokens=5, return_dict_in_generate=True, output_scores=True)
    >>> transition_scores = model.compute_transition_scores(
    ...     outputs.sequences, outputs.scores, normalize_logits=True
    ... )
    >>> # input_length is the length of the input prompt for decoder-only models, like the GPT family, and 1 for
    >>> # encoder-decoder models, like BART or T5.
    >>> input_length = 1 if model.config.is_encoder_decoder else inputs.input_ids.shape[1]
    >>> generated_tokens = outputs.sequences[:, input_length:]
    >>> for tok, score in zip(generated_tokens[0], transition_scores[0]):
    ...     # | token | token string | logits | probability
    ...     print(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}")
    |   262 |  the     | -1.414 | 24.33%
    |  1110 |  day     | -2.609 | 7.36%
    |   618 |  when    | -2.010 | 13.40%
    |   356 |  we      | -1.859 | 15.58%
    |   460 |  can     | -2.508 | 8.14%

    >>> # Example 2: Reconstruct the sequence scores from Beam Search
    >>> outputs = model.generate(
    ...     **inputs,
    ...     max_new_tokens=5,
    ...     num_beams=4,
    ...     num_return_sequences=4,
    ...     return_dict_in_generate=True,
    ...     output_scores=True,
    ... )
    >>> transition_scores = model.compute_transition_scores(
    ...     outputs.sequences, outputs.scores, outputs.beam_indices, normalize_logits=False
    ... )
    >>> # If you sum the generated tokens' scores and apply the length penalty, you'll get the sequence scores.
    >>> # Tip: recomputing the scores is only guaranteed to match with `normalize_logits=False`. Depending on the
    >>> # use case, you might want to recompute it with `normalize_logits=True`.
    >>> output_length = np.sum(transition_scores.numpy() < 0, axis=1)
    >>> length_penalty = model.generation_config.length_penalty
    >>> reconstructed_scores = np.sum(transition_scores, axis=1) / (output_length**length_penalty)
    >>> print(np.allclose(outputs.sequences_scores, reconstructed_scores))
    True

##
FlaxGenerationMixin

###
class transformers.FlaxGenerationMixin

< source >

( )

A class containing all functions for auto-regressive text generation, to be used as a mixin in FlaxPreTrainedModel.

The class exposes generate(), which can be used for:

- greedy decoding by calling `_greedy_search()` if `num_beams=1` and `do_sample=False`
- multinomial sampling by calling `_sample()` if `num_beams=1` and `do_sample=True`
- beam-search decoding by calling `_beam_search()` if `num_beams>1` and `do_sample=False`

You do not need to call any of the above methods directly. Pass custom parameter values to ‘generate’ instead. To learn more about decoding strategies refer to the text generation strategies guide.

####
generate

< source >

( input_ids: Array generation_config: Optional = None prng_key: Optional = None trace: bool = True params: Optional = None logits_processor: Optional = None **kwargs )

Parameters

- input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`) — The sequence used as a prompt for the generation.
- generation_config (`~generation.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 had 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.
- trace (`bool`, optional, defaults to `True`) — Whether to trace generation. Setting `trace=False` should only be used for debugging and will lead to a considerably slower runtime.
- params (`Dict[str, jnp.ndarray]`, optional) — Optionally the model parameters can be passed. Can be useful for parallelized generation.
- logits_processor (`FlaxLogitsProcessorList` , 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.
- kwargs (`Dict[str, Any]`, optional) — Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with decoder_.

Generates sequences of token ids for models with a language modeling head.

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