Complete-LLM's-Fine-Tuning

A comprehensive guide and codebase for fine-tuning Large Language Models (LLMs). This repository provides step-by-step tutorials, example scripts, and best practices for fine-tuning LLMs using popular frameworks like PyTorch and Hugging Face Transformers.

Important Notes

• This repository is designed to be beginner-friendly, with clear explanations and practical examples.
• It is suitable for those who are new to LLM fine-tuning or looking to enhance their skills in this area.
• The tutorials cover various aspects of fine-tuning, including dataset preparation, model training, and evaluation.
• The code is structured to be easily understandable and modifiable for different use cases.

Table of Contents

As you explore the repository, you'll find the following key topics covered:

• Prompting vs. Fine-Tuning: Understanding the differences and when to use each approach.
• Introduction to Fine-Tuning: Understanding the basics and benefits of fine-tuning LLMs.
• Approach to Fine-Tuning: Different strategies and methodologies for fine-tuning.
• Benefits of Fine-Tuning: How fine-tuning can improve model performance and adaptability.
• Evaluating Fine-Tuned Models: Techniques for assessing the performance of fine-tuned models.
• Types of Fine-Tuning: Overview of various fine-tuning methods, including LoRA and PEFT.
• First-Time Fine-Tuning: A beginner's guide to starting with fine-tuning.
• Data Preparation for Fine-Tuning: Best practices for preparing datasets for fine-tuning.
• LoRA and PEFT: Introduction to Low-Rank Adaptation (LoRA) and Parameter-Efficient Fine-Tuning (PEFT).
• Preparation of Data for Fine-Tuning: Steps to prepare your data effectively.

Features

• Step-by-step instructions for LLM fine-tuning
• Example scripts for popular frameworks (PyTorch, Hugging Face Transformers)
• Best practices for dataset preparation and evaluation
• Tips for optimizing training and reducing costs

Getting Started

1. Clone the repository:

   git clone https://github.com/yourusername/Complete-LLM-Fine-Tuning.git
   cd Complete-LLM-Fine-Tuning

2. Follow the tutorials in the notebooks/ directory.

Folder Structure

• notebooks/ — Interactive tutorials and walkthroughs
• assets/ — Images and diagrams used in the tutorials

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.