张宗平
cd6209119a
Full-scale retrain (stage1 31250 steps on 500k align, stage2 9375 steps on 100k sft) vs the prior small-scale run (5万/5k steps): metric small-scale full-scale stage2 EMA 0.81 0.75 model VUS-PR 0.14 0.22 (+57%) model Aff-F1 0.21 0.25 (vs pure_llm 0.003 — TS modality helps) model QA mean 2.45 2.41 QA vs pure_llm 2.8× 2.7× (stable lead, 6 categories) Confirms the hypothesis that weak anomaly localization was mostly a data-volume problem, not an algorithmic one: full-scale training lifted VUS-PR 57% and the model now clearly beats the pure-LLM baseline on affiliation-F1 (0.25 vs 0.003). VUS-PR still trails trivial baselines (0.22 vs random 0.33) — precise localization remains the open frontier, but the TS modality is demonstrably contributing (was unclear before).
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