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Before using an open-weight model, verify the exact release’s terms, your organization’s legal role, and whether the complete deployed system is safe and fit for its intended business task. A downloadable model’s weights do not, by themselves, establish commercial permission, open-source status, regulatory exemption, or safety.
1. Which exact model release are you evaluating?
Start with the specific files and release you intend to use—not just the model family or a product name. Record the model name, version or revision, source, license, and any separate acceptable-use policy. Keep a copy or dated record of the applicable terms so your team can establish which terms it reviewed when making the decision.
Read the actual terms for each planned activity. Check whether they permit commercial use, modification or fine-tuning, redistribution, and providing access through a hosted service. Note any conditions, restrictions, or notice requirements; preserve required notices wherever the terms require them. A permission for one activity does not automatically establish permission for another.
If a license or policy is unclear, do not treat the ability to download the weights as a grant of permission. Have the appropriate reviewer assess the exact text and planned use before proceeding.
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2. What role will your organization have, and which rules apply?
Map how the model will be used and who is responsible for each part of the service. Depending on the concrete arrangement, your organization may be a provider, deployer, or another party subject to relevant rules. These roles and obligations are not determined solely by whether you downloaded or fine-tuned the weights.
EU AI Act considerations
For general-purpose AI (GPAI) models, provider obligations under the EU AI Act can include maintaining technical documentation, providing information to downstream providers, adopting a copyright-compliance policy, and publishing a sufficiently detailed summary of training content. The European Commission says relevant GPAI obligations began applying on 2 August 2025; consult its GPAI questions and answers and guidance on provider obligations for current details.
A conditional exemption from specified documentation duties may apply to qualifying open-source GPAI models when the applicable license and public-information conditions are met. It is not a blanket exemption: it does not apply to GPAI models with systemic risk, and qualifying models remain subject to the copyright-policy and public training-content-summary obligations. The AI Act Service Desk explanation of open-source GPAI models describes these conditions. Confirm whether the exemption applies to the exact model and circumstances rather than inferring it from the phrase “open-weight.”
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These are provider-focused rules, not a statement that every organization using a model internally has every listed provider duty. The Commission materials address the AI Act; they do not settle obligations under other laws. Identify the jurisdictions, affected people, and roles relevant to your use, then get legal or compliance review where needed.
3. Is the complete deployed system secure and manageable?
Assess the system you will actually operate, not just the model weights. Include the inference runtime, adapters or fine-tunes, dependencies, hosting infrastructure, data flows, prompts, connected tools, and user interface. A model can be only one part of a system’s exposure or failure path.
- Confidentiality: identify where prompts, retrieved documents, fine-tuning data, logs, and outputs can be accessed or stored.
- Integrity: consider whether model files, dependencies, adapters, prompts, or outputs could be altered in ways that affect behavior or decisions.
- Availability and resilience: plan for service interruption, resource constraints, degraded performance, and recovery or rollback.
- Security controls: review access boundaries, deployment configuration, connected tools, and the origin and integrity of third-party files and software.
- Failure handling: define what the application should do when the model is uncertain, unavailable, produces an unsafe response, or returns an output the application cannot use.
The National Institute of Standards and Technology (NIST) notes that AI cybersecurity risks overlap with conventional software and hardware risks. Its security and resilience guidance treats these as trustworthiness characteristics. NIST’s AI RMF Core also calls for policies and procedures addressing third-party software, data, and supply-chain risks, as well as documenting risks and evaluating security and resilience.
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4. Does it perform well enough for your actual business task?
Define the intended use before choosing a model. Record the task, intended users, people affected by the output, operating conditions, limitations, required performance, and outcomes that would be unacceptable. Those details determine what to test and what safeguards are necessary.
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- Test the assembled application. Evaluate the model together with its prompts, tools, retrieval or other data sources, runtime, and user interface under the conditions in which it will be used.
- Record results and limitations. Document what worked, what failed, known boundaries, and any controls needed to manage those failures.
- Set operational checks. Monitor performance and incidents after launch, and specify how users or staff can report problems and how the organization will respond.
Generic benchmark results do not prove that a business application is reliable or suitable for your use case. NIST describes its AI Risk Management Framework as voluntary guidance intended to help incorporate trustworthiness considerations into AI products, services, and systems. The framework is a useful structure for organizing evaluation, not a certification that a particular model or deployment is safe or fit for purpose. See the NIST AI Risk Management Framework overview.
5. What data and third parties are involved?
Trace data through the full workflow: prompts, fine-tuning, retrieval, logs, storage, and generated outputs. Identify whether it includes personal, confidential, regulated, or proprietary information, and decide who can access it and how it will be protected.
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- Set access controls and retention periods for inputs, training material, logs, and outputs.
- Define deletion procedures and how requests or incidents involving data will be handled.
- Assess applicable privacy and data-protection requirements for the relevant jurisdictions and use.
- Review the origin, terms, integrity, and security of third-party model files, software, data, and services.
NIST’s AI RMF Core addresses privacy and third-party and supply-chain risk. Use that as a prompt to include external components and providers in the review, rather than limiting it to the model’s own documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. How should you compare candidate models?
Compare candidates against the same task and deployment conditions. This helps distinguish a genuine fit from a difference in test setup or licensing assumptions.
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| Comparison area | What to check for each candidate |
|---|---|
| Permissions and obligations | Terms for commercial use, modification, redistribution, hosted access, and required notices. |
| Documentation | Release details, intended uses, known limitations, and available training and evaluation information. |
| Task performance | Results and failure behavior on the same representative business cases and edge cases. |
| Security and operations | Requirements and risks across the model, runtime, dependencies, hosting, and supply chain. |
| Data handling | Privacy impact, data flows, retention and deletion controls, and infrastructure choices. |
| Lifecycle management | Version pinning, monitoring, incident response, maintenance, and ability to replace or roll back the model. |
There is no universal ranking implied by these criteria. A candidate should be judged against your requirements and the specific release and deployment you have evaluated.
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7. Who owns the decision, and what triggers a new review?
Name a business owner and assign technical, legal or privacy, and security reviewers. Record the intended use, release and terms reviewed, evaluation results, safeguards, and any residual risks the organization accepts. Make clear who can approve launch and who can pause or roll back the system if its behavior or operating conditions change.
Reassess the decision if the model revision or terms change, if data or infrastructure changes, or if the system is put to a different use. A release that was acceptable for one task and deployment is not automatically acceptable for another.
Quick Recap
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