# Introduction

![Bonus Unit 1 Thumbnail](https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/bonus-unit1/thumbnail.jpg)

Welcome to this first **Bonus Unit**, where you'll learn to **fine-tune a Large Language Model (LLM) for function calling**.

In terms of LLMs, function calling is quickly becoming a *must-know* technique. 

The idea is, rather than relying only on prompt-based approaches like we did in Unit 1, function calling trains your model to **take actions and interpret observations during the training phase**, making your AI more robust.

> **When should I do this Bonus Unit?**
>
> This section is **optional** and is more advanced than Unit 1, so don't hesitate to either do this unit now or revisit it when your knowledge has improved thanks to this course. 
>  
> But don't worry, this Bonus Unit is designed to have all the information you need, so we'll walk you through every core concept of fine-tuning a model for function-calling even if you haven’t learned yet the inner workings of fine-tuning.

The best way for you to be able to follow this Bonus Unit is:

1. Know how to Fine-Tune an LLM with Transformers, if it's not the case [check this](https://huggingface.co/learn/nlp-course/chapter3/1?fw=pt).

2. Know how to use `SFTTrainer` to fine-tune our model, to learn more about it [check this documentation](https://huggingface.co/learn/nlp-course/en/chapter11/1). 

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## What You’ll Learn

1. **Function Calling**  
   How modern LLMs structure their conversations effectively letting them trigger **Tools**.

2. **LoRA (Low-Rank Adaptation)**  
   A **lightweight and efficient** fine-tuning method that cuts down on computational and storage overhead. LoRA makes training large models *faster, cheaper, and easier* to deploy.

3. **The Thought → Act → Observe Cycle** in Function Calling models  
   A simple but powerful approach for structuring how your model decides when (and how) to call functions, track intermediate steps, and interpret the results from external Tools or APIs.

4. **New Special Tokens**  
   We’ll introduce **special markers** that help the model distinguish between:
   - Internal “chain-of-thought” reasoning  
   - Outgoing function calls  
   - Responses coming back from external tools

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By the end of this bonus unit, you’ll be able to:

- **Understand** the inner working of APIs when it comes to Tools.  
- **Fine-tune** a model using the LoRA technique.  
- **Implement** and **modify** the Thought → Act → Observe cycle to create robust and maintainable Function-calling workflows.  
- **Design and utilize** special tokens to seamlessly separate the model’s internal reasoning from its external actions.

And you'll **have fine-tuned your own model to do function calling.** 🔥

Let’s dive into **function calling**!