📚 LLM Adaptation Techniques – Colab Notebooks

This repository contains hands-on demonstrations of Large Language Model (LLM) adaptation techniques implemented using Google Colab notebooks.

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🚀 Contents

📓 Notebooks

• 🔗 Finetuning.ipynb
  Demonstrates full model fine-tuning on downstream tasks.

• 🔗 Transfer_learning.ipynb
  Covers transfer learning concepts and practical implementation.

• 🔗 Prompt_engineering.ipynb
  Explores prompt design strategies for improving LLM outputs.

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📄 Notes

• 📘 Notes_prompt_engineering_finetuning.pdf
• 📘 Notes_transfer_learning.pdf

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📌 How to Use

• Click on any .ipynb file above to open it.
• Run the notebooks directly in Google Colab.
• Follow step-by-step cells for practical understanding.

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📘 Reference

This work is based on:

Natural Language Processing with Transformers
Lewis Tunstall, Leandro von Werra, Thomas Wolf
O’Reilly Media

https://www.oreilly.com/library/view/natural-language-processing/9781098136789/

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⚙️ Requirements

• Google Colab (recommended)
• Python 3.x
• Hugging Face Transformers
• PyTorch

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🎯 Objective

To provide a practical and intuitive understanding of:

• Fine-tuning
• Transfer learning
• Prompt engineering

for modern LLM applications.

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