Build LLM apps that can process very long documents using the Jamba model

Learn how the Jamba model integrates transformers and the Mamba architecture to efficiently process long contexts while maintaining quality. Learn how the Jamba model integrates transformers and the Mamba architecture to efficiently process long contexts while maintaining quality.Understand the training process for long context models, and the metrics used to evaluate their performance. Understand the training process for long context models, and the metrics used to evaluate their performance.Gain hands-on experience applying Jamba to tasks such as processing large documents, tool-calling, and building large context RAG apps. Gain hands-on experience applying Jamba to tasks such as processing large documents, tool-calling, and building large context RAG apps._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.

Build Long-Context AI Apps with Jamba

Learn a flexible framework to build a variety of complex AI applications.

Learn a flexible framework to Learn how Haystack components, pipelines, and document stores can be used to build custom AI applications.build custom AI applications with components, pipelines, and document stores Learn how Haystack components, pipelines, and document stores can be used to build custom AI applications.Build a RAG pipeline and extend its ability by creating custom components. Build a RAG pipeline and extend its ability by creating custom components.Use conditional branching, self-reflecting agents, and function calling to build complex pipelines and apps. Use conditional branching, self-reflecting agents, and function calling to build complex pipelines and apps._In Building AI Applications with Haystack you will learn a high-level orchestration framework that helps ensure your applications are flexible, extendible, and maintainable, even as the technology stack changes, user needs arise, and new features are added.Using a framework can provide common features out of the box that significantly speeds up the development process. Haystack offers robust and flexible architecture and framework for building AI applications. It manages complexity and helps you focus more on developing your application at a higher level of abstraction.Throughout the course, you will develop several projects, including a RAG app, a news summarization app, a chat agent with function calling, and a self-reflecting agent with loops.What you’ll do: Learn about the core abstractions and unique building blocks of the Haystack framework and see how these elements can be combined for various AI use cases.Build a RAG pipeline by using Haystack components, pipelines, and document stores.Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs.Use conditional routing to create a branching pipeline with a fall back to web-search when the LLM does not have the context needed to fully respond to the user’s query.Build a self-reflecting agent for named entity recognition with a Haystack pipeline that is able to loop using an output validator custom component.Create a chat agent using OpenAI’s function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent’s capabilities.Start building exciting LLM applications and optimizing your development workflow using Haystack._Anyone who has basic Python knowledge and wants to learn to build and customize complex apps using Haystack!

Build Long-Context AI Apps with Jamba

Building AI Applications With Haystack