transformers.js/pr_1665/en/api/generation/configuration_utils.md

generation/configuration_utils

Configuration for text generation.

GenerationConfig holds the parameters that control generate().

Classes

GenerationConfig

Class that holds a configuration for a generation task.

GenerationConfig.max_length : number

The maximum length the generated tokens can have. Corresponds to the length of the input prompt + max_new_tokens. Its effect is overridden by max_new_tokens, if also set.

Default: 20

GenerationConfig.max_new_tokens : number

The maximum number of tokens to generate, ignoring the number of tokens in the prompt.

Default: null

GenerationConfig.min_length : number

The minimum length of the sequence to be generated. Corresponds to the length of the input prompt + min_new_tokens. Its effect is overridden by min_new_tokens, if also set.

Default: 0

GenerationConfig.min_new_tokens : number

The minimum number of tokens to generate, ignoring the number of tokens in the prompt.

Default: null

GenerationConfig.early_stopping : boolean | 'never'

Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values:

• true, where the generation stops as soon as there are num_beams complete candidates;
• false, where a heuristic is applied and the generation stops when it is very unlikely to find better candidates;
• "never", where the beam search procedure only stops when there cannot be better candidates (canonical beam search algorithm).

Default: false

GenerationConfig.max_time : number

The maximum time, in seconds, allowed for generation. Generation will still finish the current pass after the allocated time has passed.

Default: null

GenerationConfig.do_sample : boolean

Whether to use sampling; use greedy decoding otherwise.

Default: false

GenerationConfig.num_beams : number

Number of beams for beam search. 1 means no beam search.

Default: 1

GenerationConfig.num_beam_groups : number

Number of groups to divide num_beams into to encourage diversity among different groups of beams. See this paper for more details.

Default: 1

GenerationConfig.penalty_alpha : number

Balance model confidence against the degeneration penalty during contrastive search decoding.

Default: null

GenerationConfig.use_cache : boolean

Whether the model should reuse past key/value states, when supported, to speed up decoding.

Default: true

GenerationConfig.temperature : number

The value used to modulate the next token probabilities.

Default: 1.0

GenerationConfig.top_k : number

The number of highest-probability vocabulary tokens to keep for top-k filtering.

Default: 50

GenerationConfig.top_p : number

If set to a float below 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.

Default: 1.0

GenerationConfig.typical_p : number

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 a float below 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 for more details.

Default: 1.0

GenerationConfig.epsilon_cutoff : number

If set to a 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 for more details.

Default: 0.0

GenerationConfig.eta_cutoff : number

Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to a 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 for more details.

Default: 0.0

GenerationConfig.diversity_penalty : number

This value is subtracted from a beam's score if it generates the same token as any beam from another group at a particular time. Note that diversity_penalty is only effective if group beam search is enabled.

Default: 0.0

GenerationConfig.repetition_penalty : number

Penalty applied to repeated tokens. 1.0 means no penalty. See this paper for more details.

Default: 1.0

GenerationConfig.encoder_repetition_penalty : number

Penalty applied to sequences that are not in the original input. 1.0 means no penalty.

Default: 1.0

GenerationConfig.length_penalty : number

Exponential penalty applied to sequence length during 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.

Default: 1.0

GenerationConfig.no_repeat_ngram_size : number

If set to an integer greater than 0, all n-grams of that size can only occur once.

Default: 0

GenerationConfig.bad_words_ids : number[][]

List of token IDs that are not allowed to be generated. In order to get the token IDs of the words that should not appear in the generated text, use tokenizer(bad_words, { add_prefix_space: true, add_special_tokens: false }).input_ids.

Default: null

GenerationConfig.force_words_ids : number[][] | number[][][]

List of token IDs that must be generated. If given a number[][], this is treated as a simple list of words that must be included, the opposite of bad_words_ids. If given number[][][], this triggers a disjunctive constraint, which allows different forms of each word.

Default: null

GenerationConfig.renormalize_logits : boolean

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 because search algorithms assume the score logits are normalized, but some logit processors or warpers break the normalization.

Default: false

GenerationConfig.constraints : Object[]

Custom constraints that guide generation to include certain tokens as defined by Constraint objects.

Default: null

GenerationConfig.forced_bos_token_id : number

The ID of the token to force as the first generated token after the decoder_start_token_id. Useful for multilingual models like mBART where the first generated token needs to be the target language token.

Default: null

GenerationConfig.forced_eos_token_id : number[]?

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.

Default: null

GenerationConfig.remove_invalid_values : boolean

Whether to remove possible nan and inf outputs of the model to prevent the generation method from crashing. Note that using remove_invalid_values can slow down generation.

Default: false

GenerationConfig.exponential_decay_length_penalty : [number, number]

This tuple adds an exponentially increasing length penalty after a certain number of tokens have been generated. The tuple consists of: (start_index, decay_factor) where start_index indicates where the penalty starts and decay_factor represents the factor of exponential decay.

Default: null

GenerationConfig.suppress_tokens : number[]

A list of tokens to suppress during generation. The SuppressTokens logit processor sets their log probabilities to -inf so that they are not sampled.

Default: null

GenerationConfig.streamer : TextStreamer

Streamer used to yield generated text incrementally.

Default: null

GenerationConfig.begin_suppress_tokens : number[]

A list of tokens to suppress at the beginning of the generation. The SuppressBeginTokens logit processor sets their log probabilities to -inf so that they are not sampled.

Default: null

GenerationConfig.forced_decoder_ids : [number, number][]

A list of integer pairs that maps 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.

Default: null

GenerationConfig.guidance_scale : number

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 tied to the input prompt, usually at the expense of poorer quality.

Default: null

GenerationConfig.num_return_sequences : number

The number of independently computed returned sequences for each element in the batch.

Default: 1

GenerationConfig.output_attentions : boolean

Whether to return attention tensors from all attention layers. See attentions under returned tensors for more details.

Default: false

GenerationConfig.output_hidden_states : boolean

Whether to return the hidden states of all layers. See hidden_states under returned tensors for more details.

Default: false

GenerationConfig.output_scores : boolean

Whether to return the prediction scores. See scores under returned tensors for more details.

Default: false

GenerationConfig.return_dict_in_generate : boolean

Whether to return a ModelOutput instead of a plain tuple.

Default: false

GenerationConfig.pad_token_id : number

The ID of the padding token.

Default: null

GenerationConfig.bos_token_id : number

The ID of the beginning-of-sequence token.

Default: null

GenerationConfig.eos_token_id : number[]?

The ID of the end-of-sequence token. Optionally, use a list to set multiple end-of-sequence tokens.

Default: null

GenerationConfig.encoder_no_repeat_ngram_size : number

If set to an integer greater than 0, all n-grams of that size that occur in the encoder_input_ids cannot occur in the decoder_input_ids.

Default: 0

GenerationConfig.decoder_start_token_id : number

If an encoder-decoder model starts decoding with a token other than bos, the ID of that token.

Default: null

GenerationConfig.generation_kwargs : Object

Additional generation kwargs forwarded to the model's generate function. Kwargs that are not present in generate's signature are used in the model forward pass.

Default: {}

GenerationConfig.constructor(config)

Parameters

• config (GenerationConfig | PretrainedConfig)