Master LLM Fine-Tuning : Core Technical Concepts, Terminologies, Librari...

🧠 Master LLM Fine-Tuning : Core Technical Concepts, Terminologies, Libraries You MUST Know | Part2

💡 If you’ve already watched Part1 where we covered the functional basics of fine-tuning, this video dives into the core & technical concepts, terminologies & libraries that make the fine-tuning journey more enjoyable and effective—especially inside Google Colab.

In this video, you'll learn:

🎯 What Fine-Tuning really is (not just running code!)

🎯 Briefly covered libraries like transformers, bitsandbytes, peft, trl, accelerate

🎯 How LLMs work and why Tokenization matters

🎯 Supervised Learning, Loss Functions & Optimization

🎯 What PEFT (Parameter-Efficient Fine-Tuning) is

🎯 Why LoRA & QLoRA are game changers in Colab

🎯 Data prep, evaluation metrics, and common pitfalls (like overfitting)

🎯 Quick Overview of HuggingFace & Google Colab

👨‍💻 Whether you're a beginner or intermediate ML enthusiast, these insights will give you a strong foundation to fine-tune your own models using Hugging Face & Google Colab.

📌 Stay tuned for Part3, where we’ll start hands-on code walkthroughs using real datasets, models from Hugging Face along with setting up Google Colab!