AI21 Jamba
AI Assistantsv1.6A 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.
Visual Guide
📊 Interactive PresentationInteractive presentation with key insights and features
Key Features
Jamba combines the strengths of both Mamba (SSM) and Transformer architectures, enabling high throughput and performance while maintaining a large context window.
Process and analyze extremely long documents, such as financial reports, legal contracts, or entire codebases, without losing context.
Jamba uses an MoE architecture with 16 experts, of which 2 are active per token, to optimize performance and efficiency.
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
ForA financial analyst needs to quickly analyze a lengthy annual report to identify key trends and risks.
Example Prompt / Workflow
Legal Document Review
ForA legal team needs to review thousands of contracts to identify specific clauses or potential issues.
Example Prompt / Workflow
Customer Support Chatbot
ForA company wants to build a chatbot that can answer customer questions based on a large knowledge base of technical documentation.
Example Prompt / Workflow
Frequently Asked Questions
Pricing
Jamba-1.5 Mini
- ✓ Efficient & lightweight model for a wide range of tasks.
Jamba-1.5 Large
- ✓ Most powerful model for complex tasks.
Pros & Cons
Pros
- ✓ Extremely large 256K context window.
- ✓ Hybrid architecture offers a good balance of performance and efficiency.
- ✓ Open-source and available for private deployments.
- ✓ High throughput and low latency.
Cons
- ✕ The base model is not instruction-tuned and requires fine-tuning for specific applications.
- ✕ Requires specific hardware (CUDA) and software dependencies to run optimized kernels.
- ✕ Relatively new model, so the community and tooling are still growing.
Quick Start
Step 1
Install the necessary libraries: transformers, mamba-ssm, and causal-conv1d.
Step 2
Download the model from Hugging Face.
Step 3
Load the model and tokenizer using the transformers library.
Step 4
Start generating text with the model.