Confirms that the model class is compatible with generation. If not, raises an exception that points to the right class to use.

Confirms that the model class is compatible with generation. If not, raises an exception that points to the right class to use.

Confirms that the model class is compatible with generation. If not, raises an exception that points to the right class to use.

Validates model kwargs for generation. Generate argument typos will also be caught here.

Validates model kwargs for generation. Generate argument typos will also be caught here.

Validates model kwargs for generation. Generate argument typos will also be caught here.

Validates model kwargs for generation. Generate argument typos will also be caught here.

Validates model kwargs for generation. Generate argument typos will also be caught here.

Validates model kwargs for generation. Generate argument typos will also be caught here.

Validates model kwargs for generation. Generate argument typos will also be caught here.

Add a callback to the current list of [`~transformers.TrainerCallback`]. Args: callback (`type` or [`~transformers.Traine

Applies Rotary Position Embedding to the query and key tensors. Args: q (`tf.Tensor`): The query tensor. k (`tf.Tensor`): The key

Applies Rotary Position Embedding to the query and key tensors. Args: q (`tf.Tensor`): The query tensor. k (`tf.Tensor`): The key

r""" Generates sequences of token ids for models with a language modeling head using **greedy decoding** or **sample** (depending on `

A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired arguments, depending on the

r""" Generates sequences of token ids for models with a language modeling head using **beam search multinomial sampling** and can be u

r""" Generates sequences of token ids for models with a language modeling head using **beam search multinomial sampling** and can be u

r""" Generates sequences of token ids for models with a language modeling head using **beam search multinomial sampling** and can be u

r""" Generates sequences of token ids for models with a language modeling head using **beam search decoding** and can be used for text

r""" Generates sequences of token ids for models with a language modeling head using **beam search decoding** and can be used for text

r""" Generates sequences of token ids for models with a language modeling head using **beam search decoding** and can be used for text

gradient of tanh approximation of gelu gradient of actual gelu is: 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 *

Link to paper: https://arxiv.org/abs/2108.12409 Alibi tensor is not causal as the original paper mentions, it relies on a translation invaria

This method turns a tokenizer vocab into a "clean" vocab where each token represents the actual string it will yield, without any spe

This method turns a tokenizer vocab into a "clean" vocab where each token represents the actual string it will yield, without any spe

r""" Generates sequences of token ids for models with a language modeling head using **constrained beam search decoding** and can be u

r""" Generates sequences of token ids for models with a language modeling head using **constrained beam search decoding** and can be u

r""" Generates sequences of token ids for models with a language modeling head using **constrained beam search decoding** and can be u

r""" Generates sequences of token ids for models with a language modeling head using **contrastive search** and can be used for text-d

r""" Generates sequences of token ids for models with a language modeling head using **contrastive search** and can be used for text-d

r""" Generates sequences of token ids for models with a language modeling head using **contrastive search** and can be used for text-d

r""" Generates sequences of token ids for models with a language modeling head using **contrastive search** and can be used for text-d

r""" Generates sequences of token ids for models with a language modeling head using **contrastive search** and can be used for text-d

Converts a sequence of tokens (string) in a single string.

Converts a sequence of tokens (string) in a single string.

Creates a draft of a model card using the information available to the `Trainer`. Args: language (`str`, *optional*):

Setup the optimizer. We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the

Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or passed as an argument.

Run evaluation and returns metrics. The calling script will be responsible for providing a method to compute metrics, as they are ta

For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point operations for every backw

r""" Generates sequences of token ids for models with a language modeling head. <Tip warning={true}> Most generation-contro

Determines which tokens must be banned given latest tokens and the previously seen ngrams.

Determines which tokens must be banned given latest tokens and the previously seen ngrams.

Get all parameter names that weight decay will be applied to Note that some models implement their own layernorm instead of calling

Returns the evaluation [`~torch.utils.data.DataLoader`]. Subclass and override this method if you want to inject some custom behavio

Returns the optimizer class and optimizer parameters based on the training arguments. Args: args (`transformers.training

get a matrix of size (batch_size,) + (vocab_size,)*n (for n-grams) that represent the n-grams that occurred previously. The B

get a matrix of size (batch_size,) + (vocab_size,)*n (for n-grams) that represent the n-grams that occurred previously. The B

Returns the test [`~torch.utils.data.DataLoader`]. Subclass and override this method if you want to inject some custom behavior.

Returns the training [`~torch.utils.data.DataLoader`]. Will use no sampler if `train_dataset` does not implement `__len__`, a random

r""" Generates sequences of token ids for models with a language modeling head using **greedy decoding** and can be used for text-deco

r""" Generates sequences for models with a language modeling head using greedy decoding. Parameters: input_ids (`tf.Tenso

r""" Generates sequences of token ids for models with a language modeling head using **greedy decoding** and can be used for text-deco

r""" Generates sequences of token ids for models with a language modeling head using **greedy decoding** and can be used for text-deco

r""" Generates sequences for models with a language modeling head using greedy decoding. Parameters: input_ids (`tf.Tenso

r""" Generates sequences of token ids for models with a language modeling head using **diverse beam search decoding** and can be used

r""" Generates sequences of token ids for models with a language modeling head using **diverse beam search decoding** and can be used

r""" Generates sequences of token ids for models with a language modeling head using **diverse beam search decoding** and can be used

Returns whether # of leaves == # of words. Otherwise some word is a subset of another.

Returns whether # of leaves == # of words. Otherwise some word is a subset of another.

r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the in

r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the in

r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the in

r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the in