张宗平
aecccfab1d
- eval/ts_effectiveness.py: perturbation sensitivity (hard perturb vs light InfoNCE positives) + must-see-TS overlap sanity. - Measured on stage1_final.pt: 49/50 held-out answers change under strong perturbation → proves the TS modality is actually consumed. - Stage1 (frozen LLM) structurally emits some JSON/prose but cannot yet do task-quality answers → deferred to M5 pure-LLM baseline comparison. - Mark T3.3/T3.4 (M3 exit) done.
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