Update README.md

README.md

@@ -141,60 +141,6 @@ for res in output:
   
 **For more usage details and parameter explanations, see the [documentation](https://www.paddleocr.ai/latest/en/version3.x/pipeline_usage/PaddleOCR-VL.html).**
 
-## PaddleOCR-VL-0.9B Usage with transformers
-
-
-Currently, we support inference using the PaddleOCR-VL-0.9B model with the `transformers` library, which can recognize texts, formulas, tables, and chart elements. In the future, we plan to support full document parsing inference with `transformers`. Below is a simple script we provide to support inference using the PaddleOCR-VL-0.9B model with `transformers`. 
-
-> [!NOTE]
-> Note: We currently recommend using the official method for inference, as it is faster and supports page-level document parsing. The example code below only supports element-level recognition.
-
-
-```python
-from PIL import Image
-import torch
-from transformers import AutoModelForCausalLM, AutoProcessor
-
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-CHOSEN_TASK = "ocr"  # Options: 'ocr' | 'table' | 'chart' | 'formula'
-PROMPTS = {
-    "ocr": "OCR:",
-    "table": "Table Recognition:",
-    "formula": "Formula Recognition:",
-    "chart": "Chart Recognition:",
-}
-
-model_path = "PaddlePaddle/PaddleOCR-VL"
-image_path = "test.png"
-image = Image.open(image_path).convert("RGB")
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_path, trust_remote_code=True, torch_dtype=torch.bfloat16
-).to(DEVICE).eval()
-processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
-
-messages = [
-    {"role": "user",         
-     "content": [
-            {"type": "image", "image": image},
-            {"type": "text", "text": PROMPTS[CHOSEN_TASK]},
-        ]
-    }
-]
-inputs = processor.apply_chat_template(
-    messages, 
-    tokenize=True, 
-    add_generation_prompt=True, 	
-    return_dict=True,
-	return_tensors="pt"
-).to(DEVICE)
-
-outputs = model.generate(**inputs, max_new_tokens=1024)
-outputs = processor.batch_decode(outputs, skip_special_tokens=True)[0]
-print(outputs)
-```
-
 ## Performance
 
 ### Page-Level Document Parsing