Google Gemma Fine Tuning: How to Teach a Large Language Model?

👋 Welcome to our deep dive into fine-tuning the Google Gemma large language model! In today's video, we're embarking on a journey to customize Gemma using a unique JSONL dataset. Whether you're an AI enthusiast or a professional looking to enhance your models, this tutorial is crafted to provide you with a comprehensive understanding and hands-on experience. 🚀 What We Cover: Loading the Google Gemma language model. Understanding and preparing your custom JSONL data. The step-by-step process to fine-tune Gemma using a 2 billion parameter model. Practical examples of before and after fine-tuning results. Essential installations and configurations for a successful fine-tuning. 💡 Why This Matters: Fine-tuning allows you to tailor AI responses to specific instructions or data, significantly improving interaction quality and relevance. By following this guide, you'll learn how to make the model respond accurately to unique prompts, enhancing your AI projects. 🛠️ Tools & Techniques Featured: TensorFlow, Keras, Keras NLP, and TensorRT installations. Utilising the Databricks Dolly dataset for instruction-based fine-tuning. Optimising model performance with strategic coding practices. 🔗 Resources & Links: All necessary code snippets, installation commands, and dataset links are provided in the video description below for easy access and reference. 👍 If you find this video helpful, make sure to hit the like button, share with others who might benefit, and subscribe for more AI-focused content. Your support helps us create more helpful guides like this one! Timestamps: 0:00 - Introduction to Fine-Tuning Google Gemma 0:33 - Overview of Google Gemma and the Fine-Tuning Process 1:29 - Setting Up Your Environment for Fine-Tuning 2:01 - Downloading and Preparing the Databricks Dolly Dataset 3:01 - Instruction-Based Fine-Tuning Explained 4:00 - Step-by-Step Fine-Tuning with Custom JSONL Data 5:02 - Before and After Comparison: Understanding the Impact 6:01 - Final Thought