---
license: mit
datasets:
- vidore/colpali_train_set
base_model:
- Qwen/Qwen2-VL-7B-Instruct
pipeline_tag: feature-extraction
library_name: transformers
tags:
- vidore
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

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-VL-7B 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 trained with batch_size 256 for 3 epochs.



- **Developed by:** IEIT systems