colqwen3-base

ColQwen3: Visual Retriever based on Qwen3-VL-2B-Instruct with ColBERT strategy

source code: Mungeryang/colqwen3

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 Qwen3-VL-2B-Instruct 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.

<p align="center"><img width=800 src="https://github.com/illuin-tech/colpali/blob/main/assets/colpali_architecture.webp?raw=true"/></p>

Usage

[!WARNING] This version should not be used: it is solely the base version useful for deterministic LoRA initialization.

Contact

• Mungeryang: [email protected]/[email protected]

Acknowledgments

❤️❤️❤️

[!WARNING] Thanks to the Colpali team and Qwen team for their excellent open-source works! I accomplished this work by standing on the shoulders of giants~

<p align="center"> <img src="https://cdn.mos.cms.futurecdn.net/pqHroHNqYyQoJvEPrYkbcj-1200-80.jpg" width="80%"/> <p>

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}, 
}