6 papers
RPFQ-ViT: Rotated Phase-Frame Quantization for Extremely Low-Bit Weights in Vision Transformers
Mengyuan Fan, Bokai Huang, JiaMing Pan +4
Vision Transformers (ViTs) achieve strong performance on image recognition and mobile vision applications, but their high-dimensional linear projections and attention computations…
REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent
Qian Zhang, Yaoming Li, Zhewen Tan +9
Post-training quantization (PTQ) is essential for deploying large language models (LLMs) under strict resource constraints. State-of-the-art PTQ methods quantize each layer with a…
ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization
Yongge Ma, Guoan Wang, Feiyu Wang +5
Post-training quantization (PTQ) is widely used to reduce the memory and computational cost of large language models. Existing PTQ methods typically obtain an initial quantized mod…
Fairy2i: Training Complex LLMs from Real LLMs with All Parameters in
Feiyu Wang, Xinyu Tan, Bokai Huang +4
Large language models (LLMs) have revolutionized artificial intelligence, yet their massive memory and computational demands necessitate aggressive quantization, increasingly pushi…
HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs
Guoan Wang, Feiyu Wang, Zongwei Lv +2
As large language models (LLMs) continue to scale, deployment is increasingly bottlenecked by the memory wall, motivating a shift toward extremely low-bit quantization. However, mo…
iFairy: the First 2-bit Complex LLM with All Parameters in
Feiyu Wang, Guoan Wang, Yihao Zhang +7
Quantization-Aware Training (QAT) integrates quantization into the training loop, enabling LLMs to learn robust low-bit representations, and is widely recognized as one of the most…