Get ready to dive deep into the world of fine-tuning in this comprehensive series! In this exciting first part, we'll unravel the mystery behind foundation models and their significance. Discover the different types of foundation models and grasp the crucial distinctions between pre-training, continued pre-training, and fine-tuning.

We'll lay a solid groundwork for your understanding before we get our hands dirty with actual fine-tuning in the next video. Join us on this incredible journey to becoming an API badass! Like, comment, and subscribe to stay tuned for more game-changing content!

Chapters:
00:00 Intro
00:50 What We Will Cover
01:46 Understanding Pre-Training
01:57 The Big Picture
02:47 What is Pre-Training?
05:04 Foundation Models (2021 Paper)
06:29 Foundation Model Diagram
08:02 Pre-Training Not Available
09:16 Continued Pre-Training
16:21 Understanding Fine-Tuning
16:30 Options For Adapting LLMs
18:18 In-Context Learning
20:02 Fine-Tuning In More Detail
21:51 Parameter Efficient Fine Tuning
24:19 The Fine-Tuning Process
26:27 Example Code
29:26 Conclusion

Links:
https://github.com/ReallyEasyAI/Fine-Tuning

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