Build Long-Context AI Apps with Jamba

Learn to use the Jamba model, a hybrid transformer-Mamba architecture trained to handle long contexts, in this new course, Build Long-Context AI Apps with Jamba, built in partnership with AI21 Labs and taught by Chen Wang and Chen Almagor.

The transformer architecture is the foundation of most large language models but it is computationally expensive when handling very long input contexts. 

There’s an alternative to transformers called Mamba. Mamba is a selective state space model that can process very long contexts with a much lower computational cost. However, researchers found that the pure Mamba architecture underperforms in understanding the context, and the quality of the output can be lower even in tasks as simple as repeating the input in the output of the model.

To overcome these challenges, AI21 Labs developed the Jamba model, which combines Mamba’s computational efficiency with the transformer’s attention mechanism to help with the output quality.

In this course, you’ll learn about the Jamba architecture, how it works, and how it is trained. You’ll also learn how to prompt Jamba and use it to process long documents and build long-context RAG apps.

In detail, you’ll:

• Learn key information about Jamba’s large context model and its unique, hybrid architecture that allows for high-quality outputs, high throughput, and low memory usage.
• Learn how Jamba combines transformers with Mamba, a selective state-space model to achieve high performance and quality.
• Use the AI21SDK, especially its document parameter, with an example of prompting a large over 200k-token annual report.
• Use Jamba for tool-calling with hands-on examples from calling simple arithmetic functions to a function that returns SEC 10-Q company quarterly report.
• Learn how the training for long context is done, and the metrics used for evaluating the performance.
• Create a RAG app using the AI21 Conversational RAG tool and build your own RAG pipeline that uses the Jamba model with LangChain.

Start building AI apps that can handle context as long as all of your unread emails from the last 20 years!

Anyone who has basic Python knowledge and wants to learn more about how the Jamba model works and how it is used for building long-context AI apps.