Learn to extend LLMs with custom functionality via function-calling, enabling them to form calls to external functions.
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Instructors: Jiantao Jiao, Venkat Srinivasan
Learn to extend LLMs with custom functionality via function-calling, enabling them to form calls to external functions.
Extract structured data from natural language inputs, making real-world data usable for analysis.
Build an end-to-end application that processes customer service transcripts using LLMs.
This course will teach you two critical skills for building applications with LLMs: function-calling and structured data extraction.
Function-calling allows you to extend LLMs with custom capabilities by enabling them to form calls to external functions based on natural language instructions. Structured data extraction enables LLMs to pull usable information from unstructured text.
You’ll work with NexusRavenV2-13B, an open source model fine-tuned for function-calling and data extraction. The model, available on Hugging Face, has outperformed GPT-4 in some function-calling tasks, and has 13 billion parameters so it can be hosted locally.
What you’ll explore:
The skills you’ll learn in this course will allow you to build advanced AI agents and assistants that can process and analyze customer feedback, automate data entry and content management workflows, enhance search and recommendation systems with structured data, and many other real-world applications.
This course is designed for anyone interested in using function-calling capabilities with large language models to interface with external tools and extract structured data. Familiarity with LLMs and basic Python knowledge are recommended.
Introduction
What is function calling
Function calling variations
Interfacing with external tools
Structured Extraction
Applications
Course project dialog processing
Conclusion
Co-founder & CEO of Nexusflow and Assistant Professor of EECS and Statistics at UC Berkeley
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