Ludwig is a declarative deep learning framework for LLMs, multimodal models, and tabular AI. It allows users to complete model training, fine-tuning, and deployment solely through YAML configuration files, without writing training loops, data pipelines, or evaluation code. The project is hosted by the Linux Foundation AI & Data, and its tech stack includes Python 3.12, PyTorch 2.7+, Pydantic 2, Transformers 5, and Ray 2.54.
In terms of use cases, Ludwig covers LLM fine-tuning and alignment, tabular and multimodal modeling, time series, and vision tasks. In the LLM domain, it supports supervised fine-tuning (SFT), alignment training (DPO, KTO, ORPO, GRPO, and other reward-model-free RLHF), multiple PEFT adapters (LoRA, DoRA, VeRA, LoRA+, TinyLoRA, OFT, HRA, WaveFT, LN-Tuning, VBLoRA, C3A), and LoRA initializers (PiSSA, EVA, CorDA, LoftQ). Multi-adapter PEFT allows multiple named adapters to be attached to one base model and switched at runtime, with support for merging methods such as TIES, DARE, SVD, and magnitude pruning. Quantization supports 4-bit/8-bit QLoRA (bitsandbytes) and torchao's int4/int8/float8 and QAT. Vision-language model fine-tuning supports LLaVA, Qwen2-VL, and InternVL.
Key capabilities include: input modalities covering text, numerical, categorical, binary, set, bag, sequence, image, audio, time series, vector, and date; text encoders supporting any HuggingFace Transformer (BERT, RoBERTa, ModernBERT, Qwen3, Llama-3.1, etc.) as well as Mamba-2 and Jamba; image encoders covering DINOv2, ConvNeXt, EfficientNet, ViT, CAFormer, ConvFormer, PoolFormer, and TIMM (1000+ models); time series encoders including PatchTST, N-BEATS, CNN, RNN, and Transformer, with support for MASE and sMAPE metrics and the model.forecast() API; combiners providing concat, transformer, tab_transformer, FT-Transformer, TabNet, TabPFN v2, HyperNetwork, ProjectAggregate, GatedFusion, and Perceiver; multi-task learning supporting Nash-MTL, Pareto-MTL, FAMO, GradNorm, and uncertainty-based loss balancing; image segmentation supporting UNet, SegFormer, and FPN decoders. Distributed training supports HuggingFace Accelerate's DDP, FSDP, and DeepSpeed (without code changes), and provides a Ray backend for cluster training and out-of-memory datasets. Automatic batch size selection, learning rate range testing, mixed precision, gradient checkpointing, and accumulation are all supported.
In terms of usage, users define models, training, and deployment through YAML configuration. Supported commands include: ludwig serve to start a REST API (FastAPI, Prometheus metrics, structured logging); vLLM to provide an OpenAI-compatible API; Ray Serve for distributed deployment; and KServe for Kubernetes-native deployment. Model export supports SafeTensors, torch.export .pt2, and ONNX, and can be uploaded to HuggingFace Hub via ludwig upload hf_hub. Experiment tracking integrates TensorBoard, Weights & Biases, Comet ML, MLflow, and Aim Stack. Hyperparameter optimization supports Ray Tune (ASHA, PBT, Bayesian) and native Optuna (auto/GP/TPE/CMA-ES), and can be persisted to SQLite or PostgreSQL. AutoML is provided through ludwig.automl.auto_train(), which searches encoder/combiner/decoder combinations given a dataset and time budget. It also supports K-fold cross-validation (ludwig experiment --k_fold N), dataset quality checks, OpenML integration, LLM configuration generation (ludwig generate_config), and 70+ built-in benchmark datasets (such as ludwig://mnist, ludwig://alpaca).
Applicable scenarios include: researchers conducting reproducible experiments (each run records and saves the complete configuration, which can be compared using ludwig visualize); engineers seamlessly scaling from notebooks to multi-GPU and then to Ray clusters; teams needing multimodal modeling to mix text, images, numbers, audio, and time series through a single configuration change.
In terms of limitations: Ludwig 0.16 requires Python 3.12+, and the specific dependency matrix is in the contribution guide. The official sources do not provide detailed information such as fees, pricing, or platform restrictions; these facts are not yet available.
