SqueezeLLM: Dense-and-Sparse Quantization
Paper • 2306.07629 • Published • 4
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Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
Paper • 2309.02784 • Published • 2 -
Extreme Compression of Large Language Models via Additive Quantization
Paper • 2401.06118 • Published • 14 -
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper • 2402.04291 • Published • 50 -
OneBit: Towards Extremely Low-bit Large Language Models
Paper • 2402.11295 • Published • 24 -
APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
Paper • 2402.14866 • Published -
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs
Paper • 2403.02775 • Published • 13 -
GPTVQ: The Blessing of Dimensionality for LLM Quantization
Paper • 2402.15319 • Published • 22 -
COMQ: A Backpropagation-Free Algorithm for Post-Training Quantization
Paper • 2403.07134 • Published -
OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Paper • 2306.02272 • Published -
QuantEase: Optimization-based Quantization for Language Models
Paper • 2309.01885 • Published • 4 -
SliceGPT: Compress Large Language Models by Deleting Rows and Columns
Paper • 2401.15024 • Published • 73 -
decoupleQ: Towards 2-bit Post-Training Uniform Quantization via decoupling Parameters into Integer and Floating Points
Paper • 2404.12759 • Published -
SpinQuant: LLM quantization with learned rotations
Paper • 2405.16406 • Published • 3 -
Rotation and Permutation for Advanced Outlier Management and Efficient Quantization of LLMs
Paper • 2406.01721 • Published -
Attention-aware Post-training Quantization without Backpropagation
Paper • 2406.13474 • Published • 1 -
Accuracy is Not All You Need
Paper • 2407.09141 • Published • 3 -
FlatQuant: Flatness Matters for LLM Quantization
Paper • 2410.09426 • Published • 15