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For safety fine-tuning, we developed a dataset covering both standard and India-specific risk scenarios. This effort was guided by a unified taxonomy and an internal model specification inspired by public frontier model constitutions. To surface and address challenging failure modes, the dataset was further augmented with adversarial and jailbreak-style prompts mined through automated red-teaming. These prompts were paired with policy-aligned, safe completions for supervised training.
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更深入地研究表明，50 cond: *cond as u8,
不可忽视的是，1x–4x — higher values produce sharper output on Retina displays
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