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Our initial setup involves installing the necessary Z.AI and OpenAI packages, then securely obtaining our API credential using concealed terminal input. After initializing the ZaiClient, we execute a fundamental chat request to GLM-5, instructing it to describe the Mixture-of-Experts design. We subsequently investigate streaming outputs, observing tokens being generated live as the model produces a single-line Python command for prime number verification.
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在这一背景下,The evaluation solver uses fixed settings: λ = 0.01, diversity bonus = 0.0, temperature = 0.001. It executes more internal steps (starting at 8000, scaling with population size) and returns the final strategy instead of the average, providing a responsive, low-noise exploitability measure. This training/evaluation difference emerged from the search, not human input.
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