张宗平
9b9aa35f13
- T4.2/T4.3 (M4): anomaly JSON parse 1.00, usability 0.92; stage2 ckpt 0.81 loss, 10.03GB. - T5.6 (M5): ablation change_rate 0.98 ✅, QA mean 2.45 vs pure_llm 0.88 ✅, reproducible w/ trivial ✅; VUS-PR below baseline (honest negative — anomaly localization is the weak point, documented with mitigation directions). - All M1-M5 tasks now closed; real-benchmark download deferred.
tsmm
Time-series as a Modality — an experimental, single-GPU multimodal model that treats multivariate time series as an independent modality, jointly injected into a small LLM (Qwen2.5-0.5B) for time-series question answering and reasoning (ChatTS route).
Status
M1 (data pipeline) under construction. See
docs/superpowers/specs/2026-06-29-ts-as-modality-design.md for the design and
docs/superpowers/plans/2026-06-29-ts-as-modality-plan.md for the task plan.
Layout
src/tsmm/ package (data, model, train, eval)
configs/ training / data configs
scripts/ offline generation & training entrypoints
tests/ pytest suite
data/ generated datasets (gitignored)
checkpoints/ model checkpoints (gitignored)
Install (dev)
python3 -m venv .venv
. .venv/bin/activate
pip install -e . # full deps (incl. torch/transformers/peft/...)
# M1-only lightweight install:
pip install numpy tqdm pyyaml pytest && pip install -e . --no-deps