train: loop + checkpoint save/load + sampler + train binary

Training loop (train_loop.rs): sample batch_size sequences, forward loss +
backward (tape SUMs grads), clip_grad_norm with ×1/batch averaging, AdamW step
with scheduled lr, zero_grad; logs loss/lr/gnorm/tok-s and checkpoints
periodically; returns the loss trace.

Checkpoint (checkpoint.rs): flat little-endian dump of params() in order
(magic/version/count + per-param ndim/dims/f32 data); load_into validates and
overwrites a matching model's params via set_value (exact f32 round-trip).

Sampler (sample.rs): autoregressive greedy / temperature generation — re-runs
forward on the growing prefix (model is single-sequence, RoPE pos=row).

bin/train.rs: end-to-end entry — load tokenizer+corpus, train a tiny 4-layer
model for a bounded budget, checkpoint, print samples. no_cuda stub keeps it
buildable on a GPU-less host.

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

crates/xtrain-train/src/lib.rs

@@ -10,3 +10,13 @@
 pub mod clip;
 pub mod data;
 pub mod schedule;
+
+#[cfg(not(no_cuda))]
+pub mod checkpoint;
+#[cfg(not(no_cuda))]
+pub mod sample;
+#[cfg(not(no_cuda))]
+mod train_loop;
+
+#[cfg(not(no_cuda))]
+pub use train_loop::{TrainConfig, train};