Add CLI commands for predict and train YOLOv10 models. (#13940)

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

docs/en/models/yolov10.md

@@ -122,6 +122,10 @@ Here is a detailed comparison of YOLOv10 variants with other state-of-the-art mo
 
 For predicting new images with YOLOv10:
 
+!!! Example
+
+    === "Python"
+
         ```python
         from ultralytics import YOLO
 
@@ -135,8 +139,19 @@ results = model("image.jpg")
         results[0].show()
         ``` 
 
+    === "CLI"
+
+        ```bash
+        # Load a COCO-pretrained YOLOv10n model and run inference on the 'bus.jpg' image
+        yolo detect predict model=yolov10n.pt source=path/to/bus.jpg
+        ```
+
 For training YOLOv10 on a custom dataset:
 
+!!! Example
+
+    === "Python"
+
         ```python
         from ultralytics import YOLO
 
@@ -147,6 +162,16 @@ model = YOLO("yolov10n.yaml")
         model.train(data="coco8.yaml", epochs=100, imgsz=640)
         ```
     
+    === "CLI"
+
+        ```bash
+        # Build a YOLOv10n model from scratch and train it on the COCO8 example dataset for 100 epochs
+        yolo train model=yolov10n.yaml data=coco8.yaml epochs=100 imgsz=640
+
+        # Build a YOLOv10n model from scratch and run inference on the 'bus.jpg' image
+        yolo predict model=yolov10n.yaml source=path/to/bus.jpg
+        ```
+        
 ## Supported Tasks and Modes
 
 The YOLOv10 models series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and accuracy requirements, making them versatile for a wide array of applications.