Describe
Enter six core model details or start with a Generic template. Keep the first draft moving.
Open-weight release documentation
Ship models with better safety documentation.
Fill in a few model details and generate a release-ready Model Card, safety checklist, and evidence-gap report—without uploading model weights.
No signup required · Markdown + JSON export · Saved only in this browser
acme/open-7bNeeds evidence
- Model identity documented
- Intended use documented
- Evaluation evidence incomplete
- Safety limitations missing
A smaller release workflow
Describe. Review. Export.
The first version focuses on the work model authors need before they publish—not a giant governance platform.
Review
See which claims are documented, which need evidence, and which release details are still missing.
Export
Copy a readable Markdown Model Card or download the structured OpenWeight Safety JSON.
Checklist coverage
Make the important gaps visible.
A blank field is not proof that a model is unsafe. It is a signal that the release documentation needs attention.
Model identity and versionArchitecture, parameter count, repository and release context.
Intended and out-of-scope useWho the model is for, and where human review is expected.
Training data disclosureSources, licensing, known gaps and sensitive-data considerations.
Evaluation evidenceBenchmarks, jailbreak, harmful-content, bias and privacy notes.
Limitations and mitigationsKnown failure modes, residual risk and release restrictions.
Next recommended testsA practical bridge from documentation gaps to red-team tools.
Open weight model safety
Make the evidence behind a release inspectable.
Open weight model safety starts with information that reviewers and downstream users can inspect. Publishing weights changes who can run, adapt, and redistribute a model, but it does not explain the training data, intended use, evaluation methods, known failure modes, license constraints, or mitigations. A responsible release needs those claims and the evidence behind them in one place.
OpenWeight Safety turns that documentation work into a practical browser-based workflow. It records model identity, intended and out-of-scope uses, training-data disclosure, limitations, and evaluation evidence. Depending on the release, that evidence may include performance benchmarks, jailbreak testing, harmful-content evaluation, bias analysis, privacy checks, and links to reproducible results.
The generated Model Card and checklist make missing fields visible before publication and give reviewers a consistent starting point. They do not prove that a model is safe, predict every downstream use, or replace security testing and legal review. The goal is narrower and more useful: make release claims easier to examine, challenge, and improve.
This checklist draws on Model Cards research, Hugging Face guidance, NIST AI RMF, OWASP guidance for LLM applications, and publicly documented transparency requirements. It helps organize evidence; it does not provide a safety certification or compliance guarantee.
Local-first promise
Your form responses stay in this browser's local storage. Model weights are never uploaded.
Your form responses stay in this browser's local storage. Model weights are never uploaded.
Open source direction
Start with documentation. Grow into repeatable checks.
The future CLI will share the same schema as the website, so a model author can validate a release in a local workflow or CI without running arbitrary model code.
OpenWeight Safety Checklist v1.0
Schema 1.0.0 · Markdown + JSON · Deterministic status rules
Read the methodology →Questions before you start
Frequently asked questions
What is an open-weight model?
An open-weight model makes its trained parameters available for download or use, while its training data, license, and usage rights may still have important restrictions.
Does this tool upload my model weights?
No. The MVP runs the form and document generation in your browser. It does not download or upload model weights.
Is this a safety certification?
No. It helps organize documentation and identify evidence gaps. It is not a safety certification, legal opinion, or guarantee of model behavior.
Can I export the result to Hugging Face?
Yes. The Markdown output is structured to be copied into a Hugging Face README.md and edited before release.
Ready when you are
Generate your first release draft.
No account, no model upload, no paywall on the first useful result.
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