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Ludwig is a low-code framework designed for creating custom AI models such as LLMs and deep neural networks. Its main features include:
- Easy custom model creation using a YAML configuration file, supporting multi-task and multi-modality learning with thorough configuration validation.
- Designed for large-scale efficiency with features like automatic batch size selection, distributed training options, parameter efficient fine-tuning, 4-bit quantization, and support for large datasets.
- Provides expert-level control over models, including hyperparameter optimization, explainability, and detailed metric visualizations.
- Offers modularity and extensibility, allowing experimentation with various model architectures, tasks, and features with minimal configuration changes.
- Built for production use with features like prebuilt Docker containers, compatibility with Ray on Kubernetes, model exporting options, and easy uploading to HuggingFace. Lastly, Ludwig is supported by the Linux Foundation AI & Data.
In this tutorial, I will show you how to use ludwig and train a custom LLM.
Ready ?
Let’s dive in.
Ludwig is compatible with datasets that have a tabular structure, where each feature is represented in its own column and each instance occupies a row.
For illustration, we’ll work with the Rotten Tomatoes dataset. This CSV file contains diverse feature types and a binary target.
Now, let’s preview the top 5 rows to understand the data’s layout:
To use Ludwig for model training, we must first set up a Ludwig configuration. This configuration encompasses details like input and output features, preprocessing steps, model structure, training process, hyperparameter exploration, and backend settings, ensuring a comprehensive model…