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ColPali

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This model was released on 2024-06-27 and added to Hugging Face Transformers on 2024-12-17.

ColPali

ColPali is a model designed to retrieve documents by analyzing their visual features. Unlike traditional systems that rely heavily on text extraction and OCR, ColPali treats each page as an image. It uses Paligemma-3B to capture not only text, but also the layout, tables, charts, and other visual elements to create detailed multi-vector embeddings that can be used for retrieval by computing pairwise late interaction similarity scores. This offers a more comprehensive understanding of documents and enables more efficient and accurate retrieval.

This model was contributed by @tonywu71 (ILLUIN Technology) and @yonigozlan (HuggingFace).

You can find all the original ColPali checkpoints under Vidore’s Hf-native ColVision Models collection.

Click on the ColPali models in the right sidebar for more examples of how to use ColPali for image retrieval.

image retrieval

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 weights to int4.

import requests
import torch
from PIL import Image

from transformers import BitsAndBytesConfig, ColPaliForRetrieval, ColPaliProcessor


model_name = "vidore/colpali-v1.3-hf"

# 4-bit quantization configuration
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

model = ColPaliForRetrieval.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto",
)

processor = ColPaliProcessor.from_pretrained(model_name)

url1 = "https://upload.wikimedia.org/wikipedia/commons/8/89/US-original-Declaration-1776.jpg"
url2 = "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Romeoandjuliet1597.jpg/500px-Romeoandjuliet1597.jpg"

images = [
    Image.open(requests.get(url1, stream=True).raw),
    Image.open(requests.get(url2, stream=True).raw),
]

queries = [
    "When was the United States Declaration of Independence proclaimed?",
    "Who printed the edition of Romeo and Juliet?",
]

# Process the inputs
inputs_images = processor(images=images, return_tensors="pt").to(model.device)
inputs_text = processor(text=queries, return_tensors="pt").to(model.device)

# Forward pass
with torch.no_grad():
    image_embeddings = model(**inputs_images).embeddings
    query_embeddings = model(**inputs_text).embeddings

# Score the queries against the images
scores = processor.score_retrieval(query_embeddings, image_embeddings)

print("Retrieval scores (query x image):")
print(scores)

Notes

score_retrieval() returns a 2D tensor where the first dimension is the number of queries and the second dimension is the number of images. A higher score indicates more similarity between the query and image.

ColPaliConfig

class transformers.ColPaliConfig

< source >

( vlm_config = None text_config = None embedding_dim: int = 128 **kwargs )

Parameters

vlm_config (PreTrainedConfig, optional) — Configuration of the VLM backbone model.
text_config (PreTrainedConfig, optional) — Configuration of the text backbone model. Overrides the text_config attribute of the vlm_config if provided.
embedding_dim (int, optional, defaults to 128) — Dimension of the multi-vector embeddings produced by the model.

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

Creating a configuration with the default settings will result in a configuration where the VLM backbone is set to the default PaliGemma configuration, i.e the one from vidore/colpali-v1.2.

Note that contrarily to what the class name suggests (actually the name refers to the ColPali methodology), you can use a different VLM backbone model than PaliGemma by passing the corresponding VLM configuration to the class constructor.

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.models.colpali import ColPaliConfig, ColPaliForRetrieval

config = ColPaliConfig()
model = ColPaliForRetrieval(config)

ColPaliProcessor

class transformers.ColPaliProcessor

< source >

( image_processor = None tokenizer = None chat_template = None visual_prompt_prefix: str = 'Describe the image.' query_prefix: str = 'Question: ' )

Parameters

image_processor (SiglipImageProcessorFast) — 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.
visual_prompt_prefix (str, optional, defaults to "Describe the image.") — A string that gets tokenized and prepended to the image tokens.
query_prefix (str, optional, defaults to "Question -- "): A prefix to be used for the query.

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

ColPaliProcessor offers all the functionalities of SiglipImageProcessorFast and tokenizer_class. See the ~SiglipImageProcessorFast and ~tokenizer_class 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] = None text: str | list[str] | list[list[str]] = None **kwargs: typing_extensions.Unpack[transformers.models.colpali.processing_colpali.ColPaliProcessorKwargs] ) → BatchFeature

Parameters

images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list, list, list], 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, list], 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.
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.

ColPaliForRetrieval

class transformers.ColPaliForRetrieval

< source >

( config: ColPaliConfig )

Parameters

config (ColPaliConfig) — 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 ColPali architecture 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 ColPali 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, etc.) of a document.

ColPali is part of the ColVision model family, which was first introduced 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

< source >

( input_ids: torch.LongTensor | None = None pixel_values: torch.FloatTensor | None = None attention_mask: torch.Tensor | None = None output_attentions: bool | None = None output_hidden_states: bool | None = None return_dict: bool | None = None **kwargs ) → transformers.models.colpali.modeling_colpali.ColPaliForRetrievalOutput 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 SiglipImageProcessorFast. See SiglipImageProcessorFast.call() for details (ColPaliProcessor uses SiglipImageProcessorFast 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?
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.models.colpali.modeling_colpali.ColPaliForRetrievalOutput or tuple(torch.FloatTensor)

A transformers.models.colpali.modeling_colpali.ColPaliForRetrievalOutput 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 (ColPaliConfig) 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.
past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.

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] | 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.
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.
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 after projecting last hidden state.

The ColPaliForRetrieval 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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