张宗平
5e3030a20e
Addresses both points from review:
1) Resume (so a killed run converges across multiple restarts, like gen_supervisor):
- checkpoints now carry optimizer state + EMA value in a 'train_state' field
- --resume auto-picks the latest step ckpt and continues; --resume_from for explicit
- stage2 resume rebuilds the LoRA structure via enable_lora (same path as fresh)
then overlays saved adapter weights via set_peft_model_state_dict. Using
PeftModel.from_pretrained instead reset requires_grad and shrank the trainable
set 129->33 tensors, corrupting the optimizer state — verified and fixed.
- Smoke-verified: stage1 step4->10, stage2 step4->8, both restore optimizer +
EMA + LoRA with no state mismatch.
2) EMA loss logging (honest answer to 'is the loss swing a real problem?'):
- the old log printed the raw per-micro-batch loss every N steps; on
heterogeneous 6-task data that swings 2-3x purely from sampling (anomaly
JSON is low-loss, forecast numbers high-loss), so it looked 'unstable'
while the model may well have been converging.
- EMA(alpha=0.05) now logged alongside raw + as a tensorboard scalar
(total_loss_ema / sft_loss_ema). Read the EMA, not the raw, to judge
convergence. lr left unchanged (1e-4) pending EMA evidence.
Adds scripts/train_full_supervisor.sh: stage1->stage2 resume loop, same
crash-convergence guarantee as gen_full_supervisor.
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