Update README.md

README.md

@@ -52,9 +52,9 @@ To use this model with images, you’ll need the necessary components: the `ViTI
 from transformers import VisionEncoderDecoderModel, ViTImageProcessor, BartTokenizer
 
 # Load model, processor, and tokenizer
-model = VisionEncoderDecoderModel.from_pretrained("BlackEyesBrownDragon/Dua-Vision-Base")
-image_processor = ViTImageProcessor.from_pretrained("BlackEyesBrownDragon/Dua-Vision-Base")
-tokenizer = BartTokenizer.from_pretrained("BlackEyesBrownDragon/Dua-Vision-Base")
+model = VisionEncoderDecoderModel.from_pretrained("HV-Khurdula/Dua-Vision-Base")
+image_processor = ViTImageProcessor.from_pretrained("HV-Khurdula/Dua-Vision-Base")
+tokenizer = BartTokenizer.from_pretrained("HV-Khurdula/Dua-Vision-Base")
 ```
 
 ## Inference Example
@@ -62,15 +62,6 @@ tokenizer = BartTokenizer.from_pretrained("BlackEyesBrownDragon/Dua-Vision-Base"
 Here's a sample usage for generating captions for an image:
 
 ```python
-from PIL import Image
-import requests
-from transformers import VisionEncoderDecoderModel, ViTImageProcessor, BartTokenizer
-
-# Load model and processors
-model = VisionEncoderDecoderModel.from_pretrained("BlackEyesBrownDragon/Dua-Vision-Base")
-image_processor = ViTImageProcessor.from_pretrained("BlackEyesBrownDragon/Dua-Vision-Base")
-tokenizer = BartTokenizer.from_pretrained("BlackEyesBrownDragon/Dua-Vision-Base")
-
 # Load image and process
 image_url = "https://example.com/image.jpg"
 image = Image.open(requests.get(image_url, stream=True).raw)