How to read a model card: the four sections to check first

A model card, sometimes called a system card, is a document a provider may publish with a model to describe what it is, how it was tested and where it falls short. You do not need to read all of it. As a builder, start with four parts: context and inputs, license and use terms, limitations, and evaluations. Together they help you judge whether the model fits your project before you write any code.

Why read a model card?

Treat this article as a reading checklist, not a description of any particular card. Look for the card on the page where you download a model, or in the provider's documentation.

Reading it can save time. A short read may tell you that a model cannot handle images, that its terms do not fit your plans, or that it was not tested in your language. That is better to learn before the hackathon than during the demo.

Which sections should I read first?

Skim the headings, then look for these four, in this order.

Section	Look for	Why it matters
Context and inputs	Input limits, input and output types, languages, training data cutoff	Whether the model can take the data you have and produce what you need
License and use terms	The license or terms that apply, permitted uses, restrictions, attribution	What you need to read before you rely on the model
Limitations	Known weaknesses, out-of-scope uses, bias notes	Where it may fail on your task
Evaluations	Benchmarks, test settings, comparisons, safety tests	Rough strengths, and what to test yourself

Do not expect these exact headings. You may see something like "intended use", "model details", "risks" or "performance" instead. Look for the content under whatever heading it has.

If the card is long, use your browser's find feature to jump to words like "license", "context", "limitations" and "evaluation". If a section you need is missing, treat that as information too. Check the provider's documentation, and be more careful about relying on the model for anything that section would have covered.

What should I look for in the context section?

This is the practical fit check. Look for a section on inputs and outputs, and note:

1. Input size. How much text the model can take in one request. This matters if you plan to pass in long documents or chat histories.
2. Input and output types. Text only, or also images, audio or files? Can it return structured output or call tools?
3. Languages. Which languages are listed, and whether any results are given for them. If your users write in another language, test that yourself.
4. Training data cutoff. If the card gives one, questions about later events may need search or another source of current data.
5. Size and hardware. If you plan to run the model yourself, look for any notes on its size and the hardware it needs.

What should I check in the license?

This section is a list of questions to ask, not legal advice. Read the actual terms that apply to the model, and ask your legal team or a qualified adviser if your use is commercial or unusual.

Questions to answer from the terms:

• Is commercial use covered, and on what conditions?
• Are there restrictions on certain uses or industries?
• Can you modify, fine-tune or redistribute the model?
• Do the terms say anything about outputs or anything you build from the model?
• Is attribution asked for, and in what form?
• For hosted models, which terms of service and usage policies apply?

Do not rely on a label such as "open" to answer these questions. Read the terms for the specific model and version you plan to use. For a hackathon, also read the event's rules.

How should I read the limitations section?

Look for a section on known limitations or risks. Read it as a list of things to test, not a list of excuses. It may describe problems found in testing, such as made-up facts, weak results on certain tasks, uneven performance across groups, or sensitivity to prompt wording. If one of these overlaps with your use case, plan for it. Add checks, keep a human in the loop, or narrow the feature.

Also look for out-of-scope uses. If the card says a model is not intended for an area your project touches, take that seriously in what you build and how you describe it.

How much should I trust the evaluations?

Use them as a rough map, not a verdict. Look for a section on evaluations or performance, and when you read the tables:

• Check what each benchmark actually measures. A name is not a description.
• Check the settings, such as number of attempts, special prompting or tools enabled.
• Compare like with like. Numbers from different cards may not use the same setup.
• Look for safety testing results if your project is user facing.
• Write down the claims that matter to you, then test them on your own examples.

Evaluations can suggest where a model might be strong. Only your own test tells you whether it is strong on your task. For example, a team building a support assistant might pick ten real customer questions and run them before choosing a model.

Where to go next

Pick a model, read its card with this checklist, then build with it. Find an online or in-person hackathon at starfieldlabs.ai/events, and see the tutorials at starfieldlabs.ai/tutorials for hands-on guides to working with different models.