Read the Model Card Before the Leaderboard
Use a model card to screen an open-source AI model for intended use, limits, and evaluation fit.
The teach Start with model-card fit, not leaderboard rank. A model card turns a model from a mystery artifact into a reviewable work product. It should tell you the model's intended use, evaluation approach, limitations, and the contexts where performance may degrade. For open-source AI model selection, read the card in four passes. First, check intended use: is the model meant for chat, retrieval, code, classification, extraction, or general generation? Second, check evaluation: did the authors test anything close to your inputs, users, languages, latency targets, and error costs? Third, check limitations: what failure modes, data gaps, or harmful-use boundaries…
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