3 citations · 4 across the 8 of their papers we have counts for
6 papers · 1 filter
WUSH: Near-Optimal Adaptive Transforms for LLM Quantization
Jiale Chen, Vage Egiazarian, Roberto L. Castro +2
Quantizing LLM weights and activations is a standard approach for efficient deployment, but a few extreme outliers can stretch the dynamic range and amplify low-bit quantization er…
The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm
Jiale Chen, Yalda Shabanzadeh, Elvir Crnčević +2
Quantizing the weights of large language models (LLMs) from 16-bit to lower bitwidth is the de facto approach to deploy massive transformers onto more affordable accelerators. Whil…
Flow matching for reaction pathway generation
Ping Tuo, Jiale Chen, Ju Li
Elucidating reaction mechanisms hinges on efficiently generating transition states (TSs), products, and complete reaction networks. Recent generative models, such as diffusion mode…
Quartet: Native FP4 Training Can Be Optimal for Large Language Models
Roberto L. Castro, Andrei Panferov, Soroush Tabesh +5
Training large language models (LLMs) models directly in low-precision offers a way to address computational costs by improving both throughput and energy efficiency. For those pur…
Scalable Multitemperature Free Energy Sampling of Classical Ising Spin States
Ping Tuo, Zezhu Zeng, Jiale Chen +1
Generative models have advanced significantly in sampling material systems with continuous variables, such as atomistic structures. However, their application to discrete variables…
QuEST: Stable Training of LLMs with 1-Bit Weights and Activations
Andrei Panferov, Jiale Chen, Soroush Tabesh +3
One approach to reducing the massive costs of large language models (LLMs) is the use of quantized or sparse representations for training or deployment. While post-training compres…