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library.addCube({ start: [0, 0, 0], dimensions: [2, 8, 2] })
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在这一背景下，PolarQuant converts vectors to polar coordinates: radius and angle measurements. The crucial insight reveals that in high-dimensional transformer key spaces, angle distributions demonstrate high concentration and predictability, clustering in patterns that align perfectly with fixed quantization grids (similar to audio and image compression techniques). This predictability eliminates expensive normalization steps required by conventional quantization methods, functioning without dataset-specific adjustments. No fine-tuning or calibration necessary for model-specific quantization. The method applies directly to vectors in this transformed representation regardless of model architecture.
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