Fine-tuning GPT-3.5 Turbo allows you to customize the model to your specific use case, improving performance on specialized tasks, achieving top-tier performance, enhancing steerability, and ensuring consistent output formatting.
In this crash course, you'll learn:
What fine-tuning is and why it's important for GPT-3.5 Turbo
How to fine-tune GPT-3.5 Turbo for specific use cases
Best practices for fine-tuning GPT-3.5 Turbo
How to deploy your fine-tuned GPT-3.5 Turbo model
Whether you're a data scientist, machine learning engineer, or business user, this crash course will teach you everything you need to know about fine-tuning GPT-3.5 Turbo to achieve your goals and using a finetuned GPT3.5 Turbo model to solve a realworld problem
Benefits of attending Fine-tune GPT 3.5 Turbo crash course:
1. Customize LLM to your needs – Fine-tuning allows you to customize GPT-3.5 Turbo for your specific use case, improving performance on specialized tasks.
2. Achieve top-tier performance – A fine-tuned GPT-3.5 Turbo can match or even beat GPT-4 on tasks it’s optimized for, without needing the most advanced model.
3. Enhanced steerability – Fine-tuning gives you more control over model behavior, like ensuring responses are in a certain language.
4. Consistent output formatting – For applications requiring specific output, fine-tuning boosts consistency in areas like code completion.
Instructor: Syed Hyder Ali Zaidi, Azure Certified Data Scientist
Hyder is an Azure-Certified Data Scientist at Data Science Dojo. He specializes in working with LLMs (Language Model Models) and computer vision services. Hyder has hands-on experience in developing projects that utilize LLMs for Natural Language Processing (NLP) tasks and applying computer vision techniques for visual data analysis.
Table of Contents:
0:00 – Introduction to GPT 3.5 Turbo
1:07 – What is Fine-Tuning GPT 3.5 Turbo?
7:00 – Why do we need to fine-tune GPT 3.5 Turbo?
10:46 – What are the ways of fine-tuning
19:52 – How to train an LLM - Parameter training methods
25:18 – Supervised fine-tuning steps
26:09 – Hands-on exercise
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