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Fine tuning with OpenAI

41 min of on-demand video

Created by
Seth Kramer
Fine tuning with OpenAI

41 min of on-demand video

Created by
Seth Kramer


What's inside this tutorial?

This course will teach  how to fine-tune pre-trained OpenAI models for specific tasks and use cases. Students will learn how to select the appropriate model architecture and how to prepare and format data for fine-tuning.

They will also learn how to train and evaluate the fine-tuned model, as well as techniques for troubleshooting and improving performance.

By the end of the course, students will have the skills and knowledge necessary to fine-tune OpenAI models for a variety of natural language processing tasks such as text classification, language translation, and text generation.

Here's what you can expect:

  • Learn use-cases for fine-tuning
  • Learn best practices for creating your own model
  • The process of how fine-tuning works
  • How to create fine-tuning datasets
  • How to create your own fine-tuned AI model

Who is this for?

  • Entrepreneurs
  • Marketers
  • Data Scientists
  • Anyone interested in using the state-of-the-art language models for their projects
Get full access with a 60-day money-back guarantee

About the instructor

Seth Kramer

Hi! I'm Seth, the founder and lead instructor at No Code MBA.

I've built massive no-code automations for venture-backed startups, beautiful Webflow-powered websites (including one for a celebrity chef), and bootstrapped a 7-figure marketplace business.

No-code tools have helped me reach my entrepreneurial dreams without writing a line of code, and I'm thrilled to share everything I've learned with you through No Code MBA courses.


How fine tuning works
JSONL file format and the costs of training a model
Best practices and case studies
Create your own fine tune with Riku
Creating your own JSONL file
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