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Fine-tuningFreeOpen Source
LUDWIG
Declarative deep learning framework for LLMs and multimodal models
Apache-2.0
ABOUT
Training and fine-tuning AI models typically requires writing hundreds of lines of PyTorch boilerplate, managing complex data pipelines, and tuning numerous hyperparameters manually. Ludwig eliminates this by letting practitioners define the entire model, training, and evaluation pipeline in a single YAML file. You specify input features, output features, model type, and training parameters declaratively, and Ludwig handles the rest — data preprocessing, model construction, distributed training, and inference — making AI experimentation accessible to both researchers and engineers who want to move fast without sacrificing flexibility.
INSTALL
pip install ludwigINTEGRATION GUIDE
1. Fine-tuning Llama, Mistral, or other open LLMs with LoRA using a single YAML config
2. Building multimodal classification models that combine text, image, and tabular inputs
3. Rapidly prototyping tabular deep learning and time-series forecasting pipelines
TAGS
pythonpytorchllmfine-tuningdeclarativeopen-source