Hello, world! 🌍 Let's talk about the magic of fine-tuning! Ever wanted GPT-3.5 to give you code, precisely how you like it? You're in the right place! We're diving deep into how to fine-tune our friend, GPT-3, to get more precise code outputs. 🚀

1. What's the Goal?
2. Setting Things Up
3. The OG Prompt
4. Gathering Our Training Data
5. Model Upload Time!
6. The Moment of Truth: Testing!

We've got a mission! 🕵️‍♂️ Instead of long, windy prompts, we want to shoot GPT-3 a concise one and get back sweet, sweet code in this format:

[filename.ext]
```code
CODE HERE

Sounds cool, right? 🤩 Let's get started!

<a name="setting-things-up"></a>
## Setting Things Up 🛠️

First things first, let's make sure we've got all our tools ready. 

Install the magic potion with:
```bash
pip install --upgrade openai

And then, let's summon our trusty sidekicks:

import json
import openai
import os
import glob

Remember the OG (original) prompt? Yep, it was a bit of a chatty one! 🙊 We're on a mission to make it shorter, sweeter, and more to the point!

Alright, time to gather our training data! It's like prepping ingredients for a delightful recipe. 🍲 Our example_data folder has got the goodies. We'll use this as our base for training GPT-3.

Quick magic spell to grab all our data files:

files = glob.glob("example_data/*")

And then, it's transformation time! 🦋 We'll get these files ready for training.

Alright, fam! Once we have our data ready, it's time to give GPT-3 a little training session. Imagine it as a mini bootcamp! 🏋️‍♂️ We'll do this at OpenAI's platform: https://platform.openai.com/finetune

Alright! Moment of truth! After that mini bootcamp, it's time to see how well GPT-3's been trained! Let's test our fine-tuned model and see the magic happen! 🌟

Happy coding and fine-tuning! Remember, with every iteration, we get closer to perfection! 🌈🎈🎉

Checkout hugging face, they have over 70,000 datasets that you can use for fine-tuning.

https://huggingface.co/datasets