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ColModernVBert

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This model was released on 2025-10-01 and added to Hugging Face Transformers on 2026-02-23.

ColModernVBert

PyTorch FlashAttention SDPA

Overview

ColModernVBert is a model for efficient visual document retrieval. It leverages ModernVBert to construct multi-vector embeddings directly from document images, following the ColPali approach.

The model was introduced in ModernVBERT: Towards Smaller Visual Document Retrievers.

Python 
import torch
from PIL import Image
from huggingface_hub import hf_hub_download
from transformers import ColModernVBertProcessor, ColModernVBertForRetrieval

processor = ColModernVBertProcessor.from_pretrained("ModernVBERT/colmodernvbert-hf")
model = ColModernVBertForRetrieval.from_pretrained("ModernVBERT/colmodernvbert-hf")

# Load the test dataset
queries = [
    "A paint on the wall",
    "ColModernVBERT matches the performance of models nearly 10x larger on visual document benchmarks."
]

images = [
    Image.open(hf_hub_download("HuggingFaceTB/SmolVLM", "example_images/rococo.jpg", repo_type="space")),
    Image.open(hf_hub_download("ModernVBERT/colmodernvbert", "table.png", repo_type="model"))
]

# Preprocess the examples
batch_images = processor(images=images).to(model.device)
batch_queries = processor(text=queries).to(model.device)

# Run inference
with torch.inference_mode():
    image_embeddings = model(**batch_images).embeddings
    query_embeddings = model(**batch_queries).embeddings

# Compute retrieval scores
scores = processor.score_retrieval(
    query_embeddings=query_embeddings,
    passage_embeddings=image_embeddings,
)

scores = torch.softmax(scores, dim=-1)

print(scores)    # [[0.9350, 0.0650], [0.0015, 0.9985]]

ColModernVBertConfig

class transformers.ColModernVBertConfig

< >

( vlm_config = None embedding_dim: int = 128 initializer_range: float = 0.02 **kwargs )

Parameters

  • vlm_config (PreTrainedConfig, optional) — Configuration of the VLM backbone model.
  • embedding_dim (int, optional, defaults to 128) — Dimension of the multi-vector embeddings produced by the model.
  • initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

Configuration class to store the configuration of a ColModernVBertForRetrieval. It is used to instantiate an instance of ColModernVBertForRetrieval according to the specified arguments, defining the model architecture following the methodology from the “ColPali: Efficient Document Retrieval with Vision Language Models” paper.

Instantiating a configuration with the defaults will yield a similar configuration to the vision encoder used by the pre-trained ColModernVBert model, e.g. ModernVBERT/colmodernvbert-merged.

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 ColModernVBertConfig, ColModernVBertForRetrieval

config = ColModernVBertConfig()
model = ColModernVBertForRetrieval(config)

ColModernVBertProcessor

class transformers.ColModernVBertProcessor

< >

( image_processor tokenizer = None chat_template = None image_seq_len: int = 64 visual_prompt_prefix: str | None = None query_prefix: str | None = None **kwargs )

Parameters

  • image_processor (image_processor_class) — The image processor is a required input.
  • tokenizer (tokenizer_class) — The tokenizer is a required input.
  • chat_template (str) — A Jinja template to convert lists of messages in a chat into a tokenizable string.
  • image_seq_len (int, optional, defaults to 64) — The length of the image sequence i.e. the number of tokens per image in the input.
  • visual_prompt_prefix (str, optional) — A string that gets tokenized and prepended to the image tokens.
  • query_prefix (str, optional) — A prefix to be used for the query.

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

ColModernVBertProcessor offers all the functionalities of image_processor_class and tokenizer_class. See the ~image_processor_class and ~tokenizer_class for more information.

process_images

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None **kwargs: typing_extensions.Unpack[transformers.models.colmodernvbert.processing_colmodernvbert.ColModernVBertProcessorKwargs] ) → BatchFeature

Parameters

  • images (PIL.Image.Image, np.ndarray, torch.Tensor, list[PIL.Image.Image], list[np.ndarray], list[torch.Tensor]) — The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width.
  • 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.

Returns

BatchFeature

A BatchFeature with the following fields:

  • input_ids — List of token ids to be fed to a model.
  • attention_mask — List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names and if text is not None).
  • pixel_values — Pixel values to be fed to a model. Returned when images is not None.

Prepare for the model one or several image(s). Handles input validation, RGB conversion, and prepends the visual_prompt_prefix to each image. Optionally computes labels from token_type_ids when a suffix is provided in text_kwargs.

process_queries

< >

( text: str | list[str] **kwargs: typing_extensions.Unpack[transformers.models.colmodernvbert.processing_colmodernvbert.ColModernVBertProcessorKwargs] ) → BatchFeature

Parameters

  • text (str, list[str], list[list[str]]) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
  • 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.

Returns

BatchFeature

A BatchFeature with the following fields:

  • input_ids — List of token ids to be fed to a model.
  • attention_mask — List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names and if text is not None).

Prepare for the model one or several text queries. Handles input validation, prepends the query_prefix, and appends query augmentation tokens (used to pad query embeddings for better late-interaction retrieval performance).

score_retrieval

< >

( query_embeddings: typing.Union[ForwardRef('torch.Tensor'), list['torch.Tensor']] passage_embeddings: typing.Union[ForwardRef('torch.Tensor'), list['torch.Tensor']] batch_size: int = 128 output_dtype: typing.Optional[ForwardRef('torch.dtype')] = None output_device: typing.Union[ForwardRef('torch.device'), str] = 'cpu' ) → torch.Tensor

Parameters

  • query_embeddings (Union[torch.Tensor, list[torch.Tensor]) — Query embeddings.
  • passage_embeddings (Union[torch.Tensor, list[torch.Tensor]) — Passage embeddings.
  • batch_size (int, optional, defaults to 128) — Batch size for computing scores.
  • output_dtype (torch.dtype, optional, defaults to torch.float32) — The dtype of the output tensor. If None, the dtype of the input embeddings is used.
  • output_device (torch.device or str, optional, defaults to “cpu”) — The device of the output tensor.

Returns

torch.Tensor

A tensor of shape (n_queries, n_passages) containing the scores. The score tensor is saved on the “cpu” device.

Compute the late-interaction/MaxSim score (ColBERT-like) for the given multi-vector query embeddings (qs) and passage embeddings (ps). For ColQwen2, a passage is the image of a document page.

Because the embedding tensors are multi-vector and can thus have different shapes, they should be fed as: (1) a list of tensors, where the i-th tensor is of shape (sequence_length_i, embedding_dim) (2) a single tensor of shape (n_passages, max_sequence_length, embedding_dim) -> usually obtained by padding the list of tensors.

ColModernVBertForRetrieval

class transformers.ColModernVBertForRetrieval

< >

( config: ColModernVBertConfig )

Parameters

  • config (ColModernVBertConfig) — 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.

Following the ColPali approach, ColModernVBert leverages VLMs to construct efficient multi-vector embeddings directly from document images (“screenshots”) for document retrieval. The model is trained to maximize the similarity between these document embeddings and the corresponding query embeddings, using the late interaction method introduced in ColBERT.

Using ColModernVBert removes the need for potentially complex and brittle layout recognition and OCR pipelines with a single model that can take into account both the textual and visual content (layout, charts, …) of a document.

ColModernVBert is trained on top of ModernVBert, and was introduced in the following paper: ModernVBERT: Towards Smaller Visual Document Retrievers.

ColModernVBert is part of the ColVision model family, which was introduced with ColPali in the following paper: ColPali: Efficient Document Retrieval with Vision Language Models.

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

< >

( input_ids: torch.LongTensor | None = None pixel_values: torch.FloatTensor | None = None attention_mask: torch.Tensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.models.colmodernvbert.modeling_colmodernvbert.ColModernVBertForRetrievalOutput or tuple(torch.FloatTensor)

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

    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), optional) — The tensors corresponding to the input images. Pixel values can be obtained using image_processor_class. See image_processor_class.__call__ for details (ColModernVBertProcessor uses image_processor_class 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?

Returns

transformers.models.colmodernvbert.modeling_colmodernvbert.ColModernVBertForRetrievalOutput or tuple(torch.FloatTensor)

A transformers.models.colmodernvbert.modeling_colmodernvbert.ColModernVBertForRetrievalOutput 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 (ColModernVBertConfig) and inputs.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).

  • embeddings (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — The embeddings of the model.

  • hidden_states (tuple[torch.FloatTensor] | None.hidden_states, 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.

  • image_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True and pixel_values are provided) — Tuple of torch.FloatTensor (one for the output of the image modality projection + one for the output of each layer) of shape (batch_size, num_channels, image_size, image_size). Hidden-states of the image encoder at the output of each layer plus the initial modality projection outputs.

  • attentions (tuple[torch.FloatTensor] | None.attentions, 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.

The ColModernVBertForRetrieval 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:

Update on GitHub

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