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ludwig-ai/ludwig

A low-code, declarative framework for building and deploying custom large language models (LLMs) and other deep neural networks with ease and efficiency.

Core Features

Declarative YAML configuration for custom model building.

Optimized for scale and efficiency with distributed training and PEFT.

Expert-level control over model architectures and hyperparameters.

Modular and extensible design for experimentation.

Production-ready features including Docker, Ray, and model export.

Quick Start

pip install ludwig

Detailed Introduction

Ludwig is a powerful low-code deep learning framework designed to simplify the creation and deployment of custom AI models, including LLMs and neural networks. By leveraging a declarative YAML configuration, users can easily train state-of-the-art models on their data without extensive coding. It emphasizes scalability and efficiency through features like distributed training, parameter-efficient fine-tuning (PEFT), and quantization. Ludwig offers both ease of use for beginners and granular control for experts, making it a versatile tool for AI development, from rapid prototyping to production-grade deployments. It's hosted by the Linux Foundation AI & Data.