Ray tuning checkpoints for training the YOLOv8 network

Hi, I’ve been trying to train my own fine-tuned network with the YOLOv8 architecture on a custom dataset. I trained a model with good results, but I want to optimize the network, and Rey Tune seems to be pretty popular. I found some short documentation for applying Ray Tune to YOLO models, but what I haven’t yet found is how to make checkpoints for Ray Tune tuning. The docs: Ray Tune. The documentation comes with a code template:
from ultralytics import YOLO

Load a YOLOv8n model

model = YOLO(‘yolov8n.pt’)

Start tuning hyperparameters for YOLOv8n training on the COCO8 dataset

result_grid = model.tune(data=‘coco8.yaml’, use_ray=True)`

I’ve still not been able to find any approach on how to start saving checkpoints because the only thing that saves are the models themselves, but I’ve not been able to retrieve the data to continue tuning which is the main problem.