ultralytics 8.0.122 Fix torch.Tensor inference (#3363)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: krzysztof.gonia <4281421+kgonia@users.noreply.github.com>

docs/models/index.md

@@ -20,21 +20,25 @@ In this documentation, we provide information on four major models:
 8. [YOLO-NAS](./yolo-nas.md): YOLO Neural Architecture Search (NAS) Models.
 9. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
 
-You can use these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python:
+You can use many of these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python:
 
 ## CLI Example
 
+Use the `model` argument to pass a model YAML such as `model=yolov8n.yaml` or a pretrained *.pt file such as `model=yolov8n.pt`
+
 ```bash
-yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
+yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml epochs=100
 ```
 
 ## Python Example
 
+PyTorch pretrained models as well as model YAML files can also be passed to the `YOLO()`, `SAM()`, `NAS()` and `RTDETR()` classes to create a model instance in python:
+
 ```python
 from ultralytics import YOLO
 
-model = YOLO("model.yaml")  # build a YOLOv8n model from scratch
-# YOLO("model.pt")  use pre-trained model if available
+model = YOLO("yolov8n.pt")  # load a pretrained YOLOv8n model
+
 model.info()  # display model information
 model.train(data="coco128.yaml", epochs=100)  # train the model
 ```