ColQwen2.5: Visual Retriever based on Qwen2.5-VL-3B-Instruct with ColBERT strategy
ColQwen 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 Qwen2.5-VL-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
This version is the untrained base version to guarantee deterministic projection layer initialization.
Usage
[!WARNING] This version should not be used: it is solely the base version useful for deterministic LoRA initialization.
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},
}