34 citations · 109 across the 15 of their papers we have counts for
17 papers
UNREAL: Unifying Retrieval and Long-Context with a Single Model
Edan Kinderman, Elad Hoffer, Yochai Blau +4
Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an entire corpus. We ask whether…
Normalized Architectures are Natively 4-Bit
Maxim Fishman, Brian Chmiel, Ron Banner +2
Training large language models at 4-bit precision is critical for efficiency. We show that nGPT, an architecture that constrains weights and hidden representations to the unit hype…
FP4 All the Way: Fully Quantized Training of LLMs
Brian Chmiel, Maxim Fishman, Ron Banner +1
We demonstrate, for the first time, fully quantized training (FQT) of large language models (LLMs) using predominantly 4-bit floating-point (FP4) precision for weights, activations…
EXAQ: Exponent Aware Quantization For LLMs Acceleration
Moran Shkolnik, Maxim Fishman, Brian Chmiel +3
Quantization has established itself as the primary approach for decreasing the computational and storage expenses associated with Large Language Models (LLMs) inference. The majori…
Scaling FP8 training to trillion-token LLMs
Maxim Fishman, Brian Chmiel, Ron Banner +1
We train, for the first time, large language models using FP8 precision on datasets up to 2 trillion tokens -- a 20-fold increase over previous limits. Through these extended train…
Bimodal Distributed Binarized Neural Networks
Tal Rozen, Moshe Kimhi, Brian Chmiel +2
Binary Neural Networks (BNNs) are an extremely promising method to reduce deep neural networks' complexity and power consumption massively. Binarization techniques, however, suffer…