ColPali: Visual Retriever based on PaliGemma-3B with ColBERT strategy
ColPali is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. It is a PaliGemma-3B extension that generates ColBERT- style multi-vector representations of text and images. It was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models and first released in this repository
Version specificity
This version is similar to
vidore/colpali-v1.2
, except that the LoRA adapter was merged into the base model. Thus, loading ColPali from this checkpoint saves you the trouble of merging the pre-trained adapter yourself.This can be useful if you want to train a new adpter from scratch.
Model Description
This model is built iteratively starting from an off-the-shelf SigLIP model. We finetuned it to create BiSigLIP and fed the patch-embeddings output by SigLIP to an LLM, PaliGemma-3B to create BiPali.
One benefit of inputting image patch embeddings through a language model is that they are natively mapped to a latent space similar to textual input (query). This enables leveraging the ColBERT strategy to compute interactions between text tokens and image patches, which enables a step-change improvement in performance compared to BiPali.
Model Training
Dataset
Our training dataset of 127,460 query-page pairs is comprised of train sets of openly available academic datasets (63%) and a synthetic dataset made up of pages from web-crawled PDF documents and augmented with VLM-generated (Claude-3 Sonnet) pseudo-questions (37%). Our training set is fully English by design, enabling us to study zero-shot generalization to non-English languages. We explicitly verify no multi-page PDF document is used both ViDoRe and in the train set to prevent evaluation contamination. A validation set is created with 2% of the samples to tune hyperparameters.
Note: Multilingual data is present in the pretraining corpus of the language model (Gemma-2B) and potentially occurs during PaliGemma-3B's multimodal training.
Parameters
All models are trained for 1 epoch on the train set. Unless specified otherwise, we train models in bfloat16
format, use low-rank adapters (LoRA)
with alpha=32
and r=32
on the transformer layers from the language model,
as well as the final randomly initialized projection layer, and use a paged_adamw_8bit
optimizer.
We train on an 8 GPU setup with data parallelism, a learning rate of 5e-5 with linear decay with 2.5% warmup steps, and a batch size of 32.
Usage
Install colpali-engine
:
pip install colpali-engine>=0.3.0,<0.4.0
Then run the following code:
from typing import cast
import torch
from PIL import Image
from colpali_engine.models import ColPali, ColPaliProcessor
model_name = "vidore/colpali-v1.2-merged"
model = ColPali.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="cuda:0", # or "mps" if on Apple Silicon
).eval()
processor = ColPaliProcessor.from_pretrained(model_name)
# Your inputs
images = [
Image.new("RGB", (32, 32), color="white"),
Image.new("RGB", (16, 16), color="black"),
]
queries = [
"Is attention really all you need?",
"Are Benjamin, Antoine, Merve, and Jo best friends?",
]
# Process the inputs
batch_images = processor.process_images(images).to(model.device)
batch_queries = processor.process_queries(queries).to(model.device)
# Forward pass
with torch.no_grad():
image_embeddings = model(**batch_images)
querry_embeddings = model(**batch_queries)
scores = processor.score_multi_vector(querry_embeddings, image_embeddings)
Limitations
- Focus: The model primarily focuses on PDF-type documents and high-ressources languages, potentially limiting its generalization to other document types or less represented languages.
- Support: The model relies on multi-vector retreiving derived from the ColBERT late interaction mechanism, which may require engineering efforts to adapt to widely used vector retrieval frameworks that lack native multi-vector support.
License
ColPali's vision language backbone model (PaliGemma) is under gemma
license as specified in its model card.
Because the pre-trained adapter got merged in this model, the license for these weights are also under the gemma
license
Contact
- Manuel Faysse: [email protected]
- Hugues Sibille: [email protected]
- Tony Wu: [email protected]
Citation
If you use any datasets or models from this organization in your research, please cite the original dataset as follows:
@misc{faysse2024colpaliefficientdocumentretrieval,
title={ColPali: Efficient Document Retrieval with Vision Language Models},
author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2024},
eprint={2407.01449},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2407.01449},
}
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