Allowing each quantization group to select among multiple 4-bit grids improves accuracy over single-grid FP4 for both post-training and pre-training of LLMs.
QuIP#: Even better LLM quantization with hadamard incoherence and lattice codebooks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2years
2026 2roles
background 1polarities
background 1representative citing papers
EdgeRazor uses structural mixed-precision quantization, layer-adaptive feature distillation, and entropy-aware KL divergence to achieve 1.88-bit LLMs that outperform prior 2-bit and 3-bit baselines with 4-10x lower training budget.
citing papers explorer
-
Grid Games: The Power of Multiple Grids for Quantizing Large Language Models
Allowing each quantization group to select among multiple 4-bit grids improves accuracy over single-grid FP4 for both post-training and pre-training of LLMs.
-
EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation
EdgeRazor uses structural mixed-precision quantization, layer-adaptive feature distillation, and entropy-aware KL divergence to achieve 1.88-bit LLMs that outperform prior 2-bit and 3-bit baselines with 4-10x lower training budget.