feat(m2): multimodal splice [属性][时间戳][TS tok][事件][问题][回答] (T2.3)

- src/tsmm/model/multimodal.py: MultimodalSplicer — binds LLM input
  embedding layer, assembles text embeds (via tokenizer) + TS token embeds
  (from Projector) into inputs_embeds/attention_mask/labels; labels = -100
  everywhere except answer+EOS (training); left-truncation to max_len=1024.
- tests/test_multimodal.py: 8 tests against real Qwen2.5-0.5B tokenizer +
  stub embedding (shapes, all-ones mask, answer-only labels, TS token count
  in sequence, no-NaN, truncation). 74 tests passing.
- configs/llm.yaml: offline model path (downloaded via proxy to
  ~/models/Qwen2.5-0.5B-Instruct; HF hub unreachable on host).

Spec clarifications (small tier, comet-build Step 4):
- Training appends answer + EOS to the prompt layout.
- Build embedding layer with len(tokenizer) (incl. added special tokens),
  not tokenizer.vocab_size which excludes them (eos=151645).

configs/llm.yaml

@@ -0,0 +1,10 @@
+# LLM backbone config (T2.3 / T2.4)
+# Design ref: docs/superpowers/specs/2026-06-29-ts-as-modality-design.md §2.2
+# Qwen2.5-0.5B-Instruct: hidden=896, 24 layers, vocab=151936, BF16 ~1GB
+
+# Local offline path (downloaded via proxy; HF hub unreachable on this host)
+model_path: /home/zhangzp/models/Qwen2.5-0.5B-Instruct
+hf_model_id: Qwen/Qwen2.5-0.5B-Instruct   # for reference; offline use model_path
+
+hidden_size: 896
+torch_dtype: bfloat16