Ludwig
Low-code framework for building custom LLMs and AI models
Founded
2019
Starting Price
Free
Category
Developer Tools
Last updated March 10, 2026
About Ludwig
Ludwig is a declarative machine learning framework that lets you build, train, and fine-tune custom LLMs, neural networks, and other AI models using simple YAML configuration. Originally developed at Uber, it supports multi-modal learning, distributed training, and integrates with Hugging Face models.
Pros & Cons
Pros
- Dramatically reduces code needed to build ML models with declarative approach
- Excellent LLM fine-tuning with PEFT and quantization support
- Strong multi-modal capabilities for mixing data types
- Production-ready with Docker containers and Kubernetes support
- Backed by Predibase with active development and community
Cons
Key Features
Declarative YAML Config
Define model architecture, training, and evaluation with simple YAML instead of writing code
LLM Fine-Tuning
Fine-tune state-of-the-art large language models on your own data with parameter-efficient methods like LoRA and QLoRA
Multi-Modal Learning
Mix and match tabular data, text, images, and audio into complex model configurations
Distributed Training
Scale training with DDP, DeepSpeed, and Ray on Kubernetes for large datasets
Hugging Face Integration
Natively use any pre-trained model from the Hugging Face Transformers library
AutoML Features
Automatic batch size selection, hyperparameter optimization, and data preprocessing
Production Export
Export models to TorchScript and Triton, or upload to Hugging Face with one command
Pricing
Open Source
Free
- Full ML framework
- LLM fine-tuning
- Multi-modal support
- Distributed training
- Hugging Face integration
Best For
LLM Fine-Tuning
Fine-tune large language models on domain-specific data for better task performance
Multi-Modal Classification
Build models that combine text, images, and structured data for complex classification tasks
Rapid ML Prototyping
Quickly experiment with different model architectures and hyperparameters using YAML config changes
Production ML Pipelines
Deploy trained models to production with TorchScript export and Docker containers
