Finetune-Open-Source-LLMs-on-Custom-Data

Project Overview

The project aims to showcase the process of fine-tuning LLMs on industry-specific data. The provided notebook (FinetuneOpenSourceLLMs.ipynb) walks through the steps, utilizing Amazon's sales data (ConvAI_Data.csv).

Files

• Input Data: ConvAI_Data.csv

  • This CSV file contains the E-commerce data used for fine-tuning.

• Python Notebook: FinetuneOpenSourceLLMs.ipynb

  • Jupyter notebook providing a step-by-step guide on how to fine-tune open-source LLMs on custom data.

Usage

1. Clone the repository:

   git clone https://github.com/Praveen76/Finetune-Open-Source-LLMs-on-Custom-Data.git
   cd Finetune-Open-Source-LLMs-on-Custom-Data

2. Open and run the Jupyter notebook:

   jupyter notebook FinetuneOpenSourceLLMs.ipynb

3. Follow the instructions in the notebook to understand and apply fine-tuning on open-source LLMs.

Contributing

If you have a Data Science mini-project that you'd like to share, please follow the guidelines in CONTRIBUTING.md.

Code of Conduct

Please adhere to our Code of Conduct in all your interactions with the project.

License

This project is licensed under the MIT License.

Contact

For questions or inquiries, feel free to contact me on Linkedin.

Happy fine-tuning!!

About Me:

I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.