#
AyaVision

##
Overview

The Aya Vision 8B and 32B models is a state-of-the-art multilingual multimodal models developed by Cohere For AI. They build on the Aya Expanse recipe to handle both visual and textual information without compromising on the strong multilingual textual performance of the original model.

Aya Vision 8B combines the `Siglip2-so400-384-14` vision encoder with the Cohere CommandR-7B language model further post-trained with the Aya Expanse recipe, creating a powerful vision-language model capable of understanding images and generating text across 23 languages. Whereas, Aya Vision 32B uses Aya Expanse 32B as the language model.

Key features of Aya Vision include:

- Multimodal capabilities in 23 languages
- Strong text-only multilingual capabilities inherited from CommandR-7B post-trained with the Aya Expanse recipe and Aya Expanse 32B
- High-quality visual understanding using the Siglip2-so400-384-14 vision encoder
- Seamless integration of visual and textual information in 23 languages.

Tips:

- Aya Vision is a multimodal model that takes images and text as input and produces text as output.
- Images are represented using the `<image>` tag in the templated input.
- For best results, use the `apply_chat_template` method of the processor to format your inputs correctly.
- The model can process multiple images in a single conversation.
- Aya Vision can understand and generate text in 23 languages, making it suitable for multilingual multimodal applications.

This model was contributed by saurabhdash and yonigozlan.

##
Usage

Here’s how to use Aya Vision for inference:

    from transformers import AutoProcessor, AutoModelForImageTextToText
    import torch

    model_id = "CohereForAI/aya-vision-8b"
    torch_device = "cuda:0"

    # Use fast image processor
    processor = AutoProcessor.from_pretrained(model_id, use_fast=True)
    model = AutoModelForImageTextToText.from_pretrained(
        model_id, device_map=torch_device, torch_dtype=torch.float16
    )

    # Format message with the aya-vision chat template
    messages = [
        {"role": "user",
         "content": [
           {"type": "image", "url": "https://pbs.twimg.com/media/Fx7YvfQWYAIp6rZ?format=jpg&name=medium"},
            {"type": "text", "text": "चित्र में लिखा पाठ क्या कहता है?"},
        ]},
        ]

    # Process image on CUDA
    inputs = processor.apply_chat_template(
        messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", device=torch_device
    ).to(model.device)

    gen_tokens = model.generate(
        **inputs,
        max_new_tokens=300,
        do_sample=True,
        temperature=0.3,
    )

    gen_text = print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

###
Pipeline

    from transformers import pipeline

    pipe = pipeline(model="CohereForAI/aya-vision-8b", task="image-text-to-text", device_map="auto")

    # Format message with the aya-vision chat template
    messages = [
        {"role": "user",
         "content": [
           {"type": "image", "url": "https://media.istockphoto.com/id/458012057/photo/istanbul-turkey.jpg?s=612x612&w=0&k=20&c=qogAOVvkpfUyqLUMr_XJQyq-HkACXyYUSZbKhBlPrxo="},
            {"type": "text", "text": "Bu resimde hangi anıt gösterilmektedir?"},
        ]},
        ]
    outputs = pipe(text=messages, max_new_tokens=300, return_full_text=False)

    print(outputs)

###
Multiple Images and Batched Inputs

Aya Vision can process multiple images in a single conversation. Here’s how to use it with multiple images:

    from transformers import AutoProcessor, AutoModelForImageTextToText
    import torch

    model_id = "CohereForAI/aya-vision-8b"

    processor = AutoProcessor.from_pretrained(model_id)
    model = AutoModelForImageTextToText.from_pretrained(
        model_id, device_map="cuda:0", torch_dtype=torch.float16
    )

    # Example with multiple images in a single message
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
                },
                {
                    "type": "image",
                    "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
                },
                {
                    "type": "text",
                    "text": "These images depict two different landmarks. Can you identify them?",
                },
            ],
        },
    ]

    inputs = processor.apply_chat_template(
        messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
    ).to(model.device)

    gen_tokens = model.generate(
        **inputs,
        max_new_tokens=300,
        do_sample=True,
        temperature=0.3,
    )

    gen_text = processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
    print(gen_text)

For processing batched inputs (multiple conversations at once):

    from transformers import AutoProcessor, AutoModelForImageTextToText
    import torch

    model_id = "CohereForAI/aya-vision-8b"

    processor = AutoProcessor.from_pretrained(model_id)
    model = AutoModelForImageTextToText.from_pretrained(
        model_id, device_map="cuda:0", torch_dtype=torch.float16
    )

    # Prepare two different conversations
    batch_messages = [
        # First conversation with a single image
        [
            {
                "role": "user",
                "content": [
                    {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
                    {"type": "text", "text": "Write a haiku for this image"},
                ],
            },
        ],
        # Second conversation with multiple images
        [
            {
                "role": "user",
                "content": [
                    {
                        "type": "image",
                        "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
                    },
                    {
                        "type": "image",
                        "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
                    },
                    {
                        "type": "text",
                        "text": "These images depict two different landmarks. Can you identify them?",
                    },
                ],
            },
        ],
    ]

    # Process each conversation separately and combine into a batch
    batch_inputs = processor.apply_chat_template(
        batch_messages,
        padding=True,
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt"
    ).to(model.device)

    # Generate responses for the batch
    batch_outputs = model.generate(
        **batch_inputs,
        max_new_tokens=300,
        do_sample=True,
        temperature=0.3,
    )

    # Decode the generated responses
    for i, output in enumerate(batch_outputs):
        response = processor.tokenizer.decode(
            output[batch_inputs.input_ids.shape[1]:],
            skip_special_tokens=True
        )
        print(f"Response {i+1}:\n{response}\n")

##
AyaVisionProcessor

###
class transformers.AyaVisionProcessor

< source >

( image_processor = None tokenizer = None patch_size: int = 28 img_size: int = 364 image_token = '<image>' downsample_factor: int = 1 start_of_img_token = '<|START_OF_IMG|>' end_of_img_token = '<|END_OF_IMG|>' img_patch_token = '<|IMG_PATCH|>' img_line_break_token = '<|IMG_LINE_BREAK|>' tile_token = 'TILE' tile_global_token = 'TILE_GLOBAL' chat_template = None **kwargs )

Parameters

- image_processor (AutoImageProcessor, optional) — The image processor is a required input.
- tokenizer ([`PreTrainedTokenizer`, `PreTrainedTokenizerFast`], optional) — The tokenizer is a required input.
- patch_size (`int`, optional, defaults to 28) — The size of image patches for tokenization.
- img_size (`int`, optional, defaults to 364) — The size of the image to be tokenized. This should correspond to the size given to the image processor.
- image_token (`str`, optional, defaults to `"<image>"`) — The token to be used to represent an image in the text.
- downsample_factor (`int`, optional, defaults to 1) — The factor by which to scale the patch size.
- start_of_img_token (`str`, optional, defaults to `"<|START_OF_IMG|>"`) — The token to be used to represent the start of an image in the text.
- end_of_img_token (`str`, optional, defaults to `"<|END_OF_IMG|>"`) — The token to be used to represent the end of an image in the text.
- img_patch_token (`str`, optional, defaults to `"<|IMG_PATCH|>"`) — The token to be used to represent an image patch in the text.
- img_line_break_token (`str`, optional, defaults to `"<|IMG_LINE_BREAK|>"`) — The token to be used to represent a line break in the text.
- tile_token (`str`, optional, defaults to `"TILE"`) — The token to be used to represent an image patch in the text.
- tile_global_token (`str`, optional, defaults to `"TILE_GLOBAL"`) — The token to be used to represent the cover image in the text.
- chat_template (`str`, optional) — A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string.

Constructs a AyaVision processor which wraps a AutoImageProcessor and `PretrainedTokenizerFast` tokenizer into a single processor that inherits both the image processor and tokenizer functionalities. See the `__call__()` and decode() for more information.

####
batch_decode

< source >

( *args **kwargs )

This method forwards all its arguments to PreTrainedTokenizerFast’s batch_decode(). Please refer to the docstring of this method for more information.

####
decode

< source >

( *args **kwargs )

This method forwards all its arguments to PreTrainedTokenizerFast’s decode(). Please refer to the docstring of this method for more information.

##
AyaVisionConfig

###
class transformers.AyaVisionConfig

< source >

( vision_config = None text_config = None vision_feature_select_strategy = 'full' vision_feature_layer = -1 downsample_factor = 2 adapter_layer_norm_eps = 1e-06 image_token_index = 255036 **kwargs )

Parameters

- vision_config (`Union[AutoConfig, dict]`, optional, defaults to `CLIPVisionConfig`) — The config object or dictionary of the vision backbone.
- text_config (`Union[AutoConfig, dict]`, optional, defaults to `LlamaConfig`) — The config object or dictionary of the text backbone.
- vision_feature_select_strategy (`str`, optional, defaults to `"full"`) — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features. If `"full"`, the full vision features are used.
- vision_feature_layer (`int`, optional, defaults to -1) — The index of the layer to select the vision feature.
- downsample_factor (`int`, optional, defaults to 2) — The downsample factor to apply to the vision features.
- adapter_layer_norm_eps (`float`, optional, defaults to 1e-06) — The epsilon value used for layer normalization in the adapter.
- image_token_index (`int`, optional, defaults to 255036) — The image token index to encode the image prompt.

This is the configuration class to store the configuration of a AyaVisionForConditionalGeneration. It is used to instantiate an AyaVision model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of AyaVision. e.g. CohereForAI/aya-vision-8b

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

##
AyaVisionModel

###
class transformers.AyaVisionModel

< source >

( config: AyaVisionConfig )

Parameters

- config (AyaVisionConfig or `AyaVisionVisionConfig`) — 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 AyaVision model which consists of a vision backbone and a language model, without a language modeling head. 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.

####
forward

< source >

( input_ids: LongTensor = None pixel_values: FloatTensor = 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 vision_feature_layer: typing.Union[int, typing.List[int], NoneType] = None vision_feature_select_strategy: typing.Optional[str] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None image_sizes: Tensor = None **lm_kwargs )

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?

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)) -- The tensors corresponding to the input images. Pixel values can be obtained using [AutoImageProcessor](/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor). See [CLIPImageProcessor.__call__()](/docs/transformers/main/en/model_doc/vit#transformers.ViTFeatureExtractor.__call__) for details ([]`AyaVisionProcessor`] uses CLIPImageProcessor for processing images).
- 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_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, 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.
- vision_feature_layer (`Union[int, List[int]], *optional*, defaults to -2`) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features.
- vision_feature_select_strategy (`str`, optional, defaults to `"default"`) — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`.
- 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.
- cache_position (`torch.LongTensor` of shape `(sequence_length)`, optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.

The AyaVisionModel 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.

####
get_image_features

< source >

( pixel_values: FloatTensor vision_feature_layer: typing.Union[int, typing.List[int]] vision_feature_select_strategy: str **kwargs ) →
image_features (`torch.Tensor`)

Parameters

- pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`) — The tensors corresponding to the input images.
- vision_feature_layer (`Union[int, List[int]]`) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features.
- vision_feature_select_strategy (`str`) — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`

Returns

image_features (`torch.Tensor`)

Image feature tensor of shape `(num_images, image_length, embed_dim)`).

Obtains image last hidden states from the vision tower and apply multimodal projection.

##
AyaVisionForConditionalGeneration

###
class transformers.AyaVisionForConditionalGeneration

< source >

( config: AyaVisionConfig )

Parameters

- config (AyaVisionConfig or `AyaVisionVisionConfig`) — 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 AyaVision model which consists of a vision backbone and a language model. 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.

####
forward

< source >

( input_ids: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = 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 vision_feature_layer: typing.Union[int, typing.List[int], NoneType] = None vision_feature_select_strategy: typing.Optional[str] = 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 return_dict: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None logits_to_keep: typing.Union[int, torch.Tensor] = 0 image_sizes: typing.Optional[torch.Tensor] = None **lm_kwargs ) →
`transformers.models.aya_vision.modeling_aya_vision.AyaVisionCausalLMOutputWithPast` 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?

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)) -- The tensors corresponding to the input images. Pixel values can be obtained using [AutoImageProcessor](/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor). See [CLIPImageProcessor.__call__()](/docs/transformers/main/en/model_doc/vit#transformers.ViTFeatureExtractor.__call__) for details ([]`AyaVisionProcessor`] uses CLIPImageProcessor for processing images).
- 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_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, 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.
- vision_feature_layer (`Union[int, List[int]], *optional*, defaults to -2`) — The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features.
- vision_feature_select_strategy (`str`, optional, defaults to `"default"`) — The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`.
- 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.
- cache_position (`torch.LongTensor` of shape `(sequence_length)`, optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.
- 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]`.
- logits_to_keep (`int` or `torch.Tensor`, optional) — If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).

Returns

`transformers.models.aya_vision.modeling_aya_vision.AyaVisionCausalLMOutputWithPast` or `tuple(torch.FloatTensor)`

A `transformers.models.aya_vision.modeling_aya_vision.AyaVisionCausalLMOutputWithPast` 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 (AyaVisionConfig) 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_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, 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 (`torch.FloatTensor`, optional) — A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.

The AyaVisionForConditionalGeneration 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:

    >>> from transformers import AutoProcessor, AyaVisionForConditionalGeneration
    >>> import torch

    >>> torch_device = "cuda:0"
    >>> processor = AutoProcessor.from_pretrained("CohereForAI/aya-vision-8b", use_fast=True)
    >>> model = AyaVisionForConditionalGeneration.from_pretrained("CohereForAI/aya-vision-8b", device_map=torch_device)

    >>> messages = [
    ...     {
    ...         "role": "user",
    ...         "content": [
    ...             {
    ...                 "type": "image",
    ...                 "url": "https://pbs.twimg.com/media/Fx7YvfQWYAIp6rZ?format=jpg&name=medium",
    ...             },
    ...             {"type": "text", "text": "चित्र में लिखा पाठ क्या कहता है?"},
    ...         ],
    ...     }
    ... ]

    >>> inputs = processor.apply_chat_template(
    ...     messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", device=torch_device
    ... ).to(model.device)

    >>> gen_tokens = model.generate(**inputs, max_new_tokens=300, do_sample=True, temperature=0.3)
    >>> processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)

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