Fixed OpenVINO Docs formatting (#14773)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/integrations/openvino.md

@@ -64,6 +64,8 @@ Export a YOLOv8n model to OpenVINO format and run inference with the exported mo
 | `format` | `'openvino'` | format to export to                                  |
 | `imgsz`  | `640`        | image size as scalar or (h, w) list, i.e. (640, 480) |
 | `half`   | `False`      | FP16 quantization                                    |
+| `int8`   | `False`      | INT8 quantization                                    |
+| `batch`  | `1`          | batch size for inference                             |
 
 ## Benefits of OpenVINO
 
@@ -262,14 +264,14 @@ To reproduce the Ultralytics benchmarks above on all export [formats](../modes/e
         # Load a YOLOv8n PyTorch model
         model = YOLO("yolov8n.pt")
 
-        # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all all export formats
+        # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all export formats
         results = model.benchmarks(data="coco8.yaml")
         ```
 
     === "CLI"
 
         ```bash
-        # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all all export formats
+        # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all export formats
         yolo benchmark model=yolov8n.pt data=coco8.yaml
         ```
 
@@ -295,22 +297,22 @@ Exporting YOLOv8 models to the OpenVINO format can significantly enhance CPU spe
 
     === "Python"
 
-    ```python
-    from ultralytics import YOLO
+        ```python
+        from ultralytics import YOLO
 
-    # Load a YOLOv8n PyTorch model
-    model = YOLO("yolov8n.pt")
+        # Load a YOLOv8n PyTorch model
+        model = YOLO("yolov8n.pt")
 
-    # Export the model
-    model.export(format="openvino")  # creates 'yolov8n_openvino_model/'
-    ```
+        # Export the model
+        model.export(format="openvino")  # creates 'yolov8n_openvino_model/'
+        ```
 
     === "CLI"
 
-    ```bash
-    # Export a YOLOv8n PyTorch model to OpenVINO format
-    yolo export model=yolov8n.pt format=openvino  # creates 'yolov8n_openvino_model/'
-    ```
+        ```bash
+        # Export a YOLOv8n PyTorch model to OpenVINO format
+        yolo export model=yolov8n.pt format=openvino  # creates 'yolov8n_openvino_model/'
+        ```
 
 For more information, refer to the [export formats documentation](../modes/export.md).
 
@@ -333,22 +335,22 @@ After exporting a YOLOv8 model to OpenVINO format, you can run inference using P
 
     === "Python"
 
-    ```python
-    from ultralytics import YOLO
+        ```python
+        from ultralytics import YOLO
 
-    # Load the exported OpenVINO model
-    ov_model = YOLO("yolov8n_openvino_model/")
+        # Load the exported OpenVINO model
+        ov_model = YOLO("yolov8n_openvino_model/")
 
-    # Run inference
-    results = ov_model("https://ultralytics.com/images/bus.jpg")
-    ```
+        # Run inference
+        results = ov_model("https://ultralytics.com/images/bus.jpg")
+        ```
 
     === "CLI"
 
-    ```bash
-    # Run inference with the exported model
-    yolo predict model=yolov8n_openvino_model source='https://ultralytics.com/images/bus.jpg'
-    ```
+        ```bash
+        # Run inference with the exported model
+        yolo predict model=yolov8n_openvino_model source='https://ultralytics.com/images/bus.jpg'
+        ```
 
 Refer to our [predict mode documentation](../modes/predict.md) for more details.
 
@@ -370,21 +372,21 @@ Yes, you can benchmark YOLOv8 models in various formats including PyTorch, Torch
 
     === "Python"
 
-    ```python
-    from ultralytics import YOLO
+        ```python
+        from ultralytics import YOLO
 
-    # Load a YOLOv8n PyTorch model
-    model = YOLO("yolov8n.pt")
+        # Load a YOLOv8n PyTorch model
+        model = YOLO("yolov8n.pt")
 
-    # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all export formats
-    results = model.benchmarks(data="coco8.yaml")
-    ```
+        # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all export formats
+        results = model.benchmarks(data="coco8.yaml")
+        ```
 
     === "CLI"
 
-    ```bash
-    # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all export formats
-    yolo benchmark model=yolov8n.pt data=coco8.yaml
-    ```
+        ```bash
+        # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all export formats
+        yolo benchmark model=yolov8n.pt data=coco8.yaml
+        ```
 
 For detailed benchmark results, refer to our [benchmarks section](#openvino-yolov8-benchmarks) and [export formats](../modes/export.md) documentation.