Before using an open-weight model in a business, verify the exact release’s terms, define the intended use, assess the complete deployed system, and test it on representative business tasks. Then review data and supply-chain risks, assign accountable owners, and set a trigger for reassessment. “Open-weight” means the weights are accessible under some conditions; it does not, by itself, grant unrestricted commercial rights, make a model open source, establish that it is safe, or exempt its use from regulation.
1. Which exact model release are you evaluating?
Start with the specific downloadable release, not the model-family name or a claim that its weights are publicly available. Record enough information to identify what your team assessed and deployed:
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- Model name, release or revision, and the source from which the files were obtained.
- The license that applies to that release, plus any separate acceptable-use policy or other terms.
- Any conditions on commercial use, modification, fine-tuning, redistribution, or offering access through a hosted service.
- Required notices, attribution, or other obligations, and a copy of the terms in force when your organization approved the release.
Check whether terms apply differently to internal use, customer-facing features, modified weights, or a hosted service. Do not assume that permission for one activity covers the others. If terms are unclear or appear inconsistent, get qualified legal review before relying on the release.
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Write down the business task and operating context before testing. Specify who will use the system, who may be affected by its outputs, what information it will receive, and what the model is and is not expected to do. Include operating conditions and limitations, performance requirements, and outcomes that are unacceptable.
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Evaluate the assembled application—not just the model—against representative examples from the intended task and relevant edge cases. Define in advance what acceptable performance means for the business and what happens when the system is uncertain, unavailable, or wrong. Keep test results and known limitations with the approval record. Generic benchmark scores do not establish that your particular application is reliable or fit for purpose.
Plan for monitoring after launch and define how staff can report incidents, who investigates them, and what actions are available if performance or behavior falls outside agreed limits.
3. What does the complete deployed system include?
Assess the model as one component in a system. The review should cover the model weights, inference runtime, adapters or fine-tunes, dependencies, hosting infrastructure, data flows, system prompts, connected tools, and user interface. A change in one component can affect the behavior or risk of the whole application.
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Review confidentiality, integrity, availability, security, resilience, intended behavior, and failure handling. NIST notes that AI security risks overlap with conventional software and hardware security risks; a model assessment does not replace ordinary security review of the surrounding stack. See NIST’s overview of AI security and resilience.
4. Which data enters, changes, or leaves the system?
Map data through prompts, fine-tuning, logs, storage, and outputs. Identify personal, confidential, regulated, and proprietary information, including data that might be included in model inputs or retained by supporting services. For each flow, establish who can access it, how long it is retained, how it can be deleted, and how a privacy or security incident will be handled.
Assess the legal and privacy requirements that apply to your organization, data, users, and deployment locations. The relevant requirements depend on those facts; a general label for a model does not settle them.
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5. How will you verify suppliers and the supply chain?
Review the origin, terms, integrity, and security of third-party model files, software, data, and services. Document what components enter the deployment, how they are obtained, and who is responsible for keeping them current and secure. Consider what happens if a dependency, hosted service, or model release changes or becomes unavailable.
NIST’s AI Risk Management Framework Core calls for policies and procedures that address third-party software, data, and supply-chain risks, as well as documenting risks and impacts and evaluating AI security and resilience. The framework is voluntary guidance, not a certification or a substitute for applicable legal requirements. NIST describes it as intended to help incorporate trustworthiness considerations into AI design, development, use, and evaluation: AI Risk Management Framework overview and AI RMF Core. NIST’s Generative Artificial Intelligence Profile, published July 26, 2024, is a cross-sector companion resource with suggested generative-AI risk-management actions.
6. Does the EU AI Act change your obligations?
If your organization, model, or deployment falls within the EU AI Act, identify your role for the concrete facts. Provider obligations are not automatically the obligations of every organization that uses a model internally; determine whether your organization is a provider, deployer, or otherwise regulated in the particular arrangement.
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The European Commission says relevant obligations for general-purpose AI models entered into application on August 2, 2025. Some qualifying open-source GPAI models may receive an exemption from specified documentation duties when the license and public-information conditions are met. That exemption is conditional: it does not apply to systemic-risk GPAI models, and qualifying models remain subject to the copyright-policy and public training-content-summary obligations. The Commission’s GPAI questions and answers, guidelines on obligations for GPAI providers, and AI Act Service Desk FAQ on open-source GPAI models explain the conditions. The exemption is not a general regulatory clearance, and the FAQ does not resolve requirements under other laws.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Who owns the decision, and what triggers a new review?
Name a business owner and identify technical, legal or privacy, and security reviewers. Record the intended use, evidence considered, limitations, safeguards, and residual risks the organization has accepted. Assign responsibility for monitoring and incident response rather than leaving those tasks implicit.
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Set a review trigger for a change in model revision, license, data, deployment context, or use case. Keep a version-pinned record of the approved release and system configuration so the team can determine whether a later change requires renewed review, rollback, or replacement.
How should you compare more than one candidate?
Compare candidates on the same business task and deployment conditions. A general model ranking cannot replace evidence about your own workload, system design, and risk tolerance.
| Comparison area | What to compare |
|---|---|
| Terms | Permissions, restrictions, and obligations for commercial use, modification, redistribution, and hosted access. |
| Documentation | Intended uses, known limitations, and available information about training and evaluation. |
| Task performance | Results and failure behavior on the same representative business cases and edge cases. |
| Security and operations | Runtime, dependencies, supply chain, infrastructure requirements, and failure handling for the complete system. |
| Data and privacy | Data handling, privacy impact, and infrastructure choices under the same proposed deployment. |
| Lifecycle | Version pinning, maintenance, monitoring, incident response, and the ability to replace or roll back a candidate. |
Use the comparison to identify which candidate meets the documented requirements and what risks remain. The available guidance supports a decision process, not a universal ranking or recommendation of a particular model.
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