Dissecting Quantization Error: A Concentration-Alignment Perspective | ScienceToStartup | ScienceToStartup

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References (22)

[1]

WUSH: Near-Optimal Adaptive Transforms for LLM Quantization

2025Jiale Chen, Vage Egiazarian et al.

[2]

FPTQuant: Function-Preserving Transforms for LLM Quantization

2025B. V. Breugel, Yelysei Bondarenko et al.

[3]

OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting

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"A new method to reduce quantization error in LLMs using Concentration-Alignment Transforms, potentially improving model efficiency at low precision."

Quantization Techniques for ML Models•Score: 3•View PDF ↗

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Dissecting Quantization Error: A Concentration-Alignment Perspective

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References (22)

Founder's Pitch

"A new method to reduce quantization error in LLMs using Concentration-Alignment Transforms, potentially improving model efficiency at low precision."

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Dissecting Quantization Error: A Concentration-Alignment Perspective

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References (22)

Founder's Pitch

"A new method to reduce quantization error in LLMs using Concentration-Alignment Transforms, potentially improving model efficiency at low precision."

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