5 papers
Saten: Sparse Augmented Tensor Networks for Post-Training Compression of Large Language Models
Ryan Solgi, Kai Zhen, Rupak Vignesh Swaminathan +4
The efficient implementation of large language models (LLMs) is crucial for deployment on resource-constrained devices. Low-rank tensor compression techniques, such as tensor-train…
Wanda++: Pruning Large Language Models via Regional Gradients
Yifan Yang, Kai Zhen, Bhavana Ganesh +11
Large Language Models (LLMs) pruning seeks to remove unimportant weights for inference speedup with minimal accuracy impact. However, existing methods often suffer from accuracy de…
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…
QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models
Jiajun Zhou, Yifan Yang, Kai Zhen +6
Language Models (LLMs) are often quantized to lower precision to reduce the memory cost and latency in inference. However, quantization often degrades model performance, thus fine-…
MaZO: Masked Zeroth-Order Optimization for Multi-Task Fine-Tuning of Large Language Models
Zhen Zhang, Yifan Yang, Kai Zhen +4
Large language models have demonstrated exceptional capabilities across diverse tasks, but their fine-tuning demands significant memory, posing challenges for resource-constrained…