Conversational AI
AI21 Jamba
A family of long-context, hyper-efficient open LLMs built for the enterprise.
Overview
Hybrid Transformer-Mamba architecture for efficiency and performance.
Large 256K context window for processing long documents.
Mixture-of-Experts (MoE) architecture for optimized resource usage.
Open-source model, available for self-hosting and private deployments.
Pricing
$0.2 / 1M input tokens, $0.4 / 1M output tokens
Category
Conversational AI
Company
AI21 Labs
Visual Guide
Interactive PresentationOpen Fullscreen ↗
Key Features
01
Jamba combines the strengths of both Mamba (SSM) and Transformer architectures, enabling high throughput and performance while maintaining a large context window.
02
Process and analyze extremely long documents, such as financial reports, legal contracts, or entire codebases, without losing context.
03
Jamba uses an MoE architecture with 16 experts, of which 2 are active per token, to optimize performance and efficiency.
04
Jamba is an open-source model released under the Apache 2.0 license, allowing for self-hosting and custom fine-tuning.
Real-World Use Cases
Financial Analysis
A financial analyst needs to quickly analyze a lengthy annual report to identify key trends and risks.
Legal Document Review
A legal team needs to review thousands of contracts to identify specific clauses or potential issues.
Customer Support Chatbot
A company wants to build a chatbot that can answer customer questions based on a large knowledge base of technical documentation.
Quick Start
1
Step 1
Install the necessary libraries: transformers, mamba-ssm, and causal-conv1d.
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Step 2
Download the model from Hugging Face.
3
Step 3
Load the model and tokenizer using the transformers library.
4
Step 4
Start generating text with the model.