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Joao Gante 2024-07-15 19:59:20 +01:00 committed by GitHub
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src/transformers/generation/configuration_utils.py
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@ -113,10 +113,10 @@ class GenerationConfig(PushToHubMixin):
heuristic is applied and the generation stops when is it very unlikely to find better candidates;
`"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical
beam search algorithm).
max_time(`float`, *optional*):
max_time (`float`, *optional*):
The maximum amount of time you allow the computation to run for in seconds. generation will still finish
the current pass after allocated time has been passed.
stop_strings(`str or List[str]`, *optional*):
stop_strings (`str or List[str]`, *optional*):
A string or a list of strings that should terminate generation if the model outputs them.
> Parameters that control the generation strategy used
@ -181,10 +181,10 @@ class GenerationConfig(PushToHubMixin):
`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*):
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*):
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
@ -200,7 +200,7 @@ class GenerationConfig(PushToHubMixin):
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`):
forced_eos_token_id (`int` or 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`):
@ -234,7 +234,7 @@ class GenerationConfig(PushToHubMixin):
low_memory (`bool`, *optional*):
Switch to sequential beam search and sequential topk for contrastive search to reduce peak memory.
Used with beam search and contrastive search.
watermarking_config (Union[`WatermarkingConfig`, `dict`], *optional*):
watermarking_config (`WatermarkingConfig` or `dict`, *optional*):
Arguments used to watermark the model outputs by adding a small bias to randomly selected set of "green" tokens.
If passed as `Dict`, it will be converted to a `WatermarkingConfig` internally.
See [this paper](https://arxiv.org/abs/2306.04634) for more details. Accepts the following keys:
@ -249,12 +249,12 @@ class GenerationConfig(PushToHubMixin):
- "lefthash" (default): "green" tokens selection depend on the last token (Algorithm 2 from the paper)
- "selfhash": "green" tokens selection depends on the current token itself (Algorithm 3 from the paper)
The downside of this scheme is that it considers all possible next tokens and can be slower than "lefthash".
- context_width(`int`):
- context_width (`int`):
The context length of previous tokens to use in seeding. Higher context length makes watermarking more robust.
> Parameters that define the output variables of generate
num_return_sequences(`int`, *optional*, defaults to 1):
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
@ -284,7 +284,7 @@ class GenerationConfig(PushToHubMixin):
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 (`Union[int, List[int]]`, *optional*):
decoder_start_token_id (`int` or `List[int]`, *optional*):
If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token or a list of length
`batch_size`. Indicating a list enables different start ids for each element in the batch
(e.g. multilingual models with different target languages in one batch)
@ -323,7 +323,7 @@ class GenerationConfig(PushToHubMixin):
cache_implementation (`str`, *optional*, default to `None`):
Cache class that should be used when generating.
cache_config (`Union[CacheConfig, dict]`, *optional*, default to `None`):
cache_config (`CacheConfig` or `dict`, *optional*, default to `None`):
Arguments used in the key-value cache class can be passed in `cache_config`. Can be passed as a `Dict` and
it will be converted to its repsective `CacheConfig` internally.
Otherwise can be passed as a `CacheConfig` class matching the indicated `cache_implementation`.