In this article, you will learn how to fine-tune Llama 3 8B for custom tool calling using Unsloth and QLoRA, so that the model reliably outputs structured JSON payloads for a specific API schema.

Topics we will cover include:

Why prompt engineering alone is insufficient for reliable tool calling, and how fine-tuning with QLoRA addresses the problem at the weight level.

How to build and format a tool-calling dataset using Llama 3’s chat template, and what makes a high-quality training example.

How to train, test, and save a fine-tuned tool-calling adapter that runs on a free Google Colab T4 GPU in under 10 minutes.

How to Fine-Tune Llama 3 for Custom Tool Calling with Unsloth in Python

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Introduction

Llama 3 is a capable generalist, but that’s exactly the problem when you need an agent that reliably outputs structured JSON for a specific API.…