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/lib.rs

@@ -2,9 +2,10 @@
 //!
 //! A from-scratch decoder built entirely from the [`xtrain_autodiff`] op set:
 //! token embedding → `n_layers` × {pre-RMSNorm → multi-head causal attention
-//! (RoPE) → residual; pre-RMSNorm → SwiGLU MLP → residual} → final RMSNorm →
-//! LM-head matmul. The forward builds an autograd graph; calling `.backward()`
-//! on the cross-entropy loss fills every parameter's `.grad()`.
+//! (per-head QK-norm + RoPE) → residual; pre-RMSNorm → SwiGLU MLP → residual} →
+//! final RMSNorm → LM-head matmul. The forward builds an autograd graph; calling
+//! `.backward()` on the cross-entropy loss fills every parameter's `.grad()`.
+//! Per-head QK-norm (Qwen3-style) makes the architecture xserv-compatible (T9).
 //!
 //! Conventions (matching the engine, not HuggingFace):
 //! - Linear weights are `[in, out]` and applied as `x @ W` (no transpose), since