[Feature Request] Metric checkpoints at Base/Small scale (DA3Metric-Base / DA3Metric-Small)

### Is your feature request related to a problem?

The Monocular Metric series currently ships only at Large scale (`DA3METRIC-LARGE`, 0.35B).
The any-view Main series already provides `DA3-BASE` (0.12B) and `DA3-SMALL` (0.08B), and
there's a `DA3MONO-LARGE` relative model — but all of the smaller checkpoints are relative-only.
There is no lightweight checkpoint that outputs real-world metric scale.

This is a blocker for real-time / on-device deployment. On edge runtimes — Unity Inference
Engine (Sentis), mobile, standalone XR headsets — the Small/Base models are the only ones that
run at interactive frame rates and are small enough to convert and quantize for the target,
but they're relative-only. The Large metric model is both too heavy for these targets and, in
practice, harder to get running in these runtimes. So any application needing absolute distance
on-device (AR/XR, robotics, assistive/accessibility tools) currently has to choose between
metric scale and a deployable footprint.

For context: Depth Anything V2 did release metric checkpoints at Small/Base/Large, so the move
to DA3 effectively removes the small-footprint metric option for edge users.

### Describe the solution you'd like

Release metric checkpoints at the smaller scales — `DA3Metric-Base` and `DA3Metric-Small` — by
fine-tuning the metric head onto the existing Base/Small backbones, mirroring `DA3METRIC-LARGE`.
Even a single additional Small metric checkpoint would be valuable, and the Apache-2.0 license on
the current metric model makes a small variant especially useful for production/edge use.

### Additional context

- The backbones already exist (`DA3-BASE`, `DA3-SMALL`), so this is primarily a metric fine-tune
at smaller scale rather than new architecture.
- Happy to benchmark candidate checkpoints on Unity Inference Engine / mobile XR and report back
on accuracy and runtime if that's useful.