26 citations · 50 across the 20 of their papers we have counts for
3 papers · 1 filter
Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment
Abhinav Agarwalla, Abhay Gupta, Alexandre Marques +9
Large language models (LLMs) have revolutionized Natural Language Processing (NLP), but their size creates computational bottlenecks. We introduce a novel approach to create accura…
Sparse Fine-tuning for Inference Acceleration of Large Language Models
Eldar Kurtic, Denis Kuznedelev, Elias Frantar +2
We consider the problem of accurate sparse fine-tuning of large language models (LLMs), that is, fine-tuning pretrained LLMs on specialized tasks, while inducing sparsity in their…
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian +6
Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4 bits per paramet…