day0-release

Day-0 Release

Drive a model from a pretrained checkpoint to a publish decision for a quantized checkpoint, in a fixed sequence with a gate after every stage. This skill is a conductor: it sequences the existing domain skills and enforces the gates — it does not re-implement quantization, serving, evaluation, or comparison.

Goal (the default day-0 criterion): a quantized checkpoint smaller than the source, with accuracy drop within the threshold (default <1%) on the standard benchmark set versus the matching baseline, plus a publish recommendation.

When to use

Use only for the full goal-driven release. For a single stage, route to the domain skill directly: quantize → ptq, serve → deployment, evaluate → evaluation, compare two existing runs → compare-results.

Inputs

Resolve these before starting (ask the user for anything missing):

• Model — HF handle or checkpoint path.
• Recipe / qformat — e.g. nvfp4, fp8, or a recipe path. One candidate for v1.
• Cluster / launcher — from clusters.yaml (see the common skill's environment-setup.md).
• Eval set — defaults to the evaluation skill's AA suite (recipes/tasks/aa/).
• Threshold — max accuracy drop; default 0.01 (1%).

The chain

setup ─▶ PTQ ─▶ baseline-eval ─▶ quantized-eval ─▶ compare ─▶ closeout
          │          │                │               │
       gate_ptq   gate_run         gate_run       gate_compare

The evaluation skill deploys the model it evaluates (it stands up its own endpoint per run), so there is no separate deploy stage — a serving failure surfaces through the eval stage's gate (DEPLOYMENT_HEALTH_FAILED) and triages to the deployment skill to debug serving in isolation (see Step 4).

Run each stage by invoking the domain skill, then run its gate before proceeding. Do not advance past a failed gate. Copy this checklist and track progress:

- [ ] Step 0: Resolve inputs; confirm threshold and eval set
- [ ] Step 1: Setup gate — creds present, cluster reachable
- [ ] Step 2: PTQ (ptq skill) → gate_ptq.py
- [ ] Step 3: Baseline eval (evaluation skill, deploys source) → gate_run.py
- [ ] Step 4: Quantized eval (evaluation skill, deploys candidate) → gate_run.py
- [ ] Step 5: Compare (compare-results skill) → external sanity → gate_compare.py → decision
- [ ] Step 6: Closeout — report + publish recommendation

Step 1 — Setup gate

Use the common skill's credentials.md and remote-execution.md to confirm credentials and cluster reachability. If either fails, stop with SYSTEMIC — do not start PTQ.

Step 2 — PTQ

Invoke the ptq skill to produce the quantized checkpoint. Then gate:

# The ptq skill's post-PTQ validation produces a validation-summary JSON (size
# ratio + layer-precision counts + metadata diffs; see the ptq skill's
# references/checkpoint-validation.md). v1 gates on that summary:
python "$SKILL_DIR/scripts/gate_ptq.py" --summary <validation-summary.json>
#   add `--recipe <qformat>` to override the recipe recorded in the summary

gate_ptq.py returns JSON {pass, failure_class, detail}. On pass: false, branch on failure_class (see Triage below). Do not evaluate an unvalidated checkpoint.

Step 3 — Baseline eval

The baseline is the source (pre-quantization) model on the same task set and sampling params. Always run a fresh baseline via the evaluation skill, which deploys the source model itself. Gate with gate_run.py.

Step 4 — Quantized eval

Invoke the evaluation skill on the quantized checkpoint, matching the baseline's task set and sampling params. The evaluation skill stands up the serving endpoint itself (it builds the deployment.command, e.g. a vllm serve …), so a serving failure surfaces here as a failed gate_run.py with DEPLOYMENT_HEALTH_FAILED. When that happens, drop to the deployment skill to reproduce and debug serving in isolation (serve the checkpoint standalone, confirm /health + one generation, iterate on flags / TP / image / env vars) rather than burning full eval cycles on a broken endpoint — then carry the working command back into NEL's deployment.command and resume the eval. If the checkpoint genuinely can't serve, POINT_INFEASIBLE. Gate:

python "$SKILL_DIR/scripts/gate_run.py" --run <run-summary.json>

A pass: false here means the run is incomplete or invalid (judge/parse error, dropped samples) — do not compare scores from it.

Step 5 — Compare

Invoke the compare-results skill. It must perform the shared external baseline sanity check before the candidate-delta gate. A failed check is ANOMALOUS with failure class EXTERNAL_BASELINE_MISMATCH: investigate and rerun the baseline. If no credible comparable external score exists, record the baseline as externally unverified and continue using the validated measured baseline.

After recording the external status, produce per-task deltas and run:

python "$SKILL_DIR/scripts/gate_compare.py" \
    --baseline <baseline_scores.json> --candidate <candidate_scores.json> \
    --threshold 0.01

The threshold is a fraction of each task's score scale. Most AA tasks report 0-100, but some (e.g. tau2_bench_telecom Result) report 0-1; the gate infers each task's scale (0-1 if both scores are within [0, 1], else 0-100) and normalizes the drop accordingly, so --threshold 0.01 means "≤1 pt on a 0-100 task / ≤0.01 on a 0-1 task" uniformly. Pass --scales '{"task": max}' to override inference if a task's scores happen to fall in an ambiguous range.

gate_compare.py checks only the candidate delta; it cannot override a failed external baseline check. Combined decision:

• ACCEPT — no external check failed and every task is within the candidate threshold → go to Step 6. A missing comparable external score is not a failure; report it as externally unverified.
• REGRESSION — one or more tasks exceed threshold. v1 stops here and reports which tasks regressed by how much. (Picking the next recipe and re-running is deferred — see Scope.)
• ANOMALOUS — external baseline sanity failed, or scores are otherwise implausible (e.g. baseline lower than candidate by a large margin, or a task score is outside its valid range) → correct the baseline or surface it.

Step 6 — Closeout

Report the decision with: source vs output size + ratio, per-task baseline / candidate / delta / within-threshold, external source and sanity status, MLflow run IDs, and a publish recommendation (publish / do-not-publish). Archive artifacts to the workspace.

Triage (gate failure → decision)

Map a gate's failure_class to the next action:

SYSTEMIC (cluster down, dataset unavailable) aborts the whole run. POINT_INFEASIBLE means this (model, recipe) can't work as configured.

Output

Return a decision, not a raw artifact:

• ACCEPT + report + publish recommendation
• REGRESSION + which tasks failed the threshold and by how much
• ANOMALOUS / INFEASIBLE + reason and next automated action
• Always: workspace path + MLflow run IDs for traceability

Scope (v1)

In v1: the linear chain + gates + report. On REGRESSION, v1 reports and stops. Deferred to a follow-up: the evaluator-optimizer recipe loop (compare → pick the next recipe → re-run PTQ), which needs the bigpareto integration and a shared config/result schema.