Save model snapshot to CPU to avoid OOM on multi-GPU setups

The snapshot() deepcopy was cloning tensors on their original GPU
devices, doubling VRAM usage. For a 234GB model sharded across 6
A100-80GB GPUs (~39GB each), this left no room for the copy.

Now snapshot stores tensors on CPU and restore() moves them back
to each parameter's current device.

obliteratus/models/loader.py

@@ -312,14 +312,27 @@ class ModelHandle:
         )
 
     def snapshot(self):
-        """Save a deep copy of the model state dict so we can restore after ablation."""
-        self._original_state = copy.deepcopy(self.model.state_dict())
+        """Save a copy of the model state dict so we can restore after ablation.
+
+        Tensors are moved to CPU to avoid doubling GPU memory usage on
+        multi-GPU (device_map) setups.
+        """
+        self._original_state = {k: v.cpu().clone() for k, v in self.model.state_dict().items()}
 
     def restore(self):
-        """Restore the model to the snapshot state."""
+        """Restore the model to the snapshot state.
+
+        Moves CPU-saved tensors back to each parameter's current device.
+        """
         if self._original_state is None:
             raise RuntimeError("No snapshot to restore — call .snapshot() first.")
-        self.model.load_state_dict(self._original_state)
+        # Map each key to the device where the model currently holds it
+        current_state = self.model.state_dict()
+        restored = {}
+        for k, v in self._original_state.items():
+            target = current_state[k].device if k in current_state else None
+            restored[k] = v.to(target) if target is not None else v
+        self.model.load_state_dict(restored)
 
     def cleanup(self):
         """Remove temporary offload directory if one was auto-created."""