OpenAI API: Fine-Tuning Models, Part 4 - Train / Test Split & Creating Your First Fine-Tuning Jobs

Unleash the full potential of OpenAI models! Join me in this comprehensive guide to fine-tuning, where we'll break down every step of the process, from understanding the fundamentals to submitting and analyzing fine-tuning jobs.

We'll cover: preparing training data, splitting into train/test sets, navigating the OpenAI API, setting hyperparameters, and more.

Whether you're new to AI or looking to enhance your skills, this hands-on tutorial will empower you to create powerful fine-tuned 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 Accessing Powerful Models
09:54 Creating Your First Fine-Tuning Job (UI)
11:16 Uploading Training & Test Files
12:15 Creating a Simple Fine-Tuning Job (Code)
13:32 Submitting Your First Fine-Tuning Job
14:47 Rate Limits and Fine-Tuning
15:20 Hyperparameter Tuning Options
17:38 Learning Rate Multiplier and Epochs
19:16 Importance of Naming Conventions
21:46 Creating a Fine-Tuning Job (UI Demo)
23:09 Uploading Training & Validation Data
23:40 Naming Your Model
24:56 Submitting Your Fine-Tuning Job
27:07 Confirming Job Submission and Results
27:30 Navigating the Fine-Tuning Dashboard
28:30 Rate Limits: What to Expect

Links:

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Curious on why use vs code versus any other options to do this in and what options are there to do this in?