Fine Tuning LLMs for Function Calling w/Pawel Garbacki

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.
More resources are available here:
parlance-labs.com/education/f...
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