data: full TinyStories + tokenized-id cache, val loss, CLI arch

- Corpus::load_cached: tokenize the (large) corpus ONCE, cache the id stream to
  <corpus>.u16.bin (gpt2 vocab 50257 < 65536 → exact u16), read cache on reruns.
- Corpus::split_tail: hold out a tail slice as a validation corpus.
- train(): take an optional valid corpus + eval_every/eval_batches; periodic
  deterministic val-loss eval that checkpoints the BEST val model; returns
  TrainResult{train_losses, evals, best_val}. T6 fixed-cadence path preserved.
- bin/train + bin/export_safetensors: read architecture (--heads/--head-dim/
  --layers/--ffn) + opt knobs (--steps/--batch/--seq/--max-lr/--val-tokens/
  --eval-every) from CLI flags; defaults reproduce the v0-baseline tiny config.
- gitignore the multi-GB corpus + *.u16.bin caches + *.ckpt (dash5-only).

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

crates/xtrain-train/tests/real_training.rs

@@ -96,10 +96,12 @@ fn trains_on_tinystories() {
         log_every: 50,
         ckpt_path: None,
         ckpt_every: 0,
+        eval_every: 0,
+        eval_batches: 0,
         seed: 42,
     };
 
-    let losses = train(&model, device, &corpus, &tcfg);
+    let losses = train(&model, device, &corpus, None, &tcfg).train_losses;
     // Average the first/last few steps to smooth per-step noise.
     let head: f32 =
         losses[..10.min(losses.len())].iter().sum::<f32>() / 10.0_f32.min(losses.len() as f32);