Update README.md

README.md

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 ---
 license: mit
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+## MLCD-ViT-bigG Model Card
+
+
+MLCD-ViT-bigG is a state-of-the-art vision transformer model enhanced with 2D Rotary Position Embedding (RoPE2D), achieving superior performance on document understanding and visual question answering tasks. Developed by DeepGlint AI, this model demonstrates exceptional capabilities in processing complex visual-language interactions.
+
 
 We adopted the official [LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT) and the official training dataset [LLaVA-NeXT-Data](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Data) for evaluating the foundational visual models.  
 
@@ -12,3 +17,35 @@ We adopted the official [LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT) and
 | DFN5B (ViT-H-14-378px)       |   ×    | 64.36     | 70.87     | 38.59     | 473.00     | **48.00** |
 | **MLCD (ViT-L-14-336px)**    |   ×    | 67.84     | 76.46     | 43.48     | 531.00     | 44.30     |
 | **MLCD (ViT-bigG-14-336px)** |   √    | **71.07** | **79.63** | **44.38** | **572.00** | 46.78     |
+
+## Installation
+
+```shell
+pip install torch transformers
+git clone https://github.com/deepglint/unicom
+cd unicom/mlcd
+```
+
+## Usage
+
+```python
+from vit_rope2d_hf import MLCDVisionModel
+from transformers import AutoImageProcessor
+from PIL import Image
+import torch
+
+# Load model and processor
+model = MLCDVisionModel.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-336")
+processor = AutoImageProcessor.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-336")
+
+# Process single image
+image = Image.open("document.jpg").convert("RGB")
+inputs = processor(images=image, return_tensors="pt")
+
+# Get visual features
+with torch.no_grad():
+    outputs = model(**inputs)
+features = outputs.last_hidden_state
+
+print(f"Extracted features shape: {features.shape}")
+```