Update README.md

README.md

@@ -19,7 +19,7 @@ Where the first category has both a lightweight visual encoder and a language de
 DIVE-Doc contains a small visual encoder in combination with a large decoder in order to balance model size and performance.
 It is built by distilling the [SigLIP-400m](https://arxiv.org/abs/2303.15343) visual encoder of [PaliGEMMA](https://arxiv.org/abs/2407.07726) into a small hierarchical [Swin transformer](https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Swin_Transformer_Hierarchical_Vision_Transformer_Using_Shifted_Windows_ICCV_2021_paper) initialized with the weights of [Donut](https://link.springer.com/chapter/10.1007/978-3-031-19815-1_29), while reusing the original [GEMMA](https://arxiv.org/abs/2403.08295) decoder. 
 This enables DIVE‑Doc to reduce its visual encoder’s parameter count by 80%.
-Moreover, the model is finetuned using LoRA adapters, which have been merged into the base model using [merge_and_unload](https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraModel.merge_and_unload)).
+Moreover, the model is finetuned using LoRA adapters, which have been merged into the base model using [merge_and_unload](https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraModel.merge_and_unload).
 Trained on the [DocVQA dataset](https://openaccess.thecvf.com/content/WACV2021/html/Mathew_DocVQA_A_Dataset_for_VQA_on_Document_Images_WACV_2021_paper.html) for both the distillation and finetuning steps, this strategy allows DIVE-Doc to be competitive with LVLMs while outperforming ligthweight architectures.