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Update to lowercase MkDocs admonitions (#15990)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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10
docs/en/integrations/coreml.md
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@ -60,7 +60,7 @@ Exporting YOLOv8 to CoreML enables optimized, on-device machine learning perform
To install the required package, run:
!!! Tip "Installation"
!!! tip "Installation"
=== "CLI"
@ -75,7 +75,7 @@ For detailed instructions and best practices related to the installation process
Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
!!! Example "Usage"
!!! example "Usage"
=== "Python"
@ -131,7 +131,7 @@ Also, if you'd like to know more about other Ultralytics YOLOv8 integrations, vi
To export your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models to CoreML format, you'll first need to ensure you have the `ultralytics` package installed. You can install it using:
!!! Example "Installation"
!!! example "Installation"
=== "CLI"
@ -141,7 +141,7 @@ To export your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics)
Next, you can export the model using the following Python or CLI commands:
!!! Example "Usage"
!!! example "Usage"
=== "Python"
@ -198,7 +198,7 @@ For more information on performance optimization, visit the [CoreML official doc
Yes, you can run inference directly using the exported CoreML model. Below are the commands for Python and CLI:
!!! Example "Running Inference"
!!! example "Running Inference"
=== "Python"