Add dinov2-mlx reference entry

dinov2/README.md

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+# DINOv2
+
+[dinov2-mlx](https://github.com/bahaehmimdi/dinov2-mlx) runs
+[DINOv2](https://github.com/facebookresearch/dinov2) on Apple Silicon,
+verified against real, unmodified PyTorch at every stage (preprocessing,
+model forward, both fp32 and fp16).
+
+**Note on approach**: unlike the other examples in this repo, this does
+not reimplement DINOv2 natively in `mlx.nn`. It runs the real,
+unmodified `transformers.AutoModel` (`Dinov2Model`) through
+[torch-mlx](https://github.com/bahaehmimdi/torch-mlx) — a from-scratch
+`torch`-API-compatible layer backed by `mlx.core` — so the model
+definition itself is real `transformers` code, not a hand-written port.
+Given that's a different shape of contribution than this repo's usual
+convention (native model + `torch` used only for offline weight
+conversion), this entry is a link to the full implementation and
+results rather than inline code — happy to restructure this however
+maintainers think fits best, or to close this if it's not a good fit
+for the example gallery.
+
+## Results
+
+Verified against real PyTorch: `pooler_output` matches to ~0.32%
+relative error. Beats real PyTorch's own MPS backend by ~1.12-1.15x
+across tested image sizes (both backends timed on the identical full
+pipeline: image → resize → crop → forward). Full write-up, including
+what didn't work, in the linked repo's `BENCHMARK_RESULTS.md`.
+
+```python
+from dinov2_mlx import DINOv2MLX
+from PIL import Image
+
+model = DINOv2MLX()
+out = model.extract_features(Image.open("photo.png"))
+out["pooler_output"]        # (1, 1024) numpy array
+out["last_hidden_state"]    # (1, 257, 1024) numpy array
+```
+
+See [the repo](https://github.com/bahaehmimdi/dinov2-mlx) for install
+instructions, the fp16/`mx.compile` options, and full benchmark
+methodology.