recipe-lab — 1B curriculum/architecture ablation checkpoints

Best-val checkpoints, loss curves and lm-eval readouts for every 1B-scale cell of the recipe-lab campaign: a hunt for a training recipe (data order, backbone, optimizer) that raises downstream quality for a small hybrid-MoE model.

These are research artifacts, not a product: 1.3B dense / 1.3B-total 0.41B-active MoE, ~15B tokens, GPT-2 BPE, no instruction tuning and no safety work. They exist so each claim in the campaign ledger can be re-checked against the weights that produced it.

Every cell is one variable away from its control; the reasoning, the pre-registered predictions and the retractions live in RESULTS_1B.md.

Cells (8-task lm-eval mean, 1000 samples/task)

Files per cell: <cell>_best.pt (weights), <cell>_curve.json (loss curve + full config), evals_<cell>.json (per-task accuracies).