Ludwig - Open-source Low-code for Building Custom LLMs AI Models

Ludwig is a self-hosted open-source low-code framework for building custom AI models like LLMs and other deep neural networks.

Features

1- 🛠 Build custom models with ease

A declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures.

2- ⚡ Optimized for scale and efficiency: 

Automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), paged and 8-bit optimizers, and larger-than-memory datasets.

3- 📐 Expert level control:

 Retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations.

4- 🧱 Modular and extensible: 

Experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning.

5- 🚢 Engineered for production:

•  prebuilt Docker containers, native support for running with Ray on Kubernetes, export models to Torchscript and Triton, upload to HuggingFace with one command.

Install

Install from PyPi. Be aware that Ludwig requires Python 3.8+.

pip install ludwig

Or install with all optional dependencies:

pip install ludwig[full]

License

Apache-2.0 License

Resources & Downloads

• Github
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GitHub - ludwig-ai/ludwig: Low-code framework for building custom LLMs, neural networks, and other AI models

Low-code framework for building custom LLMs, neural networks, and other AI models - ludwig-ai/ludwig

GitHubludwig-ai

Ludwig

Declarative machine learning: End-to-end machine learning pipelines using data-driven configurations.

logoPiero Molino