Fine Tuning With Open AI API Theory | Complete OpenAI API GPT Python Tutorial - Part 7

Fine-tuning involves adapting pre-trained models to specific tasks or datasets to enhance their performance on those tasks. By leveraging the foundational knowledge of a pre-trained model, fine-tuning allows you to achieve better accuracy and efficiency for specialized applications. This video provides an in-depth exploration of the theory behind fine-tuning using OpenAI's API, along with practical insights and examples. Summary: Understand the theory behind fine-tuning OpenAI's models. Learn the difference between fine-tuning and other techniques like LoRA. Discover the benefits and use cases for fine-tuning. Explore the format and requirements for training data. Get insights into cost calculation and best practices for fine-tuning. Timestamp: 00:00 Introduction 00:35 Overview of the official OpenAI documentation 01:37 Benefits of fine-tuning 03:05 When to use fine-tuning 05:01 Iterating over the feedback loop 06:24 Training data format 08:24 Example of fine-tuning data 12:14 Checking token limits 13:11 Token count and cost calculation 17:21 FAQs on fine-tuning vs. RAG 19:48 Continuous training and model updates 21:12 Next tutorial preview Other courses: RAG LLMOps in GCP - Deploying a Retrieval Augmented Generation LLM in GCP infrastructure project: https://youtu.be/39PGfKA50As Source code for these tutorials: https://github.com/Sahilvohra58/open_ai_api_tutorials If you found this video helpful, please give it a thumbs up 👍, subscribe to our channel 🔔, and leave a comment below if you have any questions or topics you want us to cover next! Follow Us: YouTube: https://www.youtube.com/@sahilvohra8892 LinkedIn: https://www.linkedin.com/in/sahil-vohra/ Github: https://github.com/Sahilvohra58/ Thank you for watching, and happy coding! 🚀