Fine Tuning LLMs for Function Calling w/Pawel Garbacki

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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:

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
  1. Hamel Husain
  2. LLMs
  3. Applied-llms
  4. mastering llms
  5. rag
  6. fine tuning
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