Leech Lattice Vector Quantization for Efficient LLM Compression

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References (21)

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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
2025Loubna Ben Allal, Anton Lozhkov et al.
[4]
Pyramid Vector Quantization for LLMs
2024Tycho F. A. van der Ouderaa, Maximilian L. Croci et al.
[5]
QTIP: Quantization with Trellises and Incoherence Processing
2024Albert Tseng, Qingyao Sun et al.
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DataComp-LM: In search of the next generation of training sets for language models
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QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
2024Saleh Ashkboos, Amirkeivan Mohtashami et al.
[8]
GPTVQ: The Blessing of Dimensionality for LLM Quantization
2024M. V. Baalen, Andrey Kuzmin et al.
[9]
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
2024Albert Tseng, Jerry Chee et al.
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2023Jerry Chee, Yaohui Cai et al.
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GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
2022Elias Frantar, Saleh Ashkboos et al.
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Founder's Pitch

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

LLM CompressionScore: 3View PDF ↗

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1/4 signals

2.5

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2/4 signals

5

Series A Potential

0/4 signals

0

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