Update README.md

README.md

@@ -21,7 +21,7 @@ With its advanced reasoning capabilities and superior performance on geospatial
 <!-- ![LISAT Model Architecture](https://huggingface.co/jquenum/LISAt-7b/resolve/main/LISAt.png) -->
 
 <p align="center">
-  <img src="https://huggingface.co/jquenum/LISAt-7b/resolve/main/LISAt.png" width="300"/>
+  <img src="https://huggingface.co/jquenum/LISAt-7b/resolve/main/LISAt.png" width="350"/>
 </p>
 
 - **Training data**: we introduce the Geospatial Reasoning Segmentation Dataset (GRES), a collection of vision and language data designed around
@@ -76,7 +76,8 @@ Once your installation is updated, you can use LISAT-7B for inference as follows
 
 This will render as a properly formatted Python code snippet in Markdown. When you view it in a Markdown-rendering environment, it will look like this:
 
-`from transformers import AutoModelForImageSegmentation, AutoTokenizer
+```python
+from transformers import AutoModelForImageSegmentation, AutoTokenizer
 
 # Load model and tokenizer
 model = AutoModelForImageSegmentation.from_pretrained("jquenum/LISAt-7b")
@@ -87,7 +88,8 @@ input_image = "path/to/your/image.png"  # Replace with your input image
 inputs = tokenizer(input_image, return_tensors="pt")
 
 # Generate segmentation or other tasks
-outputs = model.generate(**inputs)`
+outputs = model.generate(**inputs)
+
 
 
 ## Intended Use