model: tiny RoPE+RMSNorm+SwiGLU transformer + overfit test

New crate xtrain-model: a from-scratch decoder built entirely from the
autodiff op set.
- Config (tiny: dim=32, 2 layers, 2 heads, head_dim=16, ffn=64).
- TinyTransformer: embedding -> N x {pre-RMSNorm -> multi-head causal
  attention (RoPE, additive causal mask, per-head SDPA) -> residual;
  pre-RMSNorm -> SwiGLU MLP -> residual} -> final RMSNorm -> LM head.
  x@W weight convention (engine GEMM is plain A@B); dim=n_heads*head_dim.
- params()/zero_grad-able leaves for the optimizer; param_to_host export.
- overfit test: char-level bring-up (embedded text -> vocab -> shifted
  targets), minimal hand-written GD (p -= lr*grad) memorises one fixed
  batch -> loss ~0 + greedy argmax matches targets. End-to-end fwd+bwd
  correctness signal. Gated #![cfg(not(no_cuda))].

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

crates/xtrain-model/Cargo.toml

@@ -0,0 +1,12 @@
+[package]
+name = "xtrain-model"
+version.workspace = true
+edition.workspace = true
+
+[dependencies]
+xtrain-tensor = { path = "../xtrain-tensor" }
+xtrain-autodiff = { path = "../xtrain-autodiff" }
+
+[dev-dependencies]
+# Acceptance tests drive the GPU (device selection) directly.
+xtrain-cuda = { path = "../xtrain-cuda" }