This repository contains code for fine-tuning the Llama3 8B model and implementing Retrieval-Augmented Generation (RAG) on the Kaggle platform. Additionally, it includes work with the Gemma2 model, which has 9 billion parameters.

Llama3-8B-Finetune-and-RAG focuses on fine-tuning the Llama3 model and utilizing RAG for enhanced performance in various tasks. The implementation leverages Kaggle's computational resources and provides Jupyter notebooks for easy replication and adaptation.

Llama3 8B is a powerful language model developed by Meta, containing 8 billion parameters. It is designed to understand and generate human-like text, making it useful for a wide range of natural language processing tasks.

RAG is a technique that combines retrieval-based and generative models to produce more accurate and contextually relevant text. It retrieves relevant documents from a knowledge base and uses this information to generate responses, improving the quality and relevance of the output.

Semantic caching is a technique used to store and reuse the results of previous queries to improve the efficiency of data retrieval. In the context of RAG, it helps in quickly accessing relevant information without the need to fetch it repeatedly from the knowledge base, thereby speeding up the generation process.

Gemma2 9B is another advanced language model included in this repository. It has 9 billion parameters, providing even greater capability for understanding and generating text. The inclusion of Gemma2 offers additional options for fine-tuning and implementing RAG.

• Fine-tuning Llama3 8B model.
• Implementing RAG for improved generation tasks.
• Semantic caching for efficient data retrieval.
• Sample code and notebooks for experimentation.
• Integration with Gemma2 9B model for enhanced performance.

Clone the repository:

git clone https://github.com/Hemanthkumar2112/Llama3-8B-Finetune-and-RAG.git

1. Navigate to the repository directory.
2. Open the Jupyter notebooks and follow the instructions provided.

• meta-llama-3-8b.ipynb: Notebook for initial setup and configuration.
• meta-llama-3_fine_tune_with_ORPO.ipynb: Notebook for fine-tuning using ORPO.
• meta-llama3-8b-fine-tuning.ipynb: General fine-tuning notebook.
• tamil_llama3-SFT_test_existing_tokenizer.ipynb: Notebook for testing the existing tokenizer.
• gemma2-9b.ipynb: Notebook for working with the Gemma2 9B model.

This project is licensed under the Apache-2.0 License. See the LICENSE file for details.

Contributions are welcome. Please fork the repository and create a pull request with your changes.

For any questions or issues, please open an issue on GitHub.