fix(gen-seat): hash bf16 tensors via uint8 reinterpret, not numpy

numpy has no bfloat16, so .numpy().tobytes() raised
'TypeError: Got unsupported ScalarType BFloat16' on real checkpoints.
Flatten then view(torch.uint8) before hashing.

Result on the heresy candidate: VERDICT IDENTICAL -- all 15 mtp.* tensors
byte-identical to the incumbent's verbatim base graft, the head already
measured at 47.7% acceptance in production. The ~56 GB bf16 acceptance gate
is redundant, so no second seat comes down.

services/gen-seat-mixed-quant/compare_mtp_head.py

@@ -74,8 +74,16 @@ def load_mtp(model_dir: Path) -> dict:
 
 def digest(tensor) -> str:
     """SHA-256 over the raw tensor bytes. Dtype-sensitive by design — a head
-    stored at a different precision is not the same head for our purposes."""
-    return hashlib.sha256(tensor.contiguous().view(-1).numpy().tobytes()).hexdigest()
+    stored at a different precision is not the same head for our purposes.
+
+    Goes through a uint8 reinterpret rather than `.numpy()`: numpy has no
+    bfloat16, and these checkpoints are bf16, so the direct route raises
+    `TypeError: Got unsupported ScalarType BFloat16`.
+    """
+    import torch
+
+    flat = tensor.contiguous().flatten()
+    return hashlib.sha256(flat.view(torch.uint8).numpy().tobytes()).hexdigest()
 
 
 def main() -> int: