TFBlenderbotModel

n graph mode the value in the
config will be used instead.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.   training (bool, optional, defaults to False) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).   Returns
transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.TFSeq2SeqModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when confi


TFBlenderbotModel

g.return_dict=False) comprising various elements depending on the
configuration (BlenderbotConfig) and inputs.
last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the decoder of the model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is


TFBlenderbotModel

 passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions w


TFBlenderbotModel

eights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_h


TFBlenderbotModel

eads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
  The TFBlenderbotModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, TFBlenderbotModel
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> model = TFBlenderbotModel.from_pretrained("facebook/blenderbot-400M-distill")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> last_hidden_states = outputs.last_hidden


TFBlenderbotForConditionalGeneration

class transformers.TFBlenderbotForConditionalGeneration  < source > ( *args **kwargs  )   Parameters   config (BlenderbotConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    The BLENDERBOT Model with a language modeling head. Can be used for summarization.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior. TensorFlow models and layers in transformers accept two formats as


TFBlenderbotForConditionalGeneration

 input: having all inputs as keyword arguments (like PyTorch models), or having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument: a single Tensor with input_ids only and nothing else: model(input_ids) a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_


TFBlenderbotForConditionalGeneration

mask]) or model([input_ids, attention_mask, token_type_ids]) a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids}) Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!  call  < source > ( input_ids: tf.Tensor | None = None attention_mask: tf.Tensor | None = None decoder_input_ids: tf.Tensor | None = None decoder_attention_mask: tf.Tensor | None = None decoder_position_ids: tf.Tensor | None = None head_mask: tf.Tensor | None = None decoder_head_mask: tf.Tensor | None = None cross_attn_head_mask: tf.Tensor | None = None encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None past_key_values: List[tf.Tensor] | None = None inputs_embeds: tf.Tensor | None = None decoder_in


TFBlenderbotForConditionalGeneration

puts_embeds: tf.Tensor | None = None use_cache: Optional[bool] = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: tf.Tensor | None = None training: Optional[bool] = False  ) → transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor)  Parameters   input_ids (tf.Tensor of shape ({0})) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (tf.Tensor of shape ({0}), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   decoder_input_ids (tf.Tensor of shape (batch_size, target_sequence_length), optional) —
Indices of decoder


TFBlenderbotForConditionalGeneration

input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
Blenderbot uses the bos_token_id as the starting token for decoder_input_ids generation. If
past_key_values is used, optionally only the last decoder_input_ids have to be input (see
past_key_values).   decoder_attention_mask (tf.Tensor of shape (batch_size, target_sequence_length), optional) —
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.   decoder_position_ids (tf.Tensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range [0, config.max_position_embeddings - 1].   head_mask (tf.Tensor of shape (encoder_layers, encoder_attention_heads), optional) —
Mask to nullify


TFBlenderbotForConditionalGeneration

selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   decoder_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   cross_attn_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   encoder_outputs (tf.FloatTensor, optional) —
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
of shape (batch_size, sequence_length, hidden_size) is a sequence of   past_key_values


TFBlenderbotForConditionalGeneration

 (Tuple[Tuple[tf.Tensor]] of length config.n_layers) —
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).   use_cache (bool, optional, defaults to True) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values). Set to False during training, True during generation   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.   out


TFBlenderbotForConditionalGeneration

put_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.   training (bool, optional, defaults to False) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).   labels (tf.tensor of shape (batch_size, sequence_length), optional) —
Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored
(masked), the loss is


TFBlenderbotForConditionalGeneration

 only computed for the tokens with labels in [0, ..., config.vocab_size].   Returns
transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (BlenderbotConfig) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) — Language modeling loss.
logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_head


TFBlenderbotForConditionalGeneration

s, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the w


TFBlenderbotForConditionalGeneration

eighted average in the
self-attention heads.
cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidd


TFBlenderbotForConditionalGeneration

en-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
  The TFBlenderbotForConditionalGeneration forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Conversation example::   Copied >>> from transformers import AutoTokenizer, TFBlenderbotForConditionalGenerat


TFBlenderbotForConditionalGeneration

ion
>>> mname = "facebook/blenderbot-400M-distill"
>>> model = TFBlenderbotForConditionalGeneration.from_pretrained(mname)
>>> tokenizer = AutoTokenizer.from_pretrained(mname)
>>> UTTERANCE = "My friends are cool but they eat too many carbs."
>>> print("Human: ", UTTERANCE)
>>> inputs = tokenizer([UTTERANCE], return_tensors="tf")
>>> reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0])
>>> REPLY = "I'm not sure"
>>> print("Human: ", REPLY)
>>> NEXT_UTTERANCE = (
...     "My friends are cool but they eat too many carbs.</s> <s>That's unfortunate. "
...     "Are they trying to lose weight or are they just trying to be healthier?</s> "
...     "<s> I'm not sure."
... )
>>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors="tf")
>>> next_reply_ids = model.generate(**inputs)
>>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_token


FlaxBlenderbotModel

class transformers.FlaxBlenderbotModel  < source > ( config: BlenderbotConfig input_shape: typing.Tuple[int] = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs  )   Parameters   config (BlenderbotConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    The bare MBart Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a Flax Linen
flax.nn.Module subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all ma


FlaxBlenderbotModel

tter related to general usage and behavior. Finally, this model supports inherent JAX features such as: Just-In-Time (JIT) compilation Automatic Differentiation Vectorization Parallelization  __call__  < source > ( input_ids: Array attention_mask: typing.Optional[jax.Array] = None decoder_input_ids: typing.Optional[jax.Array] = None decoder_attention_mask: typing.Optional[jax.Array] = None position_ids: typing.Optional[jax.Array] = None decoder_position_ids: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: PRNGKey = None  ) → transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or tuple(torch.FloatTensor)  Parameters   input_ids (jnp.ndarray of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by


FlaxBlenderbotModel

 default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (jnp.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
For translation and summarization training, decoder_input_ids should be provided. If no
decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right
for denoising pre-tra


FlaxBlenderbotModel

ining following the paper.   decoder_attention_mask (jnp.ndarray of shape (batch_size, target_sequence_length), optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in the
paper for more information on the default strategy.   position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].   decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range [0, config.max_position_embeddings - 1].   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all at


FlaxBlenderbotModel

tention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BlenderbotConfig) and inputs.
last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the decoder of the model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_siz


FlaxBlenderbotModel

e) is output.
past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(jnp.ndarray) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plu


FlaxBlenderbotModel

s the initial embedding outputs.
decoder_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the outpu


FlaxBlenderbotModel

t of the last layer of the encoder of the model.
encoder_hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
  The FlaxBlenderbotPreTrainedModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be


FlaxBlenderbotModel

 defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, FlaxBlenderbotModel
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> model = FlaxBlenderbotModel.from_pretrained("facebook/blenderbot-400M-distill")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state  encode  < source > ( input_ids: Array attention_mask: typing.Optional[jax.Array] = None position_ids: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: PRNGKey = None


FlaxBlenderbotModel

) → transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)  Parameters   input_ids (jnp.ndarray of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (jnp.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].   output_attentions (bool, optional) —
Whether


FlaxBlenderbotModel

or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (<class 'transformers.models.blenderbot.configuration_blenderbot.BlenderbotConfig'>) and inputs.
last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
hidden_state


FlaxBlenderbotModel

s (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
   Example:   Copied >>> from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration
>>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer = AutoTokeniz


FlaxBlenderbotModel

er.from_pretrained("facebook/blenderbot-400M-distill")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)  decode  < source > ( decoder_input_ids encoder_outputs encoder_attention_mask: typing.Optional[jax.Array] = None decoder_attention_mask: typing.Optional[jax.Array] = None decoder_position_ids: typing.Optional[jax.Array] = None past_key_values: dict = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: PRNGKey = None  ) → transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)  Parameters   decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length)) —
Indices of decoder input sequence tokens in the vo


FlaxBlenderbotModel

cabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
For translation and summarization training, decoder_input_ids should be provided. If no
decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right
for denoising pre-training following the paper.   encoder_outputs (tuple(tuple(jnp.ndarray)) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.   encoder_attention_mask (jnp.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0


FlaxBlenderbotModel

 for tokens that are masked.
What are attention masks?   decoder_attention_mask (jnp.ndarray of shape (batch_size, target_sequence_length), optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in the
paper for more information on the default strategy.   decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range [0, config.max_position_embeddings - 1].   past_key_values (Dict[str, np.ndarray], optional, returned by init_cache or when passing previous past_key_values) —
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-state


FlaxBlenderbotModel

s are of shape [batch_size, max_length].   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (<class 'transformers.models.blenderbot.configuration_blenderbot.BlenderbotConfig'>) and inputs.
last_hidden_state (jnp.ndarray


FlaxBlenderbotModel

 of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(jnp.ndarray) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if
config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values
input) to speed up sequential decoding.
hidden_states (tuple(jnp.ndarra


FlaxBlenderbotModel

y), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequen


FlaxBlenderbotModel

ce_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
   Example:   Copied >>> import jax.numpy as jnp
>>> from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration
>>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hi


FlaxBlenderbotForConditionalGeneration

class transformers.FlaxBlenderbotForConditionalGeneration  < source > ( config: BlenderbotConfig input_shape: typing.Tuple[int] = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs  )   Parameters   config (BlenderbotConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    The Blenderbot Model with a language modeling head. Can be used for summarization.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a Flax Linen
flax.nn.Module subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and be


FlaxBlenderbotForConditionalGeneration

havior. Finally, this model supports inherent JAX features such as: Just-In-Time (JIT) compilation Automatic Differentiation Vectorization Parallelization  __call__  < source > ( input_ids: Array attention_mask: typing.Optional[jax.Array] = None decoder_input_ids: typing.Optional[jax.Array] = None decoder_attention_mask: typing.Optional[jax.Array] = None position_ids: typing.Optional[jax.Array] = None decoder_position_ids: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: PRNGKey = None  ) → transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)  Parameters   input_ids (jnp.ndarray of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See


FlaxBlenderbotForConditionalGeneration

PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (jnp.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
For translation and summarization training, decoder_input_ids should be provided. If no
decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right
for denoising pre-training following the paper.   decoder_attention_mask (jnp.ndarray of shape (batch_size, target_sequence_length), optional) —


FlaxBlenderbotForConditionalGeneration

Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in the
paper for more information on the default strategy.   position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].   decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range [0, config.max_position_embeddings - 1].   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all la


FlaxBlenderbotForConditionalGeneration

yers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BlenderbotConfig) and inputs.
logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(jnp.ndarray) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors o


FlaxBlenderbotForConditionalGeneration

f shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the wei


FlaxBlenderbotForConditionalGeneration

ghted average in the
self-attention heads.
cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the


FlaxBlenderbotForConditionalGeneration

initial embedding outputs.
encoder_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
  The FlaxBlenderbotPreTrainedModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Conversation example::   Copied >>> from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration
>>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer = AutoTokeni


FlaxBlenderbotForConditionalGeneration

zer.from_pretrained("facebook/blenderbot-400M-distill")
>>> UTTERANCE = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([UTTERANCE], max_length=1024, return_tensors="np")
>>> # Generate Reply
>>> reply_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=5, early_stopping=True).sequences
>>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in reply_ids])  encode  < source > ( input_ids: Array attention_mask: typing.Optional[jax.Array] = None position_ids: typing.Optional[jax.Array] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: PRNGKey = None  ) → transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)  Parameters   input_ids (jnp.ndarray of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the v


FlaxBlenderbotForConditionalGeneration

ocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (jnp.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under retur


FlaxBlenderbotForConditionalGeneration

ned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.modeling_flax_outputs.FlaxBaseModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxBaseModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (<class 'transformers.models.blenderbot.configuration_blenderbot.BlenderbotConfig'>) and inputs.
last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of


FlaxBlenderbotForConditionalGeneration

 the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
   Example:   Copied >>> from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration
>>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)  decode  < source > ( decoder_input_ids encoder_outputs encoder_attention_mask: typing.Optional[jax.Arr


FlaxBlenderbotForConditionalGeneration

ay] = None decoder_attention_mask: typing.Optional[jax.Array] = None decoder_position_ids: typing.Optional[jax.Array] = None past_key_values: dict = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None train: bool = False params: dict = None dropout_rng: PRNGKey = None  ) → transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)  Parameters   decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length)) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
For translation and summarization training, decoder_input_ids should be provided. If no
decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right
for denoising pre-training foll


FlaxBlenderbotForConditionalGeneration

owing the paper.   encoder_outputs (tuple(tuple(jnp.ndarray)) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.   encoder_attention_mask (jnp.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   decoder_attention_mask (jnp.ndarray of shape (batch_size, target_sequence_length), optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in the
paper for more information on the default strategy.   d


FlaxBlenderbotForConditionalGeneration

ecoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) —
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range [0, config.max_position_embeddings - 1].   past_key_values (Dict[str, np.ndarray], optional, returned by init_cache or when passing previous past_key_values) —
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length].   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   R


FlaxBlenderbotForConditionalGeneration

eturns
transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (<class 'transformers.models.blenderbot.configuration_blenderbot.BlenderbotConfig'>) and inputs.
logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding output


FlaxBlenderbotForConditionalGeneration

s.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.
past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of jnp.ndarray tuples of length config.n_layers, with each tuple containing the cached key, value
states of the self-attention and the cross


FlaxBlenderbotForConditionalGeneration

-attention layers if model is used in encoder-decoder setting.
Only relevant if config.is_decoder = True.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
   Example:   Copied >>> import jax.numpy as jnp
>>> from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration
>>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits =


Overview

The IDEFICS model was proposed in OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh
The abstract from the paper is the following:
Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks that require reasoning over one or multiple images to generate a text. However, the datasets used to train these models have not been released, and the collection process has not been fully specified. We introduce the OBELICS dataset, an open web-scale filtered dataset of interleaved image-text documents comprisin


Overview

g 141 million web pages extracted from Common Crawl, 353 million associated images, and 115 billion text tokens. We describe the dataset creation process, present comprehensive filtering rules, and provide an analysis of the dataset’s content. To show the viability of OBELISC, we train an 80 billion parameters vision and language model on the dataset and obtain competitive performance on various multimodal benchmarks. We release the code to reproduce the dataset along with the dataset itself.
This model was contributed by HuggingFaceM4. The original code can be found here. (TODO: don’t have a public link yet).
Idefics modeling code in Transformers is for finetuning and inferencing the pre-trained Idefics models. To train a new Idefics model from scratch use the m4 codebase (a link will be provided once it’s made public)


IdeficsConfig

class transformers.IdeficsConfig  < source > ( vocab_size = 32000 additional_vocab_size = 0 hidden_size = 4096 intermediate_size = 11008 num_hidden_layers = 32 num_attention_heads = 32 dropout = 0.0 hidden_act = 'silu' initializer_range = 0.02 alpha_initializer = 'zeros' alphas_initializer_range = 0.0 alpha_type = 'float' rms_norm_eps = 1e-06 use_cache = True pad_token_id = 0 bos_token_id = 1 eos_token_id = 2 tie_word_embeddings = False cross_layer_interval = 1 qk_layer_norms = False freeze_text_layers = True freeze_text_module_exceptions = [] freeze_lm_head = False freeze_vision_layers = True freeze_vision_module_exceptions = [] use_resampler = False vision_config = None perceiver_config = None **kwargs  )   Parameters   additional_vocab_size (int, *optional`, defaults to 0) —
Additional vocabulary size of the model, typically for the special ”” token. Additional vocab tokens
are always trainable whereas regular vocab tokens can be frozen or not.   vocab_s


IdeficsConfig

ize (int, optional, defaults to 32000) —
Vocabulary size of the Idefics model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling ~IdeficsModel   hidden_size (int, optional, defaults to 4096) —
Dimension of the hidden representations.   intermediate_size (int, optional, defaults to 11008) —
Dimension of the MLP representations.   num_hidden_layers (int, optional, defaults to 32) —
Number of hidden layers in the Transformer encoder.   num_attention_heads (int, optional, defaults to 32) —
Number of attention heads for each attention layer in the Transformer encoder.   dropout (float, optional, defaults to 0.0) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.   hidden_act (str or function, optional, defaults to "silu") —
The non-linear activation function (function or string) in the decoder.   initializer_range (float, optional, defaults to 0.02) —
The standard de


IdeficsConfig

viation of the truncated_normal_initializer for initializing all weight matrices.   alpha_initializer (str, optional, defaults to "zeros") —
Initialization type for the alphas.   alphas_initializer_range (float, optional, defaults to 0.0) —
The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross
Attention.   alpha_type (str, optional, defaults to "float") —
Whether the gating alphas should be vectors or single floats.   rms_norm_eps (float, optional, defaults to 1e-6) —
The epsilon used by the rms normalization layers.   use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if config.is_decoder=True.   pad_token_id (int, optional, defaults to 0) —
Padding token id.   bos_token_id (int, optional, defaults to 1) —
Beginning of stream token id.   eos_token_id (int, optional, defaults to 2) —
End of stream toke


IdeficsConfig

n id.   tie_word_embeddings(bool, optional, defaults to False) —
Whether to tie weight embeddings   cross_layer_interval (int, optional, default to 1) —
Interval for cross attention (from text to image) layers.   qk_layer_norms (bool, optional, defaults to False) — Whether to add layer norm after q and k   freeze_text_layers (bool, optional, defaults to True) — Whether to freeze text layers   freeze_text_module_exceptions (bool, optional, defaults to []) —
Exceptions to freezing text layers when freeze_text_layers is True   freeze_lm_head (bool, optional, defaults to False) — Whether to freeze lm head   freeze_vision_layers (bool, optional, defaults to True) —  Whether to freeze vision layers   freeze_vision_module_exceptions (bool, optional, defaults to []) —
Exceptions to freezing vision layers when freeze_vision_layers is True   use_resampler (bool, optional, defaults to False) — Whether to use the Resampler   vision_config (IdeficsVisionConfig,  optiona


IdeficsConfig

l) — Custom vision config or dict   perceiver_config (IdeficsPerceiverConfig,  optional) — Custom perceiver config or dict    This is the configuration class to store the configuration of a IdeficsModel. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Idefics-9B. e.g. HuggingFaceM4/idefics-9b Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.  Example:   Copied >>> from transformers import IdeficsModel, IdeficsConfig
>>> # Initializing a Idefics idefics-9b style configuration
>>> configuration = IdeficsConfig()
>>> # Initializing a model from the idefics-9b style configuration
>>> model = IdeficsModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.con


IdeficsModel

class transformers.IdeficsModel  < source > ( config: IdeficsConfig  )   Parameters   config (IdeficsConfig) —
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
from_pretrained() method to load the model weights.
config — IdeficsConfig    The bare LLaMA Model outputting raw hidden-states without any specific head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.


IdeficsModel

Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a IdeficsDecoderLayer  forward  < source > ( input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = None image_encoder_embeddings: typing.Optional[torch.FloatTensor] = None perceiver_embeddings: typing.Optional[torch.FloatTensor] = None image_attention_mask: typing.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None interpolate_pos_encoding: typing.Optional[bool] = False return_dict: typing.Optional[bool]


IdeficsModel

 = None  )   Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
If past_key_values is used, optionally only the last decoder_input_ids have to be input (see
past_key_values).
If you want to change


IdeficsModel

 padding behavior, you should read modeling_opt._prepare_decoder_attention_mask
and modify to your needs. See diagram 1 in the paper for more
information on the default strategy.
1 indicates the head is not masked,
0 indicates the head is masked.
   position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1]. What are position IDs?   past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length,


IdeficsModel

 embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   use_cache (bool, optional) —
If se


IdeficsModel

t to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.    The IdeficsModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them


class transformers.IdeficsForVisionText2Text  < source > ( config vision_model = None  )     forward  < source > ( input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = None image_encoder_embeddings: typing.Optional[torch.FloatTensor] = None perceiver_embeddings: typing.Optional[torch.FloatTensor] = None image_attention_mask: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None interpolate_pos_encoding: typing.Optional[bool] = False return_dict: typing.Optional[bool] = None  ) → transformers.models.idefics.modeling_idefics.Idef


icsCausalLMOutputWithPast or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
If past_key_values is used, optionally only the last decoder_input_ids have to be input (see
past_key_values).
If you want to change padding behavior, you should read modeling_opt._prepare_d


ecoder_attention_mask
and modify to your needs. See diagram 1 in the paper for more
information on the default strategy.
1 indicates the head is not masked,
0 indicates the head is masked.
   position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1]. What are position IDs?   past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (s


ee past_key_values input) to speed up sequential decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned


tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Args —
labels (torch.LongTensor of shape (batch_size, sequence_length), optional):
Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored
(masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].   Returns
transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast or tuple(torch.FloatTensor)

A transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elemen


ts depending on the configuration (IdeficsConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head))
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hi


dden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (tuple(torch.FloatTensor), optional) — Tuple of torch.FloatTensor (one for the output of the image embeddings, (batch_size, num_images, sequence_length, hidden_size).
image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
  The


IdeficsForVisionText2Text forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, IdeficsForVisionText2Text
>>> model = IdeficsForVisionText2Text.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you consciours? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to yo


IdeficsImageProcessor

class transformers.IdeficsImageProcessor  < source > ( image_size: int = 224 image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None image_num_channels: typing.Optional[int] = 3 **kwargs  )   Parameters   image_size (int, optional, defaults to 224) —
Resize to image size   image_mean (float or List[float], optional, defaults to IDEFICS_STANDARD_MEAN) —
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the image_mean parameter in the preprocess method. Can be
overridden by the image_mean parameter in the preprocess method.   image_std (float or List[float], optional, defaults to


IdeficsImageProcessor

IDEFICS_STANDARD_STD) —
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the image_std parameter in the preprocess method.
Can be overridden by the image_std parameter in the preprocess method.   image_num_channels (int, optional, defaults to 3) —
Number of image channels.    Constructs a Idefics image processor.  preprocess  < source > ( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]] image_num_channels: typing.Optional[int] = 3 image_size: typing.Union[typing.Dict[str, int], NoneType]


IdeficsImageProcessor

 = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None transform: typing.Callable = None **kwargs  )   Parameters   images (ImageInput) —
A list of images to preprocess.   image_size (int, optional, defaults to self.image_size) —
Resize to image size   image_num_channels (int, optional, defaults to self.image_num_channels) —
Number of image channels.   image_mean (float or List[float], optional, defaults to IDEFICS_STANDARD_MEAN) —
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the image_mean parameter in the preprocess method. Can
be overridden by the image_mean par


IdeficsImageProcessor

ameter in the preprocess method.   image_std (float or List[float], optional, defaults to IDEFICS_STANDARD_STD) —
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the image_std parameter in the preprocess
method. Can be overridden by the image_std parameter in the preprocess method.   transform (Callable, optional, defaults to None) —
A custom transform function that accepts a single image can be passed for training. For example,
torchvision.Compose can be used to compose multiple transforms. If None - an inference mode is
assumed - and then a preset of inference-specific transforms will be applied to the images    Preprocess a batch of images


IdeficsProcessor

class transformers.IdeficsProcessor  < source > ( image_processor tokenizer = None image_size = 224 add_end_of_utterance_token = None **kwargs  )   Parameters   image_processor (IdeficsImageProcessor) —
An instance of IdeficsImageProcessor. The image processor is a required input.   tokenizer (LlamaTokenizerFast) —
An instance of LlamaTokenizerFast. The tokenizer is a required input.   image_size (int, optional, defaults to 224) — Image size (assuming a square image)    Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor. IdeficsProcessor offers all the functionalities of IdeficsImageProcessor and LlamaTokenizerFast. See
the docstring of call() and decode() for more information.  __call__  < source > ( prompts: typing.Union[typing.List[str], typing.List[typing.List[str]]] padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, s


IdeficsProcessor

tr, transformers.tokenization_utils_base.TruncationStrategy] = None max_length: typing.Optional[int] = None transform: typing.Callable = None add_eos_token = False add_end_of_utterance_token = None debug = False return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = <TensorType.PYTORCH: 'pt'>  ) → a dict with entries  Parameters   prompts (Union[List[TextInput], [List[List[TextInput]]]]) —
either a single prompt or a batched list of prompts - see the detailed description immediately after
the end of the arguments doc section.   padding (bool, str or PaddingStrategy, optional, defaults to False) —
Select a strategy to pad the returned sequences (according to the model’s padding side and padding
index) among:
True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
'max_length': Pad to a maximum length specified with the argument max_length or to the maximum
acce


IdeficsProcessor

ptable input length for the model if that argument is not provided.
False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different
lengths).
   max_length (int, optional) —
Maximum length of the returned list and optionally padding length (see above).   truncation (bool, optional) —
Activates truncation to cut input sequences longer than max_length to max_length.   transform (Callable, optional) —
A custom transform function that accepts a single image can be passed for training. For example,
torchvision.Compose can be used to compose multiple functions. If None a preset inference-specific
set of transforms will be applied to the images   add_eos_token (bool, optional, defaults to False) —
Adds eos_token at the end of the final prompt if True`   add_end_of_utterance_token (bool, optional) —
Whether to automatically add <end_of_utterance> after each prompt’s text input (unless followed by an
image). If None the to


IdeficsProcessor

kenizer will be checked instead and if this token is found in
additional_special_tokens then the value will be True.   debug (bool, optional, defaults to False) —
True value will help debug prompt generation by dumping useful information   return_tensors (str or TensorType, optional, defaults to TensorType.PYTORCH) —
The type of tensors to return. Can be one of:
TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
   Returns
a dict with entries

input_ids, attention_mask, pixel_values, image_attention_mask which can be
directly passed to model.generate
  This method takes batched or non-batched prompts made of text and images and converts them into prompts that
the model was trained on and prepares the image pixel values for the model to process. Detailed explanation: Each entry in prompts is either a text to be passed as is or an image that will be processed. An image can be either an image object (PIL.Image) or a url from which th


IdeficsProcessor

e image can be retrieved. When the processor encounters an image it’ll inject <fake_token_around_image><image><fake_token_around_image>
entry into the prompt.  Example:   Copied checkpoint = "HuggingFaceM4/idefics-9b"
processor = AutoProcessor.from_pretrained(checkpoint)
url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
img = processor.image_processor.fetch_images([url])[0]
prompts = [
    "User:",
    img,
    "Describe this image.
t: An image of two kittens in grass.
    "User:",
    "https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
    "Describe this image.
t:",
]
inputs = processor(prompts, return_tensors="pt")
generated_ids = model.generate(**inputs, max_length=100)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]  In this example the prompts will be converted into:   Copied <s>User:<fake_token_around_image><image><fake_token_around_image>Des


IdeficsProcessor

cribe this image.
Assistant: An image of two kittens in grass.
User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
Assistant:' and the two images will be massaged using IdeficsImageProcessor.call() method and placed inside the
pixel_values dict entry of the return value. This example also examplifies that images can be passed as objects or as text urls. It can be seen that the
first image is passed as object and the second one as a url.  To do training do:   Copied image_transform = transforms.Compose(
    [
        transforms.RandomResizedCrop(
            (w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
        ),
        transforms.ToTensor(),
        transforms.Normalize(mean=self.image_mean, std=self.image_std),
    ]
)
inputs = processor(prompts, transform=image_transform, return_tensors="pt") In order to help debug prompt generation enable debug=True which will show you what’s happe


Overview

The BiT model was proposed in Big Transfer (BiT): General Visual Representation Learning by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
BiT is a simple recipe for scaling up pre-training of ResNet-like architectures (specifically, ResNetv2). The method results in significant improvements for transfer learning.
The abstract from the paper is the following:
Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 dat


Overview

asets. BiT performs well across a surprisingly wide range of data regimes — from 1 example per class to 1M total examples. BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.
Tips:
BiT models are equivalent to ResNetv2 in terms of architecture, except that: 1) all batch normalization layers are replaced by group normalization,
2) weight standardization is used for convolutional layers. The authors show that the combination of both is useful for training with large batch sizes, and has a significant
impact on transfer learning.
This model was contributed by nielsr.
The original code can be found here


Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BiT.
 Image Classification
BitForImageClassification is supported by this example script and notebook. See also: Image classification task guide
If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.


BitConfig

class transformers.BitConfig  < source > ( num_channels = 3 embedding_size = 64 hidden_sizes = [256, 512, 1024, 2048] depths = [3, 4, 6, 3] layer_type = 'preactivation' hidden_act = 'relu' global_padding = None num_groups = 32 drop_path_rate = 0.0 embedding_dynamic_padding = False output_stride = 32 width_factor = 1 out_features = None out_indices = None **kwargs  )   Parameters   num_channels (int, optional, defaults to 3) —
The number of input channels.   embedding_size (int, optional, defaults to 64) —
Dimensionality (hidden size) for the embedding layer.   hidden_sizes (List[int], optional, defaults to [256, 512, 1024, 2048]) —
Dimensionality (hidden size) at each stage.   depths (List[int], optional, defaults to [3, 4, 6, 3]) —
Depth (number of layers) for each stage.   layer_ty


BitConfig

pe (str, optional, defaults to "preactivation") —
The layer to use, it can be either "preactivation" or "bottleneck".   hidden_act (str, optional, defaults to "relu") —
The non-linear activation function in each block. If string, "gelu", "relu", "selu" and "gelu_new"
are supported.   global_padding (str, optional) —
Padding strategy to use for the convolutional layers. Can be either "valid", "same", or None.   num_groups (int, optional, defaults to 32) —
Number of groups used for the BitGroupNormActivation layers.   drop_path_rate (float, optional, defaults to 0.0) —
The drop path rate for the stochastic depth.   embedding_dynamic_padding (bool, optional, defaults to False) —
Whether or not to make use of dynamic padding for the embedding layer.   output_stride (int, optional, defaul


BitConfig

ts to 32) —
The output stride of the model.   width_factor (int, optional, defaults to 1) —
The width factor for the model.   out_features (List[str], optional) —
If used as backbone, list of features to output. Can be any of "stem", "stage1", "stage2", etc.
(depending on how many stages the model has). If unset and out_indices is set, will default to the
corresponding stages. If unset and out_indices is unset, will default to the last stage.   out_indices (List[int], optional) —
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and out_features is set, will default to the corresponding stages.
If unset and out_features is unset, will default to the last stage.    This is the configuration cl


BitConfig

ass to store the configuration of a BitModel. It is used to instantiate an BiT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the BiT
google/bit-50 architecture. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.  Example:   Copied >>> from transformers import BitConfig, BitModel
>>> # Initializing a BiT bit-50 style configuration
>>> configuration = BitConfig()
>>> # Initializing a model (with random weights) from the bit-50 style configuration
>>> model = BitModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.conf


BitImageProcessor

class transformers.BitImageProcessor  < source > ( do_resize: bool = True size: typing.Dict[str, int] = None resample: Resampling = <Resampling.BICUBIC: 3> do_center_crop: bool = True crop_size: typing.Dict[str, int] = None do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None do_convert_rgb: bool = True **kwargs  )   Parameters   do_resize (bool, optional, defaults to True) —
Whether to resize the image’s (height, width) dimensions to the specified size. Can be overridden by
do_resize in the preprocess method.   size (Dict[str, int] optional, defaults to {"shortest_edge" -- 224}):
Size of the image after resizing. The shortest edge of the image is resized to size[“shortest_edge”], with
the longest edge resized to keep the input aspect ratio. Can be


BitImageProcessor

 overridden by size in the preprocess
method.   resample (PILImageResampling, optional, defaults to PILImageResampling.BICUBIC) —
Resampling filter to use if resizing the image. Can be overridden by resample in the preprocess method.   do_center_crop (bool, optional, defaults to True) —
Whether to center crop the image to the specified crop_size. Can be overridden by do_center_crop in the
preprocess method.   crop_size (Dict[str, int] optional, defaults to 224) —
Size of the output image after applying center_crop. Can be overridden by crop_size in the preprocess
method.   do_rescale (bool, optional, defaults to True) —
Whether to rescale the image by the specified scale rescale_factor. Can be overridden by do_rescale in
the preprocess method.   rescale_factor (int or float, optional, defaults to 1/255) —
Scale factor to use if rescaling the image. Can be overridden by rescale_factor in the preprocess
method.
do_normalize —
Whether to normal


BitImageProcessor

ize the image. Can be overridden by do_normalize in the preprocess method.   image_mean (float or List[float], optional, defaults to OPENAI_CLIP_MEAN) —
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the image_mean parameter in the preprocess method.   image_std (float or List[float], optional, defaults to OPENAI_CLIP_MEAN) —
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the image_std parameter in the preprocess method.
Can be overridden by the image_std parameter in the preprocess method.   do_convert_rgb (bool, optional, defaults to True) —
Whether to convert the image to RGB.    Constructs a BiT image processor.  preprocess  < source > ( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), ty


BitImageProcessor

ping.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]] do_resize: bool = None size: typing.Dict[str, int] = None resample: Resampling = None do_center_crop: bool = None crop_size: int = None do_rescale: bool = None rescale_factor: float = None do_normalize: bool = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None do_convert_rgb: bool = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: typing.Optional[transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None **kwargs  )   Parameters   images (ImageInput) —
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in


BitImageProcessor

 images with pixel values between 0 and 1, set do_rescale=False.   do_resize (bool, optional, defaults to self.do_resize) —
Whether to resize the image.   size (Dict[str, int], optional, defaults to self.size) —
Size of the image after resizing. Shortest edge of the image is resized to size[“shortest_edge”], with
the longest edge resized to keep the input aspect ratio.   resample (int, optional, defaults to self.resample) —
Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only
has an effect if do_resize is set to True.   do_center_crop (bool, optional, defaults to self.do_center_crop) —
Whether to center crop the image.   crop_size (Dict[str, int], optional, defaults to self.crop_size) —
Size of the center crop. Only has an effect if do_center_crop is set to True.   do_rescale (bool, optional, defaults to self.do_rescale) —
Whether to rescale the image.   rescale_factor (float, optional, default


BitImageProcessor

s to self.rescale_factor) —
Rescale factor to rescale the image by if do_rescale is set to True.   do_normalize (bool, optional, defaults to self.do_normalize) —
Whether to normalize the image.   image_mean (float or List[float], optional, defaults to self.image_mean) —
Image mean to use for normalization. Only has an effect if do_normalize is set to True.   image_std (float or List[float], optional, defaults to self.image_std) —
Image standard deviation to use for normalization. Only has an effect if do_normalize is set to
True.   do_convert_rgb (bool, optional, defaults to self.do_convert_rgb) —
Whether to convert the image to RGB.   return_tensors (str or TensorType, optional) —
The type of tensors to return. Can be one of:
Unset: Return a list of np.ndarray.
TensorType.TENSORFLOW or 'tf': Return a batch of type tf.Tensor.
TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
TensorType.NUMPY or 'np': Return a batch of type np.


BitImageProcessor

ndarray.
TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.
   data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) —
The channel dimension format for the output image. Can be one of:
"channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
"channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
Unset: Use the channel dimension format of the input image.
   input_data_format (ChannelDimension or str, optional) —
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
"channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
"channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
"none" or ChannelDimension.NONE: image in (height, width) format.
    Preprocess an image or batch of image


BitModel

class transformers.BitModel  < source > ( config  )   Parameters   config (BitConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    The bare BiT model outputting raw features without any specific head on top.
This model is a PyTorch torch.nn.Module subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.  forward  < source > ( pixel_values: Tensor output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)


BitModel

  Parameters   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See BitImageProcessor.call()
for details.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)

A transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (BitConfig) an


BitModel

d inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) — Last layer hidden-state after a pooling operation on the spatial dimensions.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, num_channels, height, width).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
  The BitModel forward method, overrides the __call__ special method


BitModel

. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoImageProcessor, BitModel
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> image_processor = AutoImageProcessor.from_pretrained("google/bit-50")
>>> model = BitModel.from_pretrained("google/bit-50")
>>> inputs = image_processor(image, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 2048, 7,


BitForImageClassification

class transformers.BitForImageClassification  < source > ( config  )   Parameters   config (BitConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    BiT Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet. This model is a PyTorch torch.nn.Module subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.  forward  < source > ( pixel_values: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.ImageClassifierOutputWithNoAttention or tupl