张宗平
204d5237ba
- src/tsmm/train/losses.py:
* lm_loss(logits, labels): shifted masked CE (answer span only); clean 0.0
when all positions masked.
* infonce_contrastive_loss(z1, z2): symmetric CLIP-style InfoNCE in TS-
embedding space; positive = original vs light-augmentation view.
- tests/test_losses.py: 9 tests. 100 tests passing.
Spec clarification (small tier): InfoNCE operates in TS-embedding space
(encoder/projector output) rather than answer-embedding space; light
augmentations are positives. T3.3 strong-perturbation sensitivity is a
separate downstream check.
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