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这个模型并不像其他 AI 巨头那样「刷分」，而是朝着小型化、端侧化、低延迟的方向做了极致优化，将视觉处理所需的 Token 降到传统 ViT 的 1/16，极大降低延迟，可以根据摄像头捕捉到的内容实时给出判断，反应速度非常快。，这一点在服务器推荐中也有详细论述
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1983 to 1988, making up almost a decade of IBM's efforts and very few sales.
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转换为 TFLite 后，你可以选择将模型打包为两种可在设备端运行的格式之一：.task（用于 MediaPipe）或 .litertlm（用于 LiteRT-LM）。