Ludwig
A low-code declarative framework for building custom LLMs, neural networks, and other AI models with YAML configurations.
What is Ludwig?
Ludwig is a low-code, declarative deep learning framework designed for building and fine-tuning custom AI models, including large language models (LLMs) and other neural networks. It allows users to define complete model pipelines through YAML configuration files, abstracting away engineering complexity while supporting multi-modal data and scalable distributed training. The framework aims to make state-of-the-art AI accessible without sacrificing control or performance.
Machine learning engineers, data scientists, and researchers who want to rapidly prototype, benchmark, and deploy custom models—especially those working with LLMs, multi-modal data, or requiring production-ready pipelines.
Developers choose Ludwig for its unique balance of low-code simplicity and deep customization, enabling fast experimentation via YAML while retaining full control over model architectures. Its built-in scalability, production tooling, and support for cutting-edge techniques like PEFT and quantization reduce infrastructure overhead.
Overview
Low-code framework for building custom LLMs, neural networks, and other AI models
Use Cases
Best For
- Fine-tuning large language models (LLMs) with parameter-efficient techniques like LoRA
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