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@@ -53,3 +53,15 @@ git add results && git commit -m "ladder results" && git push Analysis (anywhere): `python scripts/plot_ladder.py --results results/h200node1` -> per-run table, loss curves, and the gap-vs-scale figure (the paper's part-2 headline). If a specific checkpoint is needed for the estimator audits, scp just that `runs/<name>/ckpt.pt`. + +## HF upload & security (shared nodes) +Results (and optionally checkpoints) can go to a **private** HF repo: `HF_UPLOAD=1 [HF_CKPT=1] ./scripts/run_ladder.sh` +or manually `python scripts/upload_hf.py --results results/<tag> [--with-ckpt runs]` (default repo +`<whoami>/zbp-scaling-runs`, created private if missing). + +Uploads authenticate ONLY via the `HF_TOKEN` environment variable or a standard `hf auth login`; tokens are +never CLI arguments (argv is world-readable via /proc on shared machines), never written by our scripts, and +`.gitignore` excludes token-like files. On a shared node, mint a **fine-grained HF token scoped to the single +private repo** (write permission only), `export HF_TOKEN=...` per session, and revoke it after the campaign. +Zero-token alternative: push only the small JSONL results to GitHub (a repo-scoped deploy key suffices) and +upload checkpoints from a trusted machine. |