We present Jamba, a new base large language model based on a novel hybrid
Transformer-Mamba mixture-of-experts (MoE) architecture. Specifically, Jamba
interleaves blocks of Transformer and Mamba layers, enjoying the benefits of
both model families. MoE is added in some of these layers to increase model
capacity while keeping active parameter usage manageable. This flexible
architecture allows resource- and objective-specific configurations. In the
particular configuration we have implemented, we end up with a powerful model
that fits in a single 80GB GPU. Built at large scale, Jamba provides high
throughput and small memory footprint compared to vanilla Transformers, and at
the same time state-of-the-art performance on standard language model
benchmarks and long-context evaluations. Remarkably, the model presents strong
results for up to 256K tokens context length. We study various architectural
decisions, such as how to combine Transformer and Mamba layers, and how to mix
experts, and show that some of them are crucial in large scale modeling. We
also describe several interesting properties of these architectures which the
training and evaluation of Jamba have revealed, and plan to release checkpoints
from various ablation runs, to encourage further exploration of this novel
architecture. We make the weights of our implementation of Jamba publicly
available under a permissive license.

基于混合 Transformer-Mamba 和专家混合 (MoE) 架构，Jamba 是一个强大的基础大型语言模型，具有资源和目标特定的配置，能够在单个 80GB GPU 上提供高吞吐量和小内存占用，同时在标准语言模型基准测试和长文本上展示出最先进的表现。