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ludwig

Free

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

Model APIsFreeFree tier
Inputs: text, image, audioOutputs: text, image, audio
Type
Open Source
Visit Website GitHub (11.7k stars)

About ludwig

Ludwig is a powerful open-source tool for quickly and easily building, training, and deploying deep learning models. It allows users to efficiently create deep learning architectures from scratch, or use pre-trained models to solve their own problems. Ludwig’s simple and intuitive interface makes it easy for users of all levels to quickly get up and running. With Ludwig, users can experiment with different data sets and architectures without having to write complex code. Ludwig also provides state-of-the-art results with minimal tuning, making it an ideal tool for both experts and beginners. Its scalability and robustness make it suitable for large and small projects alike, and its efficient distributed architecture ensures that models can be trained quickly and efficiently. Ludwig is an excellent choice for anyone looking to get started with deep learning, or to take their projects to the next level. It’s an invaluable resource for data scientists, analysts, and researchers who want to quickly and accurately build models that meet their goals.

Key Features

Quickly build and deploy deep learning models.
Easily create architectures from scratch or use pre-trained models.
Experiment with data sets and architectures without writing code.

Pros & Cons

Pros
  • Eliminates boilerplate infrastructure code – focus on model config, not training loops.
  • Supports a wide range of data modalities (text, image, audio, tabular, time series, geospatial, vectors) in one framework.
  • Seamless scaling from single machine to distributed clusters via Ray integration.
  • Production-ready serving with one command and export to multiple formats (ONNX, SafeTensors).
  • Deep integration with HuggingFace models and the PyTorch ecosystem.
Cons
  • Built exclusively on PyTorch, limiting compatibility with TensorFlow or JAX.
  • Declarative YAML approach may be less intuitive for users who prefer imperative coding.
  • Documentation and community resources are still maturing compared to established frameworks.

Best For

Quickly build and deploy deep learning models.Easily create architectures from scratch or use pre-trained models.Experiment with data sets and architectures without writing code.

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FAQ

What is Ludwig?
Ludwig is an open-source declarative deep learning framework that allows users to build, fine-tune, and deploy custom models using YAML configuration, without writing training loops.
What data types does Ludwig support?
Ludwig supports tabular, text, image, audio, time series, geospatial, and vector data types.
Does Ludwig support distributed training?
Yes, Ludwig can scale from local to distributed clusters using Ray, DeepSpeed, FSDP, and KubeRay without code changes.
Can I fine-tune LLMs with Ludwig?
Yes, Ludwig includes a fine-tuning suite supporting SFT, DPO, KTO, ORPO, GRPO, and various adapter methods like LoRA, QLoRA, DoRA, VeRA with 4-bit quantization.
How do I serve a model trained with Ludwig?
You can serve your model as a REST API with a single command, and export to SafeTensors, ONNX, or torch.export. Prebuilt Docker images are available.