Model Card Checklist

Phase 3 · Document 06 · Model Cards and System Cards — Reference artifact (not a 15-section concept doc) Prev: 05 — Unsloth Model Page Guide · Next: 07 — Model Selection Memo Template

A copy-paste checklist for reading any model card / system card / provider page in 5–10 minutes. Pair it with the concept doc 00 — How to Read Model Cards. Copy the block below into your notes for each model you evaluate. Fields you can't fill are findings too — mark them unspecified and treat unknowns as risk.

How to use it

  1. Open the model card, the provider page, and (for open models) the Hugging Face page — they differ (00).
  2. Fill the block per model. Gates (license, data retention, limitations) can reject a model regardless of benchmarks.
  3. Verify capability claims with your own eval (Phase 1.07) before committing.
  4. Feed the result into a Model Selection Memo.

The checklist (copy this block)

# Model Card Checklist — <model name>   (date: YYYY-MM-DD)
Sources read:  [ ] model card  [ ] system card  [ ] provider page  [ ] HF page

## Identity
- [ ] Exact model ID (versioned, not an alias): ____
- [ ] Family / variant (size, stage, modality): ____
- [ ] Lab / creator: ____
- [ ] Provider(s) / host(s) (if different from lab): ____
- [ ] Release date / last updated: ____
- [ ] Is there a newer version?  Y / N

## Architecture & Training
- [ ] Dense or MoE? total / active params: ____
- [ ] Context window (max input): ____
- [ ] Max output tokens: ____
- [ ] Knowledge cutoff: ____
- [ ] Post-training method (SFT / RLHF / DPO / RL-reasoning): ____
- [ ] Tokenizer / vocab size (if relevant): ____

## Capability  (filter — verify by eval)
- [ ] Intended tasks: ____
- [ ] Tool / function calling?  Y / N — reliability verified? Y/N
- [ ] Structured output (JSON schema)?  Y / N
- [ ] Reasoning / extended thinking?  Y / N — effort/budget control: ____
- [ ] Multimodal INPUTS (text/image/audio/video/pdf): ____
- [ ] Multimodal OUTPUTS: ____
- [ ] Benchmarks reported — and what they actually measure: ____

## Cost  (provider page, NOT the model card)
- [ ] Input price /1M: ____
- [ ] Output price /1M: ____
- [ ] Cached input price /1M: ____
- [ ] Reasoning tokens billed? rate: ____
- [ ] Image/audio/video priced differently? ____

## Limitations  (GATE)
- [ ] Stated weaknesses / failure modes: ____
- [ ] Out-of-scope uses (vs my use): ____
- [ ] Language coverage limits: ____
- [ ] Long-context recall verified at my length?  Y / N

## Deployment
- [ ] API-only or weights available? ____
- [ ] License — commercial use OK?  Y / N : ____    (GATE)
- [ ] vLLM compatible (safetensors)?  Y / N
- [ ] llama.cpp / GGUF available?  Y / N
- [ ] Ollama available?  Y / N
- [ ] Fine-tuning supported?  Y / N
- [ ] Access channels (API / Bedrock / Vertex / others): ____

## Risk & Safety  (GATE)
- [ ] Safety / dangerous-capability evals performed? ____
- [ ] Red-teaming?  refusal / over-refusal posture: ____
- [ ] Jailbreak / injection findings → my guardrails: ____
- [ ] Data retention / training-on-inputs policy: ____    (GATE)
- [ ] Data residency / region options: ____
- [ ] OK for sensitive data?  Y / N
- [ ] Provider mitigations vs MY responsibilities: ____

## Verdict
- [ ] Capability fit:  pass / fail
- [ ] All GATES pass (license, data policy, limitations)?  Y / N
- [ ] Capabilities I must verify by eval: ____
- [ ] Shortlist decision:  keep / drop  — why: ____

Severity legend (which fields are gates)

MarkerMeaningExamples
GATEA fail here disqualifies the model regardless of qualityLicense (commercial), data retention/residency, out-of-scope/limitations vs your use
FilterRequired capability; absence drops the modelTool calling, structured output, modality, context window
VerifyClaimed — confirm with your own evalTool-call reliability, long-context recall, benchmark relevance
InfoUseful context, not decisive aloneRelease date, tokenizer, benchmark scores

Worked mini-example (illustrative)

# Model Card Checklist — Example-Instruct-8B   (date: 2026-06-13)
Identity: example-instruct-8b-2026-04 (versioned) · Lab: ExampleAI · Provider: ExampleAI API + OpenRouter
Architecture: dense, 8B · context 128K · max output 8K · cutoff 2025-12 · SFT+DPO
Capability: chat+tools (tool reliability VERIFY) · structured output Y · reasoning N · text-only
Cost: $0.20/$0.60 per 1M (provider page) · cached $0.05 · no reasoning tokens
Limitations: weak on multilingual; long-context recall to verify
Deployment: weights available (safetensors + GGUF) · license: Apache-2.0 (commercial OK ✔) · vLLM ✔ · GGUF ✔
Risk: standard safety evals · no-train on API inputs (confirm contract) · OK for non-sensitive data
Verdict: capability fit PASS · gates PASS · verify: tool reliability + 128K recall · KEEP (shortlist)

Cross-references

Next: 07 — Model Selection Memo Template