张宗平
bf95ac18e4
- src/tsmm/train/stage1.py: streams align.jsonl, freeze_llm, trains Encoder+Projector (AdamW lr=1e-4), bs=8/grad_accum=4/ctx=512/BF16, gradient checkpointing + enable_input_require_grads, loss = lm + lambda*InfoNCE (original vs perturbed TS representation), ckpt + tensorboard every 2000 steps. - scripts/train_stage1.sh: wrapper with design defaults. - src/tsmm/data/collator.py: handle JSON null (missing markers) -> NaN -> fill. - .gitignore: /checkpoints/ and data/*.jsonl. - Smoke verified: --max_steps 5 on 64 samples, finite loss, peak 5.52 GB (≤6GB). - 100 tests passing.
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