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 case…
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Chapters (12)
Introduction
0:35
Overview of the official OpenAI documentation
1:37
Benefits of fine-tuning
3:05
When to use fine-tuning
5:01
Iterating over the feedback loop
6:24
Training data format
8:24
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
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