Welcome to the official repository for the book "Mastering Fine-Tuning with Databricks: Five Hands-On Use Cases."

This book provides practical, hands-on examples of fine-tuning large language models (LLMs) using Databricks. It covers five common use cases, offering detailed, end-to-end implementations to help you apply fine-tuning techniques effectively in real-world scenarios. Whether you are a data scientist, machine learning engineer, or AI enthusiast, this book will guide you through the process of fine-tuning LLMs on Databricks with ease.

This repository contains the following resources to complement the book:

• Datasets: Sample datasets used in the book's examples.
• Databricks Notebooks: Databricks notebooks with step-by-step code implementations for each use case.
• Scripts: Python scripts for various fine-tuning tasks.
• Documentation: Additional documentation and resources for further learning.
• Examples: End-to-end examples demonstrating the practical application of fine-tuning techniques.

1. Customer Support Chatbots: Fine-tuning models to handle customer queries and provide support using Databricks notebooks.
2. Sentiment Analysis and Feedback Categorization: Analyzing customer feedback and categorizing sentiment with practical implementations.
3. Content Generation and Summarization: Generating and summarizing content using LLMs on the Databricks platform.
4. Personalized Recommendations: Providing personalized product or content recommendations through fine-tuned models.
5. Translation and Language Services: High-quality translation and language support using fine-tuned models in Databricks.

1. Domain-Specific Knowledge Enhancement: Continued pre-training on domain-specific literature such as medical research papers or legal documents using Databricks.
2. Code and Programming Assistance: Continued pre-training on code repositories and programming documentation to enhance the model's capability in providing coding assistance and generating code snippets.

To get started with the examples in this repository:

1. Clone the repository:

   git clone https://github.com/username/mastering-fine-tuning-databricks.git

2. Import the relevant Databricks notebooks into your Databricks workspace.
3. Follow the instructions in the README files to set up your environment and run the notebooks.

We welcome contributions to improve the examples and documentation. If you find any issues or have suggestions, please open an issue or submit a pull request.

This repository is licensed under the MIT License. See the LICENSE file for more details.