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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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MatthewNoyce 2024-09-06 16:33:26 +01:00 • committed by GitHub
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10
docs/en/integrations/neural-magic.md
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@ -70,7 +70,7 @@ Deploying YOLOv8 with Neural Magic's DeepSparse involves a few straightforward s
To install the required packages, run:
!!! Tip "Installation"
!!! tip "Installation"
=== "CLI"
@ -83,7 +83,7 @@ To install the required packages, run:
DeepSparse Engine requires YOLOv8 models in ONNX format. Exporting your model to this format is essential for compatibility with DeepSparse. Use the following command to export YOLOv8 models:
!!! Tip "Model Export"
!!! tip "Model Export"
=== "CLI"
@ -98,7 +98,7 @@ This command will save the `yolov8n.onnx` model to your disk.
With your YOLOv8 model in ONNX format, you can deploy and run inferences using DeepSparse. This can be done easily with their intuitive Python API:
!!! Tip "Deploying and Running Inferences"
!!! tip "Deploying and Running Inferences"
=== "Python"
@ -120,7 +120,7 @@ With your YOLOv8 model in ONNX format, you can deploy and run inferences using D
It's important to check that your YOLOv8 model is performing optimally on DeepSparse. You can benchmark your model's performance to analyze throughput and latency:
!!! Tip "Benchmarking"
!!! tip "Benchmarking"
=== "CLI"
@ -133,7 +133,7 @@ It's important to check that your YOLOv8 model is performing optimally on DeepSp
DeepSparse provides additional features for practical integration of YOLOv8 in applications, such as image annotation and dataset evaluation.
!!! Tip "Additional Features"
!!! tip "Additional Features"
=== "CLI"