Google CodeGemma LLM Model with 4-bit Configuration

This Jupyter Notebook serves as a demonstration of the new Google CodeGemma LLM (Large Language Model) with a 4-bit configuration. The Google CodeGemma model represents the latest advancement in language understanding technology, offering enhanced performance and efficiency.

Overview

This notebook provides users with an opportunity to explore the capabilities of the Google CodeGemma LLM model configured with 4 bits. With this configuration, the model achieves a balance between model size and computational efficiency while maintaining high performance in various natural language processing tasks.

Features

• Demonstrates the usage of the Google CodeGemma LLM model.
• Utilizes a 4-bit configuration for optimized performance and efficiency.
• Allows users to experiment with text generation, completion, and other NLP tasks.

Usage

To use this notebook:

1. Ensure you have Jupyter Notebook installed in your environment.
2. Clone or download this repository to your local machine.
3. Open the Jupyter Notebook in your preferred environment.
4. Follow the instructions and run the code cells sequentially to explore the capabilities of the Google CodeGemma LLM model.

Requirements

• Python 3.11 (This is the Version that I used)
• Jupyter Notebook
• Google CodeGemma LLM Model (Note: The model may require additional setup or installation steps, please refer to the official documentation for details.)

Instructions

1. Setup Environment: Ensure that you have all the necessary dependencies installed, including the Google CodeGemma LLM model.
2. Run Cells: Execute each code cell sequentially to observe the model's behavior and output.
3. Experiment: Feel free to modify the input text, parameters, or experiment with different tasks to fully explore the capabilities of the Google CodeGemma LLM model with 4-bit configuration.
4. Evaluate Results: Analyze the generated outputs and evaluate the model's performance for your specific use case.