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Why Does Post-Training Quantization Work?
Yuxiang Chen, Michael Beyer, Jun Zhu +1
Post-training quantization compresses large language models (LLMs) by storing their weights at reduced precision, and each quantized weight introduces an error into the hidden stat…
LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models
Fengqi Zhu, Rongzhen Wang, Shen Nie +8
While Masked Diffusion Models (MDMs), such as LLaDA, present a promising paradigm for language modeling, there has been relatively little effort in aligning these models with human…
Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs
Chang Gao, Kang Zhao, Runqi Wang +2
Large language models (LLMs) have demonstrated impressive capabilities, but their enormous size poses significant challenges for deployment in real-world applications. To address t…
Beyond 2:4: exploring V:N:M sparsity for efficient transformer inference on GPUs
Kang Zhao, Tao Yuan, Han Bao +6
To date, 2:4 sparsity has stood as the only sparse pattern that can be accelerated using sparse tensor cores on GPUs. In practice, 2:4 sparsity often possesses low actual speedups…
1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit
Chang Gao, Jianfei Chen, Kang Zhao +2
Fully quantized training (FQT) accelerates the training of deep neural networks by quantizing the activations, weights, and gradients into lower precision. To explore the ultimate…