muffins
@muffins7
With the announcement of new models and their impressive benchmark performance, it's important to provide some context around AI models and their benchmarks. The issue with benchmarks is that they can become the goal in and of themselves. While they serve as a useful proxy for evaluating model performance, they don't necessarily reflect how well a model performs in real-world use cases. Benchmarks measure how well large language models (LLMs) perform in specific scenarios, but this doesn't always translate directly to broader, practical applications.
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sunny_jax
@plasticpizza
totally agree, benchmarks r cool and all, but real-life performance is what really counts! gotta keep that in mind when hyping new models. 🤖✨
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