Finetuning Phi-2 Model | QLORA | Preprocessing of dataset | Custom Dataset

Unlock the Potential of PHI-2: A Complete Finetuning Guide with QLORA

Dive deep into the art of finetuning the Microsoft PHI-2 language model with our comprehensive tutorial. In this video, we break down the concept of finetuning, providing viewers with a clear understanding of its importance and the intricacies involved. We cover every angle, from the basics to the most advanced techniques, including:

- 🛠️ **Finetuning Explained**: Grasp the fundamentals of finetuning and why it's crucial for customizing LLMs for specific tasks.
- 🗝️ **Key Steps in Finetuning**: Follow our detailed guide through the critical stages of the finetuning process, ensuring you know what to expect at every turn.
- 🔄 **Types of Finetuning**: Discover the differences between Full Finetuning (Full FT) and Prompt Engineering Finetuning (PEFT), and learn which approach suits your needs.
- 📊 **Evaluation Metrics**: Learn how to objectively assess your finetuned model's performance using the ROUGE metric, ensuring your results are both reliable and impressive.
- 💻 **Google Colab Practical**: Join us for a step-by-step walkthrough in Google Colab, where we put theory into practice, demonstrating the finetuning process in real-time.

👨‍💻 Stay tuned and follow along as we fine-tune our way to AI excellence!

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https://github.com/sainathpawar/FineT...