Fine Tuning LLMs for Function Calling
fine-tuning
llm-conf-2024
Abstract
In this talk, we will go through the process and best practices of fine-tuning an LLM for function/tool use. We will discuss topics like data preparation, objective-based tuning, efficient serving, and evaluation.
This talk was given by Pawel Garbacki at the Mastering LLMs Conference.
Chapters
00:00 Introduction and Background
00:29 Functional Tool Calling Overview
02:23 Single-Turn First Call Objective
02:51 Forced Call Explanation
03:28 Parallel Function Calling
04:00 Nested Calls Explanation
06:24 Multi-Turn Chat Use Case
13:54 Selecting Function Call Syntax
17:44 Full Weight Tuning vs. LoRa Tuning
19:19 Efficient LoRa Serving
23:06 Constrained Generation
26:21 Generic Function Calling Models
40:02 Q&A