Deploying Advanced LLMs with Custom PyFuncs in MLflow
Advanced Large Language Models (LLMs) such as the MPT-7B instruct transformer are intricate and have requirements that don’t align with traditional MLflow flavors. This demands a deeper understanding and the need for custom solutions.
What’s in this tutorial?
This guide is designed to provide insights into the deployment of advanced LLMs with MLflow, with a focus on using custom PyFuncs to address challenges:
The World of LLMs: An introduction to LLMs, particularly models like the MPT-7B instruct transformer. We’ll delve into their intricacies, importance, and the challenges associated with their deployment.
Why Custom PyFuncs for LLM Deployment?: We’ll explore the reasons behind the need for custom PyFuncs in the context of LLMs. How do they provide a bridge, ensuring that LLMs can be seamlessly deployed while adhering to MLflow’s standards?
Managing Complex Behaviors: How custom PyFuncs can help in handling intricate model behaviors and dependencies that aren’t catered to by MLflow’s default flavors.
Interface Data Manipulation: Delve into how custom PyFuncs allow the manipulation of interface data to generate prompts, thereby simplifying end-user interactions in a RESTful environment.
Crafting Custom PyFuncs for LLM Deployment: A step-by-step walkthrough on how to define, manage, and deploy an LLM using a custom PyFunc. We’ll look at how to design a pyfunc to address LLM requirements and behaviors, and then how to deploy it using MLflow.
Challenges with Traditional LLM Deployment: Recognize the issues and limitations when trying to deploy an advanced LLM using MLflow’s built-in capabilities. Understand why custom PyFuncs become essential in such scenarios.
By the conclusion of this guide, you’ll possess a deep understanding of how to deploy advanced LLMs in MLflow using custom PyFuncs. You’ll appreciate the role of custom PyFuncs in making complex deployments streamlined and efficient.