most cited1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit

1 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2025

Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models

Yi Feng, Jiaqi Wang, Wenxuan Zhang +6

Recent progress in large language models (LLMs) has opened new possibilities for mental health support, yet current approaches lack realism in simulating specialized psychotherapy…

cs.LG2025

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…

cs.LG2024★ 1 cited

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…

cs.LG2024★ 1 cited

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…

cs.CL2024★ 1 cited

SS-GEN: A Social Story Generation Framework with Large Language Models

Yi Feng, Mingyang Song, Jiaqi Wang +5

Children with Autism Spectrum Disorder (ASD) often misunderstand social situations and struggle to participate in daily routines. Social Stories are traditionally crafted by psycho…