Ludwig
Low-code framework for building custom LLMs and AI models
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
- Steeper learning curve for users unfamiliar with ML concepts
- Requires understanding of model architectures for advanced configurations
- No visual/GUI interface — requires YAML configuration
- Limited compared to full ML platforms for experiment management
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
Extensible Architecture
Swap encoders, decoders, and other components; extend with custom PyTorch modules
Pricing
- Full ML framework
- LLM fine-tuning
- Multi-modal support
- Distributed training
- Hugging Face integration
- Apache 2.0 license
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
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