1 citations · 1 across the 3 of their papers we have counts for
7 papers
Modelling the broken age-[Fe/H] relation and metallicity distribution function of mono-age populations
Qinhao Shao, Jianhui Lian
This study investigates the chemical evolution history and radial migration in the Milky Way utilizing high-precision age measurements based on large-scale stellar spectroscopic su…
SAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability
Xianglong Yan, Zenan Wu, Kewei Zhang +2
Extreme low-bit weight-only quantization is an effective approach to reducing the deployment costs of large language models. Below 3 bits, scalar quantization often suffers severe…
JustQuant: You Don't Need Smoothing, SVD, or Rotation for 4-Bit Activation Quantization
Kaicheng Yang, Kaisen Yang, Chunyu Liu +8
Recent generative models have become increasingly powerful, but their inference cost continues to grow. Model quantization offers a promising way to compress these models and accel…
DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
DeepSeek-AI, :, Anyi Xu +585
The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation…
PT-LLM: Post-Training Ternarization for Large Language Models
Xianglong Yan, Chengzhu Bao, Zhiteng Li +6
Large Language Models (LLMs) have shown impressive capabilities across diverse tasks, but their large memory and compute demands hinder deployment. Ternarization has gained attenti…
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution
Libo Zhu, Haotong Qin, Kaicheng Yang +5
One-step diffusion-based image super-resolution (OSDSR) models are showing increasingly superior performance nowadays. However, although their denoising steps are reduced to one an…
BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolution
Kai Liu, Kaicheng Yang, Zheng Chen +5
While super-resolution (SR) methods based on diffusion models (DM) have demonstrated inspiring performance, their deployment is impeded due to the heavy request of memory and compu…