张宗平
17ed7947d3
Full-scale eval crashed in the pure-LLM QA-judge path: pure_llm emits
free-form JSON whose 'segments' list can contain entries like
["CPU Usage", 30] (channel name instead of an int). _seg_from_entry called
int() unconditionally and raised ValueError, killing the whole report run
(model predictor had already finished; result lost).
Fix: wrap _seg_from_entry in try/except (ValueError, TypeError) → return
None for any unparseable entry, so good segments alongside junk are still
extracted. Verified: {["CPU Usage",30],[10,20]} → [(10,20)]. + unit test.
Also archive logs/train_full_run.log (stage1 31250 EMA1.26, stage2 9375 EMA0.75).
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