feat(m3): stage 1 alignment training script + smoke (T3.2)

- src/tsmm/train/stage1.py: streams align.jsonl, freeze_llm, trains
  Encoder+Projector (AdamW lr=1e-4), bs=8/grad_accum=4/ctx=512/BF16,
  gradient checkpointing + enable_input_require_grads, loss = lm + lambda*InfoNCE
  (original vs perturbed TS representation), ckpt + tensorboard every 2000 steps.
- scripts/train_stage1.sh: wrapper with design defaults.
- src/tsmm/data/collator.py: handle JSON null (missing markers) -> NaN -> fill.
- .gitignore: /checkpoints/ and data/*.jsonl.
- Smoke verified: --max_steps 5 on 64 samples, finite loss, peak 5.52 GB (≤6GB).
- 100 tests passing.

.gitignore

@@ -126,3 +126,8 @@ coverage/
 .context-*/
 tmp/
 .temp/
+
+# local artifacts (design §6.1)
+/checkpoints/
+/data/*.jsonl
+