ludwig-ai/ludwig

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What is Ludwig?

Ludwig is a declarative deep learning framework that lets you train, fine-tune, and deploy AI models — from LLM fine-tuning to tabular classification — using a YAML config file and zero boilerplate Python.

# Fine-tune Llama-3.1 with LoRA in one config file
model_type: llm
base_model: meta-llama/Llama-3.1-8B
adapter:
  type: lora
trainer:
  type: finetune
  epochs: 3
input_features:
name: instruction
    type: text
output_features:
name: response
    type: text

ludwig train --config model.yaml --dataset my_data.csv

Tech stack: Python 3.12 · PyTorch 2.7+ · Pydantic 2 · Transformers 5 · Ray 2.54

Ludwig is hosted by the Linux Foundation AI & Data.

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What's New in Ludwig 0.16

| Feature | Description | | ------------------------------- | ------------------------------------------------------------------------------------------------------ | | PatchTST & N-BEATS encoders | State-of-the-art timeseries forecasting encoders with MASE/sMAPE metrics | | Advanced PEFT adapters | PiSSA, EVA, CorDA/LoftQ initializers; TinyLoRA, OFT, HRA, WaveFT, LN-Tuning, VBLoRA, C3A adapter types | | VLM fine-tuning | Train LLaVA, Qwen2-VL, InternVL via is_multimodal: true with gated cross-attention | | HyperNetwork combiner | Conditioning-based feature fusion — one feature generates weights for others | | Nash-MTL & Pareto-MTL | Game-theoretic and preference-based multi-task loss balancing | | LLM config generation | ludwig generate_config "describe your task" — LLM writes the YAML for you | | ModelInspector | Architecture analysis, weight collection, feature importance proxy | | Ray Serve & KServe | Distributed and Kubernetes-native model deployment shims | | GRPO alignment | Reward-model-free RLHF via Group Relative Policy Optimization | | torchao quantization + QAT | PyTorch-native int4/int8/float8 with Quantization-Aware Training | | Multi-adapter PEFT | Multiple named LoRA adapters with weighted merging (TIES, DARE, SVD) | | Native Optuna executor | GPT/TPE/CMA-ES samplers, pruning, resumable SQLite/PostgreSQL storage | | Timeseries forecasting | model.forecast(dataset, horizon=N) API with TimeseriesOutputFeature | | Muon & ScheduleFreeAdamW | New optimizers for large-scale pretraining and fine-tuning | | Image segmentation decoders | UNet, SegFormer, FPN decoders for semantic segmentation |

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Installation

pip install ludwig           # core
pip install ludwig[full]     # all optional dependencies
pip install ludwig[llm]      # LLM fine-tuning only

Requires Python 3.12+. See contributing for a full dependency matrix.

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Quick Start

Fine-tune an LLM (instruction tuning)

Ludwig supports the full LLM fine-tuning spectrum:

| Technique | Config key | | --------------------------------- | ------------------------------------------------------------------------ | | Supervised fine-tuning (SFT) | trainer.type: finetune | | DPO / KTO / ORPO / GRPO alignment | trainer.type: dpo (or kto, orpo, grpo) | | LoRA / DoRA / VeRA / PiSSA | adapter.type: lora (or dora, vera, lora + init_weights: pissa) | | 4-bit QLoRA (bitsandbytes) | quantization.bits: 4 | | torchao + QAT | quantization.backend: torchao | | Multi-adapter with merging | adapters: dict + merge: block | | VLM (vision-language) | is_multimodal: true |

model_type: llm
base_model: meta-llama/Llama-3.1-8B
quantization:
  bits: 4
adapter:
  type: lora
prompt:
  template: |
    ### Instruction: {instruction}
    ### Input: {input}
    ### Response:
input_features:
name: prompt
    type: text

output_features:
name: output
    type: text

trainer:
  type: finetune
  learning_rate: 0.0001
  batch_size: 1
  gradient_accumulation_steps: 16
  epochs: 3
  learning_rate_scheduler:
    decay: cosine
    warmup_fraction: 0.01
backend:
  type: local

export HUGGING_FACE_HUB_TOKEN="<your_token>"
ludwig train --config model.yaml --dataset "ludwig://alpaca"

Train a multimodal classifier

input_features:
name: review_text
    type: text
    encoder:
      type: bert
name: star_rating
    type: number
name: product_image
    type: image
    encoder:
      type: dinov2
output_features:
name: recommended
    type: binary

ludwig train --config model.yaml --dataset reviews.csv

Generate a config from natural language

ludwig generate_config "I have a CSV with age, income, education level, and I want to predict loan default"

Make predictions

ludwig predict --model_path results/experiment_run/model --dataset new_data.csv

Launch a REST API

ludwig serve --model_path results/experiment_run/model
POST http://localhost:8000/predict

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Capabilities

LLM Fine-Tuning

• Supervised fine-tuning (SFT) on instruction/response pairs
• Alignment training: DPO, KTO, ORPO, GRPO (reward-model-free RLHF)
• PEFT adapters: LoRA, DoRA, VeRA, LoRA+, TinyLoRA, OFT, HRA, WaveFT, LN-Tuning, VBLoRA, C3A
• LoRA initializers: PiSSA, EVA, CorDA, LoftQ for improved convergence
• Multi-adapter PEFT: multiple named adapters on one base model, switchable at runtime; merge with TIES, DARE, SVD, magnitude pruning
• Quantization: 4-bit/8-bit QLoRA (bitsandbytes), torchao int4/int8/float8 with QAT
• VLM fine-tuning: LLaVA, Qwen2-VL, InternVL via is_multimodal: true
• Sequence packing for efficient training on variable-length inputs
• Paged and 8-bit optimizers for memory-efficient training

Multimodal & Tabular Models

• Input modalities: text, numbers, categories, binary, sets, bags, sequences, images, audio, timeseries, vectors, dates
• Text encoders: any HuggingFace Transformer (BERT, RoBERTa, ModernBERT, Qwen3, Llama-3.1, etc.), plus Mamba-2, Jamba
• Image encoders: DINOv2, ConvNeXt, EfficientNet, ViT, CAFormer, ConvFormer, PoolFormer, TIMM (1000+ models)
• Timeseries encoders: PatchTST, N-BEATS, CNN, RNN, Transformer; MASE and sMAPE metrics; model.forecast() API
• Combiners: concat, transformer, tab_transformer, FT-Transformer, TabNet, TabPFN v2, HyperNetwork, ProjectAggregate, GatedFusion, Perceiver
• Multi-task learning: multiple output features in a single model; Nash-MTL, Pareto-MTL, FAMO, GradNorm, uncertainty loss balancing
• Image segmentation: UNet, SegFormer, FPN decoders

Training Infrastructure

• Distributed training: HuggingFace Accelerate with DDP, FSDP, DeepSpeed (zero-code changes)
• Ray backend: training across a Ray cluster, larger-than-memory datasets via Ray Data
• Automatic batch size selection and learning rate range test
• Mixed precision (fp16/bf16), gradient checkpointing, gradient accumulation
• Optimizers: AdamW, Adafactor, SGD, Muon, ScheduleFreeAdamW, Lion, paged/8-bit variants
• Learning rate schedulers: cosine, linear, polynomial, reduce-on-plateau, OneCycleLR
• Model Soup: uniform and greedy checkpoint averaging for better generalization at zero inference cost
• Modality dropout for robust multimodal models

Hyperparameter Optimization

• Executors: Ray Tune (ASHA, PBT, Bayesian) and native Optuna (auto/GP/TPE/CMA-ES)
• Optuna persistence: SQLite or PostgreSQL for resumable HPO runs
• Pruning with Optuna's MedianPruner and HyperbandPruner
• Search spaces: uniform, log-uniform, choice, randint, quantized
• Full Ludwig config is searchable — any nested parameter can be a hyperparameter

Production & Deployment

• REST API: FastAPI server with Prometheus metrics and structured logging (ludwig serve)
• vLLM serving: OpenAI-compatible API with PagedAttention and continuous batching
• Ray Serve: distributed deployment with auto-scaling and traffic splitting
• KServe: Kubernetes-native deployment with Open Inference Protocol v2
• Model export: SafeTensors (default), torch.export .pt2 bundles, ONNX
• HuggingFace Hub: ludwig upload hf_hub — push model + auto-generated model card
• Docker: prebuilt containers at ludwigai/ludwig

Tooling & Integrations

• Experiment tracking: TensorBoard, Weights & Biases, Comet ML, MLflow, Aim Stack
• Model inspection: ModelInspector — weight enumeration, architecture summary, feature importance proxy
• Visualizations: learning curves, confusion matrices, calibration plots, ROC curves, hyperopt analysis
• AutoML: ludwig.automl.auto_train() — give it a dataset and a time budget; the YAML-driven search space samples encoder/combiner/decoder combinations and validates them before training
• Dataset quality checks: from ludwig.utils.dataset_quality import check_dataset_quality — validates a DataFrame before training (missing values, class imbalance, near-duplicate columns, ID leakage, …)
• OpenML integration: load any OpenML task directly — OpenMLLoader fetches by task ID and caches locally as Parquet
• LLM config generation: ludwig generate_config "describe your task" — LLM writes the YAML
• K-fold cross-validation: ludwig experiment --k_fold N
• Dataset Zoo: 70+ built-in benchmark datasets (ludwig://mnist, ludwig://alpaca, …)

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Examples

LLM & Alignment

| Use Case | Link | | ------------------------------------- | ----------------------------------------------------------------------------------- | | LLM instruction tuning (LoRA + QLoRA) | examples/llm | | DPO / GRPO alignment | examples/llm/alignment | | Advanced PEFT (PiSSA, OFT, VBLoRA, …) | examples/llms/peft_advanced | | VLM fine-tuning (LLaVA, Qwen2-VL) | examples/vlm |

Tabular & Multimodal

| Use Case | Link | | -------------------------------------- | ------------------------------------------------------------------------------------------------- | | Binary classification (Titanic) | examples/titanic | | Tabular classification (census income) | examples/adult_census_income | | Multimodal classification | examples/multimodal_classification | | Multi-task learning | examples/multi_task |

Timeseries & Vision

| Use Case | Link | | ------------------------------------------ | ----------------------------------------------------------------------------------------- | | Timeseries forecasting (PatchTST, N-BEATS) | examples/forecasting | | Weather forecasting | examples/weather | | Image classification (MNIST) | examples/mnist | | Semantic segmentation | examples/semantic_segmentation |

NLP & Audio

| Use Case | Link | | ------------------------ | --------------------------------------------------------------------------------------- | | Text classification | examples/text_classification | | Named entity recognition | examples/ner_tagging | | Machine translation | examples/machine_translation | | Speech recognition | examples/speech_recognition | | Speaker verification | examples/speaker_verification |

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Why Ludwig?

• Zero boilerplate — no training loop, no data pipeline, no evaluation code. The YAML config is the entire program.
• Best-in-class LLM support — full spectrum from LoRA to GRPO alignment, torchao QAT, and VLM fine-tuning, all in config.
• Multimodal out of the box — mix text, images, numbers, audio, and timeseries with one config change.
• Scale without code changes — go from laptop → multi-GPU → Ray cluster by changing backend.type.
• Expert control when you need it — every activation function, scheduler, and optimizer is configurable.
• Reproducible research — every run is logged and the full config is saved. Compare experiments with ludwig visualize.

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Publications

• Ludwig: A Type-Based Declarative Deep Learning Toolbox (2019)
• Declarative Machine Learning Systems (2021)
• Ludwig's State-of-the-Art Benchmarks

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Community

• Discord — ask questions, share what you've built
• GitHub Issues — bugs and feature requests
• X / Twitter — announcements
• Medium — tutorials and deep-dives