Update README.md

README.md

@@ -11,7 +11,7 @@ tags:
 ---
 # ColQwen2: Visual Retriever based on Qwen2-VL-2B-Instruct with ColBERT strategy
 
-### This is the base version trained with batch_size 256 instead of 32 for 1 epoch
+### This is the base version trained with batch_size 256 instead of 32 for 5 epoch and with the updated pad token
 
 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-2B](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) extension that generates [ColBERT](https://arxiv.org/abs/2004.12832)- style multi-vector representations of text and images. 
@@ -63,11 +63,11 @@ from PIL import Image
 from colpali_engine.models import ColQwen2, ColQwen2Processor
 
 model = ColQwen2.from_pretrained(
-        "manu/colqwen2-ba64",
+        "manu/colqwen2-v1.0-alpha",
         torch_dtype=torch.bfloat16,
         device_map="cuda:0",  # or "mps" if on Apple Silicon
     ).eval()
-processor = ColQwen2Processor.from_pretrained("manu/colqwen2-ba64")
+processor = ColQwen2Processor.from_pretrained("manu/colqwen2-v1.0-alpha")
 
 # Your inputs
 images = [