Jamba 1.5 Mini

Name: Jamba 1.5 Mini
Author: AI21 Labs

by AI21 Labs

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released August 2024context N/A

Jamba 1.5 Mini is a compact model from AI21 Labs built on a hybrid Mamba-Transformer architecture, which blends state space models with attention mechanisms for efficient processing. It handles long contexts — up to 256K tokens — while staying lightweight enough for faster, lower-cost inference. It trades some raw capability for speed and efficiency, making it a practical choice when throughput matters.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Tool Use

Moderate

Multilingual

Moderate

Long Context

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Jamba 1.5 Mini

by AI21 Labs

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released August 2024context N/A

Jamba 1.5 Mini is a compact model from AI21 Labs built on a hybrid Mamba-Transformer architecture, which blends state space models with attention mechanisms for efficient processing. It handles long contexts — up to 256K tokens — while staying lightweight enough for faster, lower-cost inference. It trades some raw capability for speed and efficiency, making it a practical choice when throughput matters.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Tool Use

Moderate

Multilingual

Moderate

Long Context

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.AttentionA mechanism that lets the model focus on relevant parts of the input when generating each output token.Compact ModelA smaller language model designed to use fewer computational resources while still performing useful tasks.Hybrid Mamba-Transformer ArchitectureA neural network design that combines Mamba (a fast, efficient sequence model) with Transformer components to balance speed and capability.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.MambaA state-space model architecture designed to process long sequences faster and with less memory than traditional transformer models.Mamba-Transformer ArchitectureA hybrid model design that combines Mamba (a state-space model) with Transformer components to process long sequences more efficiently than pure Transformers while maintaining strong performance.State SpaceThe set of all possible configurations or conditions an agent can be in, including its needs, sensations, and environment.State Space ModelsA neural network architecture that processes sequences by tracking hidden states over time, offering faster inference and lower memory use than traditional transformers.ThroughputThe number of tokens a model can generate per second, measuring its processing speed.TokensThe basic units of text that a language model processes, typically representing words or word fragments.TransformerThe dominant neural network architecture for language models, using self-attention to process sequences.Transformer ArchitectureA neural network design that processes text by analyzing relationships between all words simultaneously, forming the foundation of modern large language models.