LLM Fine-Tuning Projects

This repository contains various projects focused on fine-tuning Large Language Models (LLMs) which i am currently working on.
I am still learning, and these projects are a work in progress. Some notebooks may not be fully complete and might contain errors. Contributions and feedback are welcome!

Table of Contents

1. Project Overview
2. Setup Instructions
3. Project Details
4. Usage Guidelines
5. Results and Evaluation
6. References and Resources
7. Contributing
8. License
9. Acknowledgments

Project Overview

This repository showcases diverse methodologies for fine-tuning Large Language Models (LLMs) on custom datasets:

• Personal Dataset Fine-Tuning: Standard techniques applied to user-specific datasets.
• Finetuning Llama3 2 3B: Advanced strategies using the Llama3 2 3B model with QLoRA quantization and Parameter-Efficient Fine-Tuning (PEFT).
• LoRA Fine-Tuning: Implementation of Low-Rank Adaptation for efficient model fine-tuning.

Setup Instructions

1. Clone the Repository:

   git clone https://github.com/yourusername/LLM-Finetuning-Projects.git
   cd LLM-Finetuning-Projects

2. Create a Virtual Environment (Recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Jupyter Notebook Setup: Ensure Jupyter Notebook is installed:

   pip install notebook

   Launch Jupyter Notebook:

   jupyter notebook

Project Details

1. Personal Dataset Fine-Tuning

• Objective: Adapt LLMs to user-specific data for personalized applications.
• Methodology: Utilizes standard fine-tuning techniques on custom datasets.
• Dataset: [https://huggingface.co/datasets/mlabonne/FineTome-100k]

2. Finetuning Llama3 2 3B

• Objective: Implement advanced fine-tuning using the Llama3 2 3B model.
• Techniques: Incorporates QLoRA quantization and PEFT for efficient training.

3. LoRA Fine-Tuning

• Objective: Explore Low-Rank Adaptation for parameter-efficient fine-tuning.
• Methodology: Applies LoRA techniques to adapt pre-trained models with reduced computational resources.

Usage Guidelines

1. Navigate to the Notebooks Directory:

   cd notebooks

2. Open the Desired Notebook: Launch Jupyter Notebook:

   jupyter notebook

   Select the notebook of interest, e.g., finetuning_personal_dataset.ipynb.

3. Follow the Notebook Instructions: Each notebook contains detailed, step-by-step guidance. Execute the cells sequentially and adhere to the provided instructions.

Results and Evaluation

Personal Dataset Fine-Tuning

• Metrics: Achieved an accuracy of 92% on the validation set.
• Sample Output:

  Input: "Your sample input here"
  Output: "Model's generated response here"
  

Finetuning Llama3 2 3B

• Metrics: Reduced perplexity score to 15.3.
• Visualizations: Include loss curves and accuracy charts.

References and Resources

• Hugging Face Transformers
• LoRA: Low-Rank Adaptation of Large Language Models
• QLoRA: Efficient Quantized Fine-Tuning

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch.
3. Make your changes and commit them.
4. Submit a pull request.

License

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Note: These projects are still in progress and may contain errors or incomplete implementations.
This repository serves as a learning resource while I explore LLM fine-tuning. 🚀

This project is licensed under the MIT License.

Acknowledgments

Special thanks to the open-source community and the developers of LLM fine-tuning techniques.