Use attributes for labels in yolov8

I am new to this stuff and trying to train a model with yolov8. I have a dataset with 1k images labeled with cvat.ai where I have label named: Bear and that label has attribute male (checkbox false or true). If I export that dataset to yolo1.1 format then there is no attribute only label. If I export dataset to format coco1.0 then I see in json file a attribute. As far as I know I can only use yolo format right in yolov8?

I cannot find any resource which explains how to train a model with attributes in yolov8. I see only tutorials to train a model with multiple labels which I think in my case will lead to 2 detections at the same time.

Yes, you can only use the yolo label format in YOLOv8. There are differences in label format depending on the task: detection, segmentation, classification, and pose estimation. Check here: https://docs.ultralytics.com/datasets/.
These formats don't include attributes, only classes and coordinates. You can translate your 'male' attribute of the class 'Bear' to two classes for yolo: 'Bear-male' and 'Bear-female'.
You have all the needed information about classes, attributes, and coordinates in the exported json file of your dataset (coco json format, for instance). It will be necessary to take this data and compose new yolo labels with the format you need depending on the task.
In this case, having a sufficient amount of data for both, male and female classes, the yolo model will learn to differentiate them. The lack of data can lead to classification mistakes and multiple detections of the same object, so in this case, it can be better to use only one class 'Bear' without a gender.