Ludwig
Declarative framework that trains and fine-tunes LLMs, multimodal, and tabular models from YAML configs.
About
Rather than writing training loops, Ludwig users describe a model in YAML: the input and output columns, their data types, optional encoders, and trainer settings, and the framework builds, trains, and evaluates the network from that file. For language models one config can run supervised fine-tuning or DPO, KTO, ORPO, and GRPO alignment with LoRA, DoRA, and other adapters, 4-bit QLoRA, and vision-language tuning. The same declarative style covers tabular, text, image, audio, and time series problems, with multi-task outputs, AutoML, and hyperparameter search through Optuna or Ray Tune. Training scales from one GPU to a cluster using Accelerate, DeepSpeed, and Ray, and finished models can be served from a built-in FastAPI endpoint, exported to SafeTensors or ONNX, or pushed to the Hugging Face Hub. Install it with pip on Python 3.12 or newer; LLM jobs need a CUDA GPU, while small tabular models can train on a CPU. The project now sits under the Linux Foundation AI and Data umbrella with an Apache 2.0 license, so commercial use is fine. It suits engineers who want versioned configs in place of custom scripts.
Should you use Ludwig?
Pick it when
Pick it when your team wants to train tabular, text, image, or multimodal models, or fine-tune an LLM, from one declarative YAML file, and may later scale the same config from a laptop to a Ray cluster.
Look elsewhere when
For LLM-only fine-tuning, purpose-built tools such as LLaMA-Factory or Axolotl offer more preference methods and model presets. For plain tabular prediction, gradient boosting with XGBoost is usually simpler and needs no GPU.
Alternatives to Ludwig
- Axolotl
Also config-driven, but LLM-focused with DPO, ORPO, and KTO recipes; no tabular or general multimodal tasks.
- XGBoost
Fast gradient boosting for tabular data alone, with no deep learning setup and no GPU required.
- LLaMA-Factory
Point-and-click LLM fine-tuning across 100+ models with LoRA, QLoRA, and RLHF, where Ludwig spreads across many model types.
- AutoTrain
No-code UI over classification, regression, and LLM tasks without writing YAML, but no longer maintained.
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Details
- Category
- Model Training & Fine-Tuning
- Price
- Free
- Platform
- Local/Desktop
- Difficulty
- Easy (2/5)
- License
- Apache-2.0
- Minimum VRAM
- 8 GB
- Added
- Apr 3, 2026