53-Model Card Template v1.0 | Chapter 13 — Appendices (Forms / Checklists / Templates)

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Chapter 13 — Appendices (Forms / Checklists / Templates)

I. Purpose & Scope

• Consolidate forms, manifests, and templates required to execute this volume: model manifest, compatibility matrix, training/inference configs, UQ & error budget, evaluation & scoring, monitoring & rollback, release & signatures.
• For any template involving path quantities (arrival/phase), explicitly show gamma(ell) and d ell in text; record delta_form ∈ {general, factored} on the data side; parenthesize all expressions; publication requires p_dim = 1.0 with check_dim_report.json.

II. Recommended Layout (PTN_EXPORT/)

PTN_EXPORT/

model/

model_manifest.yaml

compat_matrix.yaml

control_spec.yaml

train_config.yaml

hpo_space.yaml

inference/

inference_openapi.yaml

inference.proto

binding_spec.md

uq/

model_uq.yaml

uq_summary.json

budget_breakdown.csv

eval/

bench_plan.yaml

scorecard.json

eval_report.md

monitoring/

monitoring_rules.yaml

rollback_fsm.yaml

alerts.jsonl

reports/

check_dim_report.json

validate_report.json

audit.jsonl

figs/

model_arch.svg

latency_hist.pdf

throughput_series.svg

path_profile.pdf

SIGNATURE.asc

report_manifest.yaml

III. Model Manifest & Compatibility

A. model_manifest.yaml

version: "1.0.0"

model:

id: "mdl-core"

semver: "1.2.0"

task: ["regression","path-estimation"]

io:

inputs: "Dataset Card v1.0:Ch.4"

outputs: ["obs.T_arr (s)","obs.Phi (rad)","score.uncertainty"]

deps:

dataset_card: "Dataset Card v1.0:Ch.3/4/6/7/8/11/12"

parameter_card: "Parameter Card v1.0:Ch.4/6/8/9"

error_budget: "Error Budget Card v1.0:Ch.5/6/8/9"

pipeline_card: "Pipeline Card v1.0:Ch.11/12"

coverage: { mode: "k", k: 2 }

sign: "SIGNATURE.asc"

checksums:

weights: "sha256:..."

code: "sha256:..."

B. compat_matrix.yaml

runtime:

cuda: ">=12.2,<13.0"

driver: ">=535"

framework: { torch: "2.2.x", triton: "2.2.x" }

data:

schema: ">=1.2,<2.0"

splits: ">=1.1,<2.0"

pipeline_api: ">=1.0,<2.0"

notes:

- "Path block required: gamma/d ell/delta_form"

IV. Architecture & Control

A. control_spec.yaml

version: "1.0.0"

preprocess:

normalize: { mean: "μ", std: "σ" }

path_align: { require: true, delta_form: "general" }

core:

f_theta: { type: "hybrid", ops: ["conv","attn","mlp"] }

loss:

total: "L = E[ℓ] + λ R"

tasks: ["task_main","task_aux"]

path_forms:

T_arr: "( ∫ ( n_eff / c_ref ) d ell )"

Phi: "( (2π / lambda_ref) * ∫ n_eff d ell )"

postprocess:

scale: { a: "learned", b: "learned" }

B. model_arch.svg — nodes/edges/tensor shapes & units (file artifact, not expanded here).

V. Training & HPO

A. train_config.yaml

version: "1.0.0"

seed: 20250924

batch: { size: 256, accum: 2, drop_last: true }

optimizer: { name: "adamw", lr: 3.0e-4, betas: [0.9,0.999], weight_decay: 0.01 }

lr_scheduler: { name: "cosine", warmup_steps: 1000, min_lr: 1.0e-6 }

amp: { enabled: true, dtype: "fp16" }

grad_clip: { type: "norm", max: 1.0 }

ddp: { world_size: 8, backend: "nccl", sync_bn: true }

eval: { every_steps: 1000, metrics: ["MAE","AUC","r_phi","Q_res"], coverage: { mode: "k", k: 2 } }

checkpoint: { best: "min:val/MAE", every_steps: 5000, keep_last: 5 }

B. hpo_space.yaml

lr: { type: "loguniform", low: 3.0e-5, high: 3.0e-3 }

wd: { type: "loguniform", low: 1.0e-6, high: 1.0e-2 }

batch: { type: "choice", values: [128,256,512] }

warmup: { type: "choice", values: [500,1000,2000] }

VI. Deployment APIs & Bindings

A. inference_openapi.yaml

openapi: 3.0.3

info: { title: "Model Inference API", version: "1.0.0" }

paths:

/models/{id}/infer:

post:

summary: "Idempotent inference"

requestBody:

content: { application/json: { schema: { $ref: "#/components/schemas/InferRequest" } } }

responses:

"200": { description: "OK" }

B. inference.proto (excerpt)

message Ctx { string idempotency_key = 1; string coverage = 2; bool return_uq = 3; }

message InferRequest { Ctx ctx = 1; bytes inputs = 2; }

message InferResponse { string status = 1; bytes payload = 2; bytes uq = 3; string version = 4; string checksum = 5; }

C. binding_spec.md (outline)

# Binding Spec

- I/O fields and units/dimensions

- Coverage mode and UQ payload

- Error classes and return envelope

VII. UQ & Error Budget

A. model_uq.yaml

version: "1.0.0"

targets: ["T_arr","Phi","epsilon_flux","r_phi","Q_res"]

methods:

T_arr: { type: "delta", jacobian: "auto", cov_group: "medium" }

Phi: { type: "mc", draws: 10000, coverage: { quantile: [0.025,0.975] } }

coverage: { mode: "k", k: 2 }

covariance:

medium: { kernel: "exp", params: { sigma2: 9.0e-6, L_c_m: 25.0 } }

reports: ["uq_summary.json","cov_blocks.json"]

B. uq_summary.json

{ "T_arr":{"point":1.23e-8,"U_k2":1.5e-9}, "Phi":{"median":0.035,"q025":0.028,"q975":0.043} }

C. budget_breakdown.csv (headers)

source,variance_share,u_or_sigma,notes

VIII. Evaluation & Scoring

A. bench_plan.yaml

version: "1.0.0"

tasks:

- id: "bench-arrival"

split: "test"

metrics: ["DeltaT_arr_s","Q_res","p_dim"]

coverage: { mode: "k", k: 2 }

baseline: { id: "base-001", version: "1.2.3" }

weights: { DeltaT_arr_s: 0.35, r_phi: 0.25, epsilon_flux: 0.15, p_dim: 0.15, Q_res: 0.10 }

B. scorecard.json (example)

{

"version":"1.0.0",

"baseline":{"id":"base-001","Q":0.62},

"method":{"id":"mdl-core","Q":0.78},

"weights":{"DeltaT_arr_s":0.35,"r_phi":0.25,"epsilon_flux":0.15,"p_dim":0.15,"Q_res":0.10}

}

IX. Monitoring & Rollback

A. monitoring_rules.yaml

version: "1.0.0"

kpis:

latency_p95_s: { target: 0.200, alert: 0.250, critical: 0.300 }

throughput_rps: { target_min: 1000 }

q_res: { target_max: 0.20 }

p_dim: { require: 1.0 }

drift:

data: { test: "ks", p_crit: 0.01 }

actions:

on_alert: ["degrade"]

on_critical: ["rollback"]

B. rollback_fsm.yaml

version: "1.0.0"

states: [normal, degrade, rollback, recover]

transitions:

- { from: normal, to: degrade, when: "gate_alert or drift_alert" }

- { from: degrade, to: rollback, when: "gate_critical or perf_critical" }

- { from: rollback,to: recover, when: "stable_prev_version_ready" }

- { from: recover, to: normal, when: "validate_pass and perf_ok" }

C. alerts.jsonl (sample line)

{
  "ts": "2025-09-24T16:10:00Z",
  "level": "critical",
  "event": "gate_fail",
  "gate": "G4",
  "detail": "p_dim < 1.0",
  "action": "rollback"
}

X. Release Manifest

A. report_manifest.yaml

version: "1.0.0"

bundle:

figs:

- "figs/model_arch.svg"

- "figs/path_profile.pdf"

reports:

- "reports/check_dim_report.json"

- "reports/validate_report.json"

- "reports/audit.jsonl"

configs:

- "model/model_manifest.yaml"

- "model/compat_matrix.yaml"

sign: "SIGNATURE.asc"

see:

- "EFT.WP.Core.Metrology v1.0:check_dim"

- "EFT.WP.Core.Equations v1.1:S20-1"

XI. Normative Path Forms

• Arrival (equivalent):
  T_arr = ( 1 / c_ref ) * ( ∫ n_eff d ell )
  T_arr = ( ∫ ( n_eff / c_ref ) d ell )
• Phase accumulation:
  Phi = ( 2π / λ_ref ) * ( ∫ n_eff d ell )

Text explicitly shows path & measure; data side records delta_form; arrays meet len(gamma_ell)=len(d_ell)=len(n_eff)≥2; require p_dim = 1.0.

XII. Checklist

• Templates stored per this volume and cross-volume conventions; see[]/references[]/version compliant, anchor coverage ≥ 90%.
• For path-related templates, explicit gamma/measure/delta_form; parenthesized arrival/phase forms; I70-dim_check passed, p_dim = 1.0.
• report_manifest.yaml / check_dim_report.json / validate_report.json / audit.jsonl / SIGNATURE.asc present.
• Coverage/covariance modes consistent across Training/Inference/UQ/Eval/Monitoring/Rollback; all artifacts carry checksum.
• Non-compliant outputs tagged [Restricted] and limited to qualitative release; path plots annotate Δell and delta_form.