base_model: google/paligemma-3b-pt-448
language:
- en
library_name: colpali
license: gemma
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
Usage
This version should not be used: it is solely the base version useful for deterministic LoRA initialization.
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
This version should not be used, it is solely the base version useful for deterministic LoRA initialization !
License
ColPali's vision language backbone model (PaliGemma) is under gemma license as specified in its model card. The adapters attached to the model are under MIT license.
Contact
- Manuel Faysse: manuel.faysse@illuin.tech
- Hugues Sibille: hugues.sibille@illuin.tech
- Tony Wu: tony.wu@illuin.tech
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},
}