8 citations · 15 across the 7 of their papers we have counts for
8 papers
LampAttention: Look-Ahead Mixed-Precision FlashAttention for Dedicated Accelerators
Stanislav Budzinskiy, Marian Gloser, Tolunay Yilmaz +5
While most attention logits can be computed in low precision without degrading numerical stability, current attention kernels fail to exploit this phenomenon. We introduce a novel…
Mechanism-Aligned Observability for Training Instability of Large Language Models
Ruixuan Huang, Yipei Wang, Hantao Huang +7
Frontier large language model training consumes massive accelerator fleets over long wall-clock times, making stability failures extremely costly. However, global symptoms such as…
LAMP: Look-Ahead Mixed-Precision Inference of Large Language Models
Stanislav Budzinskiy, Marian Gloser, Tolunay Yilmaz +5
Mixed-precision computations are a hallmark of the current stage of AI, driving the progress in large language models towards efficient, locally deployable solutions. This article…
Metis: Training LLMs with FP4 Quantization
Hengjie Cao, Mengyi Chen, Yifeng Yang +13
This work identifies anisotropy in the singular value spectra of parameters, activations, and gradients as the fundamental barrier to low-bit training of large language models (LLM…
MLLM-Based UI2Code Automation Guided by UI Layout Information
Fan Wu, Cuiyun Gao, Shuqing Li +2
Converting user interfaces into code (UI2Code) is a crucial step in website development, which is time-consuming and labor-intensive. The automation of UI2Code is essential to stre…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
Dynaseal: A Backend-Controlled LLM API Key Distribution Scheme with Constrained Invocation Parameters
Jiahao Zhao, Jiayi Nan, Lai Wei +2
Due to the exceptional performance of Large Language Models (LLMs) in diverse downstream tasks,there has been an exponential growth in edge-device requests to cloud-based models.Ho…
Generative Monoculture in Large Language Models
Fan Wu, Emily Black, Varun Chandrasekaran
We introduce {\em generative monoculture}, a behavior observed in large language models (LLMs) characterized by a significant narrowing of model output diversity relative to availa…