Zamba: A Compact 7B SSM Hybrid Model
Authors: name: Paolo Glorioso, name: Quentin Anthony, name: Yury Tokpanov, name: James Whittington, name: Jonathan Pilault, name: Adam Ibrahim, name: Beren Millidge

In this technical report, we present Zamba, a novel 7B SSM-transformer hybrid
model which achieves competitive performance against leading open-weight models
at a comparable scale. Zamba is trained on 1T tokens from openly available
datasets and is the best non-transformer model at this scale. Zamba pioneers a
unique architecture combining a Mamba backbone with a single shared attention
module, thus obtaining the benefits of attention at minimal parameter cost. Due
to its architecture, Zamba is significantly faster at inference than comparable
transformer models and requires substantially less memory for generation of
long sequences. Zamba is pretrained in two phases: the first phase is based on
existing web datasets, while the second one consists of annealing the model
over high-quality instruct and synthetic datasets, and is characterized by a
rapid learning rate decay. We open-source the weights and all checkpoints for
Zamba, through both phase 1 and annealing phases.