Update more/missing type qualifiers to lowercase MkDocs admonitions (#16215)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/modes/benchmark.md

@@ -43,7 +43,7 @@ Once your model is trained and validated, the next logical step is to evaluate i
 - **OpenVINO:** For Intel hardware optimization
 - **CoreML, TensorFlow SavedModel, and More:** For diverse deployment needs.
 
-!!! tip "Tip"
+!!! tip
 
     * Export to ONNX or OpenVINO for up to 3x CPU speedup.
     * Export to TensorRT for up to 5x GPU speedup.

docs/en/modes/export.md

@@ -39,7 +39,7 @@ Here are some of the standout functionalities:
 - **Optimized Inference:** Exported models are optimized for quicker inference times.
 - **Tutorial Videos:** In-depth guides and tutorials for a smooth exporting experience.
 
-!!! tip "Tip"
+!!! tip
 
     * Export to [ONNX](../integrations/onnx.md) or [OpenVINO](../integrations/openvino.md) for up to 3x CPU speedup.
     * Export to [TensorRT](../integrations/tensorrt.md) for up to 5x GPU speedup.

docs/en/modes/predict.md

@@ -100,7 +100,7 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
 
 YOLOv8 can process different types of input sources for inference, as shown in the table below. The sources include static images, video streams, and various data formats. The table also indicates whether each source can be used in streaming mode with the argument `stream=True` ✅. Streaming mode is beneficial for processing videos or live streams as it creates a generator of results instead of loading all frames into memory.
 
-!!! tip "Tip"
+!!! tip
 
     Use `stream=True` for processing long videos or large datasets to efficiently manage memory. When `stream=False`, the results for all frames or data points are stored in memory, which can quickly add up and cause out-of-memory errors for large inputs. In contrast, `stream=True` utilizes a generator, which only keeps the results of the current frame or data point in memory, significantly reducing memory consumption and preventing out-of-memory issues.
 

docs/en/modes/train.md

@@ -41,7 +41,7 @@ The following are some notable features of YOLOv8's Train mode:
 - **Hyperparameter Configuration:** The option to modify hyperparameters through YAML configuration files or CLI arguments.
 - **Visualization and Monitoring:** Real-time tracking of training metrics and visualization of the learning process for better insights.
 
-!!! tip "Tip"
+!!! tip
 
     * YOLOv8 datasets like COCO, VOC, ImageNet and many others automatically download on first use, i.e. `yolo train data=coco.yaml`
 

docs/en/modes/val.md

@@ -41,7 +41,7 @@ These are the notable functionalities offered by YOLOv8's Val mode:
 - **CLI and Python API:** Choose from command-line interface or Python API based on your preference for validation.
 - **Data Compatibility:** Works seamlessly with datasets used during the training phase as well as custom datasets.
 
-!!! tip "Tip"
+!!! tip
 
     * YOLOv8 models automatically remember their training settings, so you can validate a model at the same image size and on the original dataset easily with just `yolo val model=yolov8n.pt` or `model('yolov8n.pt').val()`