Leech Lattice Vector Quantization for Efficient LLM Compression | ScienceToStartup | ScienceToStartup

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References (21)

[1]

FPTQuant: Function-Preserving Transforms for LLM Quantization

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

[2]

Model-Preserving Adaptive Rounding

2025Albert Tseng, Zhaofeng Sun et al.

[3]

SmolLM2: When Smol Goes Big - Data-Centric Training of a Small Language Model

Founder's Pitch

"Leech Lattice Vector Quantization offers a novel approach to efficiently compress large language models using high-dimensional lattice structures."

LLM Compression•Score: 3•View PDF ↗

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Leech Lattice Vector Quantization for Efficient LLM Compression

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References (21)

Founder's Pitch

"Leech Lattice Vector Quantization offers a novel approach to efficiently compress large language models using high-dimensional lattice structures."

Commercial Viability Breakdown

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Leech Lattice Vector Quantization for Efficient LLM Compression

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References (21)

Founder's Pitch

"Leech Lattice Vector Quantization offers a novel approach to efficiently compress large language models using high-dimensional lattice structures."

Commercial Viability Breakdown

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