张宗平
76fd636fae
- eval/instruct_check.py (T4.2): per-category (6 types) parse_rate + rule_acc + stub-judge mean + usability; pass bars anomaly-JSON>80%, overall usability>70%. - eval/report.py: wire eval_qa_task across all categories so the report now covers design §6.5 问答均分 (QA judge 0-5 per category per predictor), not just anomaly-detection metrics. Fix latent bug: describe rule judge expects a dict ref, so parse the JSON ref_answer before passing. - Verified trivial-only smoke renders both tables; all_report VUS-PR=0.26 (not inflated) vs PA-F1~1.0 confirms PA is control-only.
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