Update README.md

README.md

@@ -16,7 +16,6 @@ ColQwen is a model based on a novel model architecture and training strategy bas
 It is a [Qwen2.5-VL-3B](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) 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.
 <p align="center"><img width=800 src="https://github.com/illuin-tech/colpali/blob/main/assets/colpali_architecture.webp?raw=true"/></p>
 
 ## Version specificity
@@ -25,7 +24,7 @@ This version is the untrained base version to guarantee deterministic projection
 This model takes dynamic image resolutions in input and does not resize them, changing their aspect ratio as in ColPali.
 Maximal resolution is set so that 768 image patches are created at most. Experiments show clear improvements with larger amounts of image patches, at the cost of memory requirements.
 
-This version is trained with `colpali-engine==0.3.1`.
+This version is trained with `colpali-engine==0.3.7`.
 
 Data is the same as the ColPali data described in the paper.