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Pixtral

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This model was published in HF papers on 2024-10-09 and contributed to Hugging Face Transformers on 2024-09-14.

Pixtral

Pixtral is a multimodal model trained to understand natural images and documents. It accepts images in their natural resolution and aspect ratio without resizing or padding due to its 2D RoPE embeddings. In addition, Pixtral has a long 128K token context window for processing a large number of images. Pixtral couples a 400M vision encoder with a 12B Mistral Nemo decoder.

Pixtral architecture. Taken from the blog post.

You can find all the original Pixtral checkpoints under the Mistral AI organization.

This model was contributed by amyeroberts and ArthurZ. Click on the Pixtral models in the right sidebar for more examples of how to apply Pixtral to different vision and language tasks.

AutoModel

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to quantize the model to 4-bits.

import requests
import torch
from PIL import Image

from transformers import AutoProcessor, BitsAndBytesConfig, LlavaForConditionalGeneration


model_id = "mistral-community/pixtral-12b"

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

model = LlavaForConditionalGeneration.from_pretrained(
    model_id,
    quantization_config=quantization_config,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

dog_url = "https://picsum.photos/id/237/200/300"
mountain_url = "https://picsum.photos/seed/picsum/200/300"
dog_image = Image.open(requests.get(dog_url, stream=True).raw)
mountain_image = Image.open(requests.get(mountain_url, stream=True).raw)

chat = [
    {
      "role": "user", "content": [
        {"type": "text", "text": "Can this animal"},
        {"type": "image"},
        {"type": "text", "text": "live here?"},
        {"type": "image"}
      ]
    }
]

prompt = processor.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = processor(text=prompt, images=[dog_image, mountain_image], return_tensors="pt").to(model.device)

inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)
inputs = {k: v.to(model.device) for k, v in inputs.items()}

generate_ids = model.generate(**inputs, max_new_tokens=100)
output = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(output)

Notes

Pixtral uses PixtralVisionModel as the vision encoder and MistralForCausalLM for its language decoder.
The model internally replaces [IMG] token placeholders with image embeddings.
```
"<s>[INST][IMG]\nWhat are the things I should be cautious about when I visit this place?[/INST]"
```
The [IMG] tokens are replaced with a number of [IMG] tokens that depend on the height and width of each image. Each row of the image is separated by a [IMG_BREAK] token and each image is separated by a [IMG_END] token. Use the ~Processor.apply_chat_template method to handle these tokens for you.

PixtralVisionConfig

class transformers.PixtralVisionConfig

< source >

( transformers_version: str | None = Nonearchitectures: list[str] | None = Noneoutput_hidden_states: bool | None = Falsereturn_dict: bool | None = Truedtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = Nonechunk_size_feed_forward: int = 0is_encoder_decoder: bool = Falseid2label: dict[int, str] | dict[str, str] | None = Nonelabel2id: dict[str, int] | dict[str, str] | None = Noneproblem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = Nonehidden_size: int = 1024intermediate_size: int = 4096num_hidden_layers: int = 24num_attention_heads: int = 16num_channels: int = 3image_size: int | list[int] | tuple[int, int] = 1024patch_size: int | list[int] | tuple[int, int] = 16hidden_act: str = 'gelu'attention_dropout: float | int = 0.0rope_parameters: transformers.modeling_rope_utils.RopeParameters | dict | None = Noneinitializer_range: float = 0.02 )

Parameters

hidden_size (int, optional, defaults to 1024) — Dimension of the hidden representations.
intermediate_size (int, optional, defaults to 4096) — Dimension of the MLP representations.
num_hidden_layers (int, optional, defaults to 24) — Number of hidden layers in the Transformer decoder.
num_attention_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer decoder.
num_channels (int, optional, defaults to 3) — The number of input channels.
image_size (Union[int, list[int], tuple[int, int]], optional, defaults to 1024) — The size (resolution) of each image.
patch_size (Union[int, list[int], tuple[int, int]], optional, defaults to 16) — The size (resolution) of each patch.
hidden_act (str, optional, defaults to gelu) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
attention_dropout (Union[float, int], optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
rope_parameters (Union[~modeling_rope_utils.RopeParameters, dict], optional) — Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for rope_theta and optionally parameters used for scaling in case you want to use RoPE with longer max_position_embeddings.
initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

This is the configuration class to store the configuration of a PixtralVisionModel. It is used to instantiate a Pixtral 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 mistral-labs/pixtral-12b

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

Example:

>>> from transformers import PixtralVisionModel, PixtralVisionConfig

>>> # Initializing a Pixtral-12B style configuration
>>> config = PixtralVisionConfig()

>>> # Initializing a model (with randomly initialized weights) from the configuration
>>> model = PixtralVisionModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

MistralCommonBackend

class transformers.MistralCommonBackend

< source >

( *args**kwargs )

PixtralVisionModel

class transformers.PixtralVisionModel

< source >

( config )

Parameters

config (PixtralVisionModel) — 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 Pixtral 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.

forward

< source >

( pixel_values: Tensorimage_sizes: typing.Optional[torch.Tensor] = None**kwargs: Unpack ) → BaseModelOutput or tuple(torch.FloatTensor)

Parameters

pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained using PixtralImageProcessor. See PixtralImageProcessor.__call__() for details (PixtralProcessor uses PixtralImageProcessor for processing images).
image_sizes (torch.Tensor of shape (batch_size, 2), optional) — The sizes of the images in the batch, being (height, width) for each image.

Returns

BaseModelOutput or tuple(torch.FloatTensor)

A BaseModelOutput 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 (PixtralVisionConfig) and inputs.

The PixtralVisionModel 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.

last_hidden_state (torch.FloatTensor 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(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.

PixtralImageProcessor

class transformers.PixtralImageProcessor

< source >

( **kwargs: Unpack )

Parameters

do_convert_rgb (bool, kwargs, optional, defaults to True) — Whether to convert the image to RGB.
do_resize (bool, kwargs, optional, defaults to True) — Whether to resize the image.
size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs, defaults to {'longest_edge' -- 1024}): Describes the maximum input dimensions to the model.
default_to_square (bool, kwargs, optional, defaults to True) — Whether to default to a square image when resizing, if size is an int.
crop_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applying center_crop.
resample (Annotated[Union[int, PILImageResampling, NoneType], None], kwargs, defaults to Resampling.BICUBIC) — 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_rescale (bool, kwargs, optional, defaults to True) — Whether to rescale the image.
rescale_factor (float, kwargs, optional, defaults to 0.00392156862745098) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, kwargs, optional, defaults to True) — Whether to normalize the image.
image_mean (Union[float, list[float], tuple[float, ...]], kwargs, optional, defaults to [0.48145466, 0.4578275, 0.40821073]) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
image_std (Union[float, list[float], tuple[float, ...]], kwargs, optional, defaults to [0.26862954, 0.26130258, 0.27577711]) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
do_pad (bool, kwargs, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model.
pad_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in {"height": int, "width" int} to pad the images to. Must be larger than any image size provided for preprocessing. If pad_size is not provided, images will be padded to the largest height and width in the batch. Applied only when do_pad=True.
do_center_crop (bool, kwargs, optional) — Whether to center crop the image.
data_format (Union[str, ~image_utils.ChannelDimension], kwargs, optional) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
input_data_format (Union[str, ~image_utils.ChannelDimension], kwargs, 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.
device (Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos.
return_tensors (Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
disable_grouping (bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
image_seq_length (int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.
patch_size (Union[dict[str, *kwargs*, int], int] optional, defaults to {"height" -- 16, "width": 16}): Size of the patches in the model, used to calculate the output image size.

Constructs a PixtralImageProcessor image processor.

preprocess

< source >

Parameters

images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
do_convert_rgb (bool, kwargs, optional) — Whether to convert the image to RGB.
do_resize (bool, kwargs, optional) — Whether to resize the image.
size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Describes the maximum input dimensions to the model.
default_to_square (bool, kwargs, optional) — Whether to default to a square image when resizing, if size is an int.
crop_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applying center_crop.
resample (Annotated[Union[int, PILImageResampling, NoneType], None], kwargs) — 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_rescale (bool, kwargs, optional) — Whether to rescale the image.
rescale_factor (float, kwargs, optional) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, kwargs, optional) — Whether to normalize the image.
image_mean (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
image_std (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
do_pad (bool, kwargs, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model.
pad_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in {"height": int, "width" int} to pad the images to. Must be larger than any image size provided for preprocessing. If pad_size is not provided, images will be padded to the largest height and width in the batch. Applied only when do_pad=True.
do_center_crop (bool, kwargs, optional) — Whether to center crop the image.
data_format (Union[str, ~image_utils.ChannelDimension], kwargs, optional) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
input_data_format (Union[str, ~image_utils.ChannelDimension], kwargs, 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.
device (Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos.
return_tensors (Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
disable_grouping (bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
image_seq_length (int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.
patch_size (Union[dict[str, *kwargs*, int], int] optional, defaults to {"height" -- 16, "width": 16}): Size of the patches in the model, used to calculate the output image size.

Returns

~image_processing_base.BatchFeature

data (dict) — Dictionary of lists/arrays/tensors returned by the call method (‘pixel_values’, etc.).
tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

PixtralImageProcessorPil

class transformers.PixtralImageProcessorPil

< source >

( **kwargs: Unpack )

Parameters

do_convert_rgb (bool, kwargs, optional, defaults to True) — Whether to convert the image to RGB.
do_resize (bool, kwargs, optional, defaults to True) — Whether to resize the image.
size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs, defaults to {'longest_edge' -- 1024}): Describes the maximum input dimensions to the model.
default_to_square (bool, kwargs, optional, defaults to True) — Whether to default to a square image when resizing, if size is an int.
crop_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applying center_crop.
resample (Annotated[Union[int, PILImageResampling, NoneType], None], kwargs, defaults to Resampling.BICUBIC) — 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_rescale (bool, kwargs, optional, defaults to True) — Whether to rescale the image.
rescale_factor (float, kwargs, optional, defaults to 0.00392156862745098) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, kwargs, optional, defaults to True) — Whether to normalize the image.
image_mean (Union[float, list[float], tuple[float, ...]], kwargs, optional, defaults to [0.48145466, 0.4578275, 0.40821073]) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
image_std (Union[float, list[float], tuple[float, ...]], kwargs, optional, defaults to [0.26862954, 0.26130258, 0.27577711]) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
do_pad (bool, kwargs, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model.
pad_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in {"height": int, "width" int} to pad the images to. Must be larger than any image size provided for preprocessing. If pad_size is not provided, images will be padded to the largest height and width in the batch. Applied only when do_pad=True.
do_center_crop (bool, kwargs, optional) — Whether to center crop the image.
data_format (Union[str, ~image_utils.ChannelDimension], kwargs, optional) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
input_data_format (Union[str, ~image_utils.ChannelDimension], kwargs, 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.
device (Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos.
return_tensors (Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
disable_grouping (bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
image_seq_length (int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.
patch_size (Union[dict[str, *kwargs*, int], int] optional, defaults to {"height" -- 16, "width": 16}): Size of the patches in the model, used to calculate the output image size.

Constructs a PixtralImageProcessor image processor.

preprocess

< source >

Parameters

images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
do_convert_rgb (bool, kwargs, optional) — Whether to convert the image to RGB.
do_resize (bool, kwargs, optional) — Whether to resize the image.
size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Describes the maximum input dimensions to the model.
default_to_square (bool, kwargs, optional) — Whether to default to a square image when resizing, if size is an int.
crop_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — Size of the output image after applying center_crop.
resample (Annotated[Union[int, PILImageResampling, NoneType], None], kwargs) — 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_rescale (bool, kwargs, optional) — Whether to rescale the image.
rescale_factor (float, kwargs, optional) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, kwargs, optional) — Whether to normalize the image.
image_mean (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
image_std (Union[float, list[float], tuple[float, ...]], kwargs, optional) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
do_pad (bool, kwargs, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model.
pad_size (Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None], kwargs) — The size in {"height": int, "width" int} to pad the images to. Must be larger than any image size provided for preprocessing. If pad_size is not provided, images will be padded to the largest height and width in the batch. Applied only when do_pad=True.
do_center_crop (bool, kwargs, optional) — Whether to center crop the image.
data_format (Union[str, ~image_utils.ChannelDimension], kwargs, optional) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
input_data_format (Union[str, ~image_utils.ChannelDimension], kwargs, 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.
device (Annotated[Union[str, torch.device, NoneType], None], kwargs) — The device to process the videos on. If unset, the device is inferred from the input videos.
return_tensors (Annotated[str | ~utils.generic.TensorType | None, None], kwargs) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
disable_grouping (bool, kwargs, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
image_seq_length (int, kwargs, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.
patch_size (Union[dict[str, *kwargs*, int], int] optional, defaults to {"height" -- 16, "width": 16}): Size of the patches in the model, used to calculate the output image size.

Returns

~image_processing_base.BatchFeature

data (dict) — Dictionary of lists/arrays/tensors returned by the call method (‘pixel_values’, etc.).
tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

PixtralProcessor

class transformers.PixtralProcessor

< source >

( image_processor = Nonetokenizer = Nonepatch_size: int = 16spatial_merge_size: int = 1chat_template = Noneimage_token = '[IMG]'image_break_token = '[IMG_BREAK]'image_end_token = '[IMG_END]'**kwargs )

Parameters

image_processor (PixtralImageProcessor) — The image processor is a required input.
tokenizer (TokenizersBackend) — The tokenizer is a required input.
patch_size (int, optional, defaults to 16) — Patch size from the vision tower.
spatial_merge_size (int, optional, defaults to 1) — The downsampling factor for the spatial merge operation.
chat_template (str) — A Jinja template to convert lists of messages in a chat into a tokenizable string.
image_token (str, optional, defaults to "[IMG]") — Special token used to denote image location.
image_break_token (str, optional, defaults to "[IMG_BREAK]") — Special token used to denote the end of a line of pixels in an image.
image_end_token (str, optional, defaults to "[IMG_END]") — Special token used to denote the end of an image input.

Constructs a PixtralProcessor which wraps a image processor and a tokenizer into a single processor.

PixtralProcessor offers all the functionalities of PixtralImageProcessor and TokenizersBackend. See the ~PixtralImageProcessor and ~TokenizersBackend for more information.

call

< source >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = Nonetext: str | list[str] | list[list[str]] | None = Nonevideos: typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]], NoneType] = Noneaudio: typing.Union[numpy.ndarray, ForwardRef('torch.Tensor'), collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence['torch.Tensor'], NoneType] = None**kwargs: Unpack )

Parameters

images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]], optional) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
text (Union[str, list[str], list[list[str]]], optional) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If you pass a pretokenized input, set is_split_into_words=True to avoid ambiguity with batched inputs.
videos (Union[list[PIL.Image.Image], numpy.ndarray, torch.Tensor, list[numpy.ndarray], list[torch.Tensor], list[list[PIL.Image.Image]], list[list[numpy.ndarray]], list[list[torch.Tensor]], ~video_utils.URL, list[~video_utils.URL], list[list[~video_utils.URL]], ~video_utils.Path, list[~video_utils.Path], list[list[~video_utils.Path]]], optional) — Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If passing in videos with pixel values between 0 and 1, set do_rescale=False.
audio (Union[numpy.ndarray, torch.Tensor, collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence[torch.Tensor]], optional) — The audio or batch of audios to be prepared. Each audio can be a NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels, and T is the sample length of the audio.
return_tensors (str or TensorType, optional) — If set, will return tensors of a particular framework. Acceptable values are:
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return NumPy np.ndarray objects.
**kwargs (ProcessingKwargs, optional) — Additional processing options for each modality (text, images, videos, audio). Model-specific parameters are listed above; see the TypedDict class for the complete list of supported arguments.

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