[FEAT] Add DINOV3 pre-training

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Hello! I am playing with the library and while is pretty great I realized there is no Dinov3 pre-training added from scratch. The reason for this feature is while we can use distillation if the dataset is very different to the pre-trained dataset basically the distillation fails.

Example of how distillation of DINOV3 (using the pre-trained checkpoint) worked in lightly for my ultrasound dataset (the model collapses to only see brighness instead of global features):

Using the pre-training recipe for Dinov2 in lightly documentation gave me better results, but as reported in DINOV3 paper once we start training for many steps dense representation fails:

So adding gram anchoring and RoPE (DINOv3) would be amazing for overcoming this. Is this something planned in your milestones? Or maybe distillation is enough for your user base. Could you guide me on how could I pretrain dinov3 with your library? Thanks!

Start by reviewing the DINOv2 pre-training recipe in the lightly documentation and the DINOv3 paper requirements mentioned in the issue. The scope is to determine how DINOv3 pre-training, including gram anchoring and RoPE, should fit the library; done means a defined and supported pre-training path for the reported use case.

Written by the indexing model from the issue text.