Overview: Advanced Features Preview

Overview: Advanced Features Preview#

Now that you’ve mastered the basics of LLM deployment with Ray Serve LLM, let’s explore some advanced features that make production LLM serving more powerful and flexible.

What We’ll Cover#

In this module, we’ll focus on 3 practical examples that demonstrate advanced capabilities:

  1. LoRA Adapters: Deploy multiple fine-tuned adapters on a single base model

  2. Structured Output: Generate consistent JSON and other structured formats

  3. Tool Calling: Enable models to call external functions and APIs

Why These Features Matter#

LoRA Adapters allow you to:

  • Serve multiple specialized models from one base model

  • Reduce memory usage and deployment complexity

  • Switch between different fine-tuned behaviors at runtime

Structured Output enables:

  • Consistent, parseable responses for applications

  • Integration with downstream systems

  • Better reliability for production use cases

Tool Calling provides:

  • Integration with external APIs and databases

  • Enhanced model capabilities through function execution

  • Building more sophisticated AI applications

Learning Approach#

We’ll take a hands-on approach - each example will show you:

  • Why the feature is useful

  • How to configure it

  • Working code you can run

  • Links to comprehensive guides for deeper learning

Let’s dive in!

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