Update README.md

README.md

@@ -9,9 +9,9 @@ library_name: transformers
 
 <img alt="olmOCR Logo" src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/olmocr/olmocr.png" width="242px" style="margin-left:'auto' margin-right:'auto' display:'block'">
 
-# olmOCR-7B-1025
+# olmOCR-2-7B-1025
 
-Full BF16 version of [olmOCR-7B-1025-FP8](https://huggingface.co/allenai/olmOCR-7B-1025-FP8).
+Full BF16 version of [olmOCR-2-7B-1025-FP8](https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8).
 We recommend using the FP8 version for all practical purposes except further fine tuning.
 
 This is a release of the olmOCR model that's fine tuned from Qwen2.5-VL-7B-Instruct using the 
@@ -51,7 +51,7 @@ This model scores the following scores on [olmOCR-bench](https://huggingface.co/
   </thead>
   <tbody> 
      <tr>
-      <td align="left">olmOCR pipeline v0.4.0 with olmOCR-7B-1025</td>
+      <td align="left">olmOCR pipeline v0.4.0 with olmOCR-2-7B-1025</td>
       <td align="center">82.9</td>
       <td align="center">82.1</td>
       <td align="center">84.3</td>
@@ -63,7 +63,7 @@ This model scores the following scores on [olmOCR-bench](https://huggingface.co/
       <td align="center">82.3 ± 1.1</td>
     </tr>  
     <tr>
-      <td align="left">olmOCR pipeline v0.4.0 with olmOCR-7B-1025-FP8</td>
+      <td align="left">olmOCR pipeline v0.4.0 with olmOCR-2-7B-1025-FP8</td>
       <td align="center">83.0</td>
       <td align="center">82.3</td>
       <td align="center">84.9</td>
@@ -112,7 +112,7 @@ from olmocr.data.renderpdf import render_pdf_to_base64png
 from olmocr.prompts import build_no_anchoring_v4_yaml_prompt
 
 # Initialize the model
-model = Qwen2_5_VLForConditionalGeneration.from_pretrained("allenai/olmOCR-7B-1025", torch_dtype=torch.bfloat16).eval()
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained("allenai/olmOCR-2-7B-1025", torch_dtype=torch.bfloat16).eval()
 processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)