Jamba (language model)

Jamba is an open-weights large language model (LLM) developed by AI21 Labs. It utilizes a Mamba-based model built on a novel state space model (SSM) and transformer hybrid architecture. It is a 52 billion parameter model trained using a mixture-of-experts (MoE) technique with 12B active parameters (number of parameters active per token). Jamba can fit up to 256K tokens in its context window and is the largest Mamba-variant LLM created, or 140k tokens in a single 80GB GPU.

Jamba performs well across a number of key measurements including throughput and efficiency while outperforming or matching other state-of-the-art models in its class on a wide range of performance benchmarks while having significantly greater context limits enabling use-cases that require increased context. The model is released with open weights under an Apache 2.0 license.

The company plans to release a beta-version instruct-tuned version on the AI21 Platform in the near future.

Characteristics

 * Context window size: 256k tokens
 * Parameters: 52 billion
 * Architecture: Hybrid Mamba (SSM) Transformer using Mixture of Experts (MoE)