Ludwig
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About
Ludwig is a low-code, declarative deep learning framework for building AI models that simplifies the model training process. It allows users to define their models using a simple YAML configuration file and offers support for various machine learning tasks including supervised learning and large language models fine-tuning. With features such as automatic batch size selection, distributed training, and hyperparameter optimization, Ludwig is optimized for performance and scalability. It supports diverse data types, allowing multi-task and multi-modality learning without requiring extensive coding knowledge. Ludwig models can be easily exported for deployment in production environments, making it a robust choice for AI development.
Platform
Task
Features
• declarative yaml configuration
• distributed training
• hyperparameter optimization
• rich metric visualizations
• model exporting capabilities
• multi-task and multi-modality learning
• support for large language model fine-tuning
• automatic batch size selection
FAQs
What is Ludwig?
Ludwig is a framework for building AI models like low-code custom LLMs, designed for efficiency and scalability.
Can I use Ludwig for my project?
Yes! Ludwig models can be used in projects, and improvements can be submitted back to the open-source community.
Can I use Ludwig models in production?
Yes, Ludwig models can be exported and served using various frameworks.
Who develops Ludwig?
Ludwig was created by a team at Uber AI and is now maintained as an open-source project supported by the Linux Foundation.
What does Ludwig not support yet?
Currently, Ludwig does not support self-supervised learning, reinforcement learning, or generative models for images and audio.
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