Unleash the full potential of OpenAI models! Join me for this comprehensive guide to fine-tuning, where we’ll cover every step of the process, from understanding the basics to submitting and analyzing fine-tuning jobs.

We cover topics including: preparing training data, splitting it into training/test sets, navigating the OpenAI API, setting hyperparameters, and more.

Whether you're new to AI or looking to improve your skills, this hands-on tutorial will help you create powerful, sophisticated models. Let's get started!

Chapters:
00:00 Introduction to Fine-Tuning
00:22 Train/Test Split explained
02:11 Benefits of train/test split
03:48 Hands-on demo: creating train/test split
05:51 Creating a Fine-Tuning Job
08:01 Access to powerful models
09:54 Creating your first fine-tuning (UI) job
11:16 Upload training and test files
12:15 Creating a simple fine-tuning job (code)
13:32 Submitting your first fine-tuning job
14:47 Speed limits and fine tuning
15:20 Hyperparameter tuning options
17:38 Learning Speed Multiplier and Epochs
19:16 Importance of Naming Conventions
21:46 Creating a fine-tuning job (UI demo)
23:09 Upload training and validation data
23:40 Naming your model
24:56 Submit your fine-tuning job
27:07 Confirmation of task submission and results
27:30 Navigating the Fine-Tuning Dashboard
28:30 Speed limits: what to expect

Links:
https://github.com/ReallyEasyAI/Fine-Tuning

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