#ArchitectureDesign

Over the past few years, language models have emerged as a fundamental component of artificial intelligence, significantly advancing various natural language processing tasks. However, Transformer-based models face challenges in terms of efficiency and memory usage, particularly when working with lengthy sequences. Jamba introduces a novel hybrid architecture integrating Transformer layers, Mamba layers, and Mixture-of-Experts (MoE) to address these limitations. By interleaving Transformer and Mamba layers, Jamba leverages their strengths in capturing complex patterns and efficiently processing long sequences. Incorporating MoE enhances Jamba’s capacity and flexibility. Jamba supports context lengths up to 256K tokens, excelling in tasks requiring understanding of extended text passages. It demonstrates impressive throughput, a small memory footprint, and state-of-the-art performance across benchmarks, making it highly adaptable to various resource constraints and deployment scenarios.