5 papers
ECO: Quantized Training without Full-Precision Master Weights
Mahdi Nikdan, Amir Zandieh, Dan Alistarh +1
Quantization has significantly improved the compute and memory efficiency of Large Language Model (LLM) training. However, existing approaches still rely on accumulating their upda…
Efficient Data Selection at Scale via Influence Distillation
Mahdi Nikdan, Vincent Cohen-Addad, Dan Alistarh +1
Effective data selection is critical for efficient training of modern Large Language Models (LLMs). This paper introduces Influence Distillation, a novel, mathematically-justified…
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…
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…
HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs
Saleh Ashkboos, Mahdi Nikdan, Soroush Tabesh +3
Quantized training of Large Language Models (LLMs) remains an open challenge, as maintaining accuracy while performing all matrix multiplications in low precision has proven diffic…