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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).

• 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.

1. Clone the repository:

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

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

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

This project is licensed under theMIT License.

For questions or inquiries, feel free to contact me onLinkedin.

Happy fine-tuning!!

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