Fix Prettier docs issues (#17798)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/models/yolo-nas.md

@@ -149,7 +149,8 @@ YOLO-NAS introduces several key features that make it a superior choice for obje
 - **Quantization-Friendly Basic Block:** Enhanced architecture that improves model performance with minimal [precision](https://www.ultralytics.com/glossary/precision) drop post quantization.
 - **Sophisticated Training and Quantization:** Employs advanced training schemes and post-training quantization techniques.
 - **AutoNAC Optimization and Pre-training:** Utilizes AutoNAC optimization and is pre-trained on prominent datasets like COCO, Objects365, and Roboflow 100.
-    These features contribute to its high accuracy, efficient performance, and suitability for deployment in production environments. Learn more in the [Key Features](#key-features) section.
+
+These features contribute to its high accuracy, efficient performance, and suitability for deployment in production environments. Learn more in the [Key Features](#key-features) section.
 
 ### Which tasks and modes are supported by YOLO-NAS models?
 

docs/en/models/yolov7.md

@@ -151,4 +151,5 @@ YOLOv7 offers several key features that revolutionize real-time object detection
 - **Dynamic Label Assignment**: Uses a coarse-to-fine lead guided method to assign dynamic targets for outputs across different branches, improving accuracy.
 - **Extended and Compound Scaling**: Efficiently utilizes parameters and computation to scale the model for various real-time applications.
 - **Efficiency**: Reduces parameter count by 40% and computation by 50% compared to other state-of-the-art models while achieving faster inference speeds.
-    For further details on these features, see the [YOLOv7 Overview](#overview) section.
+
+For further details on these features, see the [YOLOv7 Overview](#overview) section.