---
license: mit
library_name: colpali
base_model: HuggingFaceTB/SmolVLM-256M-Instruct
language:
- en
tags:
- colsmolvlm
- vidore-experimental
- vidore
---
# ColSmolVLM-256M-Instruct: Visual Retriever based on SmolVLM-256M-Instruct with ColBERT strategy

ColSmolVLM 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 SmolVLM extension that generates [ColBERT](https://arxiv.org/abs/2004.12832)- style multi-vector representations of text and images. 
It was introduced in the paper [ColPali: Efficient Document Retrieval with Vision Language Models](https://arxiv.org/abs/2407.01449) and first released in [this repository](https://github.com/ManuelFay/colpali)

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.


## License

ColSmol's vision language backbone model (ColSmolVLM) is under `apache2.0` license. 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:

```bibtex
@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}, 
}
```