MONA bfloat16: safe low-precision (gamma=1e-6 needs bf16 exponent range)

- Switch MONA from fp32 to bfloat16 (same exponent range as fp32, no underflow)
- fp16 causes NaN: gamma=1e-6 falls into subnormal range (min normal ~6.1e-5)
- bf16 min normal ~1.2e-38, so 1e-6 is safe
- RTX 4090 supports bf16 natively
- Document bf16 vs fp16 vs fp32 comparison in README
- Update model summary: 3.5M MONA (last 12 blocks), 5.6M total trainable

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/models/adapters.py

@@ -68,7 +68,11 @@ class MonaAdapter(nn.Module):
         self.gammax = nn.Parameter(torch.ones(dim))
 
     def forward(self, x: torch.Tensor, hw: tuple[int, int]) -> torch.Tensor:
-        """Apply MONA adapter in fp32 (gamma=1e-6 and small bottleneck underflow in fp16).
+        """Apply MONA adapter in bfloat16.
+
+        bfloat16 has the same exponent range as fp32 (min ~1.2e-38), so gamma=1e-6
+        is safe. fp16 would underflow (min normal ~6.1e-5). RTX 4090 supports bf16
+        natively with comparable throughput to fp16.
 
         Args:
             x: Token features [B, N, D] where N includes CLS + register + patch tokens.
@@ -78,10 +82,9 @@ class MonaAdapter(nn.Module):
             Adapted features [B, N, D] (same shape, residual connection).
         """
         orig_dtype = x.dtype
-        with torch.amp.autocast("cuda", enabled=False):
-            x = x.float()
+        with torch.amp.autocast("cuda", dtype=torch.bfloat16):
             identity = x
-            # Scaled LayerNorm.
+            # Scaled LayerNorm (gamma=1e-6 safe in bf16).
             x = self.norm(x) * self.gamma + x * self.gammax
 
             x = self.down(x)  # [B, N, bottleneck]