💡 Hands-on examples are the best way to learn about a new framework.

Here, you'll find several examples of how to build models with Ludwig for a variety of tasks with LLMs, neural networks, and tree-based models. We provide sample datasets, commands, scripts, and colab notebooks. Please reach out if you have any questions!

💬 The LLMs section gives a broad overview of the full breadth of Ludwig's LLM capabilities like zero-shot batch inference and fine-tuning Llama-2-7b.

🎯 The Supervised ML section has in-depth tutorials for how to use Ludwig's command line interface and Python API for machine learning in a supervised setting. Check out Image Classification on MNIST.

🏛️ The Use Cases section illustrates how Ludwig can be applied to a variety of machine learning tasks, such as, natural language understanding, timeseries forcasting, multi-label classification to name just a few. Read about Ludwig models for Sentiment Analysis.

Have fun exploring!