model: add per-head QK-norm (Qwen3-compat) for xserv export

xserv's Qwen3 forward unconditionally applies per-head RMSNorm to Q and K
(q_norm/k_norm, shape [head_dim]) before RoPE — even gamma=1 is a real RMS
divide, not identity. xtrain never had this, so an exact xserv<->xtrain loop
was structurally impossible. Add it (reusing the 2D rms_norm op on the
[seq*nh, hd] head rows, inserted between reshape and rope to mirror
qwen3.rs's order) so the trained model is genuinely Qwen3-compatible.

params() inserts q_norm,k_norm after wv; num_params() counts them; the
PyTorch parity refs (parity.py / adamw_parity.py) + their name lists add the
same step so the dumps stay self-consistent.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

crates/xtrain-model/src/config.rs

@@ -42,6 +42,7 @@ impl Config {
     /// Total learnable parameter count (for logging / sanity).
     pub fn num_params(&self) -> usize {
         let per_layer = 2 * self.dim                       // 2 rmsnorm gammas
+            + 2 * self.head_dim                             // q/k per-head norm gammas
             + 3 * self.dim * self.dim                       // q/k/v proj
             + self.dim * self.dim                           // out proj
             + 2 * self.dim * self.ffn_hidden                // gate/up proj