Update README.md

README.md

@@ -39,8 +39,8 @@ The original code can be found [here](https://github.com/Ucas-HaoranWei/GOT-OCR2
 >>> from transformers import AutoProcessor, AutoModelForImageTextToText
 
 >>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("yonigozlan/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("yonigozlan/GOT-OCR-2.0-hf")
+>>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
+>>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
 
 >>> image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
 >>> inputs = processor(image, return_tensors="pt").to(device)
@@ -63,8 +63,8 @@ The original code can be found [here](https://github.com/Ucas-HaoranWei/GOT-OCR2
 >>> from transformers import AutoProcessor, AutoModelForImageTextToText
 
 >>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("yonigozlan/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("yonigozlan/GOT-OCR-2.0-hf")
+>>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
+>>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
 
 >>> image1 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png"
 >>> image2 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
@@ -91,8 +91,8 @@ GOT-OCR2 can also generate formatted text, such as markdown or LaTeX. Here is an
 >>> from transformers import AutoProcessor, AutoModelForImageTextToText
 
 >>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("yonigozlan/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("yonigozlan/GOT-OCR-2.0-hf")
+>>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
+>>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
 
 >>> image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/latex.png"
 >>> inputs = processor(image, return_tensors="pt", format=True).to(device)
@@ -119,8 +119,8 @@ Here is an example of how to process multiple pages at once:
 >>> from transformers import AutoProcessor, AutoModelForImageTextToText
 
 >>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("yonigozlan/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("yonigozlan/GOT-OCR-2.0-hf")
+>>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
+>>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
 
 >>> image1 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/page1.png"
 >>> image2 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/page2.png"
@@ -148,8 +148,8 @@ Here is an example of how to process cropped patches:
 >>> from transformers import AutoProcessor, AutoModelForImageTextToText
 
 >>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("yonigozlan/GOT-OCR-2.0-hf", torch_dtype=torch.bfloat16, device_map=device)
->>> processor = AutoProcessor.from_pretrained("yonigozlan/GOT-OCR-2.0-hf")
+>>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", torch_dtype=torch.bfloat16, device_map=device)
+>>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
 
 >>> image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/one_column.png"
 >>> inputs = processor(image, return_tensors="pt", format=True, crop_to_patches=True, max_patches=3).to(device)
@@ -174,8 +174,8 @@ GOT supports interactive OCR, where the user can specify the region to be recogn
 >>> from transformers import AutoProcessor, AutoModelForImageTextToText
 
 >>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("yonigozlan/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("yonigozlan/GOT-OCR-2.0-hf")
+>>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
+>>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
 
 >>> image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png"
 >>> inputs = processor(image, return_tensors="pt", color="green").to(device) # or box=[x1, y1, x2, y2] for coordinates (image pixels)
@@ -202,8 +202,8 @@ Here is an example of how to process sheet music:
 >>> import verovio
 
 >>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = AutoModelForImageTextToText.from_pretrained("yonigozlan/GOT-OCR-2.0-hf", device_map=device)
->>> processor = AutoProcessor.from_pretrained("yonigozlan/GOT-OCR-2.0-hf")
+>>> model = AutoModelForImageTextToText.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf", device_map=device)
+>>> processor = AutoProcessor.from_pretrained("stepfun-ai/GOT-OCR-2.0-hf")
 
 >>> image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/sheet_music.png"
 >>> inputs = processor(image, return_tensors="pt", format=True).to(device)