How to Read a Model Card: What PMs and Engineers Actually Need to Know
What model cards tell you about training data, benchmarks, limitations, and bias — and how to use them to make informed model selection decisions.
Model cards are the nutrition labels of AI. Most people ignore them. The people who read them carefully are the ones who avoid nasty surprises in production.
A model card is a document published with a model release that describes what the model is, what it was trained on, how it was evaluated, where it performs well, where it doesn't, and what risks it poses. Here's how to read one without getting lost.
What a model card should contain
| Section | What to look for |
|---|---|
| Model description | Architecture, parameter count, training modalities (text only? multimodal?) |
| Intended uses | What tasks the model was designed for — and explicitly what it was NOT designed for |
| Training data | Sources, time range, filtering applied — this tells you about potential biases and knowledge cutoffs |
| Evaluation results | Which benchmarks, what scores, how they compare to baselines |
| Limitations | Where the model is known to underperform — this is the honest part, read it carefully |
| Ethical considerations | Known biases, risks, and mitigations applied |
| Usage recommendations | When to use, when not to use, recommended configurations |
Red flags in a model card
- No limitations section, or a limitations section that only lists 'general LLM limitations' without specifics — incomplete card
- Evaluation only on standard benchmarks (MMLU, HumanEval) with no task-specific evaluations — doesn't tell you how it performs on your use case
- Training data described as 'publicly available internet data' with no details — tells you nothing about what biases it may have absorbed
- No information about RLHF or safety fine-tuning — if the model was just pretrained, it may have no safety guardrails
- Outdated knowledge cutoff for a use case requiring current information — the card will tell you this if you read it
How to use a model card for decision-making
When evaluating a model for a specific use case, read the model card with a specific question in mind: 'Is there anything in this card that would make this model unsuitable for my use case?' Check: does the intended use include my domain? Are there specific limitations that affect my task? Is the knowledge cutoff recent enough? Are there known biases that would be problematic for my users?
Model cards are written by the model developers, so they're not fully objective. But even a carefully worded card reveals important information if you read between the lines. A vague or incomplete limitations section is itself a signal.
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