Update https://docs.ultralytics.com/models (#6513)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/en/guides/hyperparameter-tuning.md

@@ -69,7 +69,7 @@ The process is repeated until either the set number of iterations is reached or
 
 Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyperparameter tuning of YOLOv8n on COCO8 for 30 epochs with an AdamW optimizer and skipping plotting, checkpointing and validation other than on final epoch for faster Tuning.
 
-!!! Example ""
+!!! Example
 
     === "Python"
 

docs/en/guides/kfold-cross-validation.md

@@ -30,7 +30,7 @@ Without further ado, let's dive in!
         - It includes 6 class labels, each with its total instance counts listed below.
 
           | Class Label | Instance Count |
-          |:------------|:--------------:|
+                              |:------------|:--------------:|
           | Apple       |      7049      |
           | Grapes      |      7202      |
           | Pineapple   |      1613      |

docs/en/guides/sahi-tiled-inference.md

@@ -167,7 +167,7 @@ That's it! Now you're equipped to use YOLOv8 with SAHI for both standard and sli
 
 If you use SAHI in your research or development work, please cite the original SAHI paper and acknowledge the authors:
 
-!!! Note ""
+!!! Quote ""
 
     === "BibTeX"