README.md

May 6, 2026 · View on GitHub

Declarative deep learning framework for LLMs, multimodal models, and tabular AI.

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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.

What's New in Ludwig 0.16

FeatureDescription
PatchTST & N-BEATS encodersState-of-the-art timeseries forecasting encoders with MASE/sMAPE metrics
Advanced PEFT adaptersPiSSA, EVA, CorDA/LoftQ initializers; TinyLoRA, OFT, HRA, WaveFT, LN-Tuning, VBLoRA, C3A adapter types
VLM fine-tuningTrain LLaVA, Qwen2-VL, InternVL via is_multimodal: true with gated cross-attention
HyperNetwork combinerConditioning-based feature fusion — one feature generates weights for others
Nash-MTL & Pareto-MTLGame-theoretic and preference-based multi-task loss balancing
LLM config generationludwig generate_config "describe your task" — LLM writes the YAML for you
ModelInspectorArchitecture analysis, weight collection, feature importance proxy
Ray Serve & KServeDistributed and Kubernetes-native model deployment shims
GRPO alignmentReward-model-free RLHF via Group Relative Policy Optimization
torchao quantization + QATPyTorch-native int4/int8/float8 with Quantization-Aware Training
Multi-adapter PEFTMultiple named LoRA adapters with weighted merging (TIES, DARE, SVD)
Native Optuna executorGPT/TPE/CMA-ES samplers, pruning, resumable SQLite/PostgreSQL storage
Timeseries forecastingmodel.forecast(dataset, horizon=N) API with TimeseriesOutputFeature
Muon & ScheduleFreeAdamWNew optimizers for large-scale pretraining and fine-tuning
Image segmentation decodersUNet, SegFormer, FPN decoders for semantic segmentation

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.

Quick Start

Fine-tune an LLM (instruction tuning)

Open In Colab

Ludwig supports the full LLM fine-tuning spectrum:

TechniqueConfig key
Supervised fine-tuning (SFT)trainer.type: finetune
DPO / KTO / ORPO / GRPO alignmenttrainer.type: dpo (or kto, orpo, grpo)
LoRA / DoRA / VeRA / PiSSAadapter.type: lora (or dora, vera, lora + init_weights: pissa)
4-bit QLoRA (bitsandbytes)quantization.bits: 4
torchao + QATquantization.backend: torchao
Multi-adapter with mergingadapters: 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

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
  • LLM config generation: ludwig generate_config "describe your task" — LLM writes the YAML
  • K-fold cross-validation: ludwig experiment --k_fold N
  • Dataset Zoo: 50+ built-in benchmark datasets (ludwig://mnist, ludwig://alpaca, …)

Examples

LLM & Alignment

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

Tabular & Multimodal

Use CaseLink
Binary classification (Titanic)examples/titanic
Tabular classification (census income)examples/adult_census_income
Multimodal classificationexamples/multimodal_classification
Multi-task learningexamples/multi_task

Timeseries & Vision

Use CaseLink
Timeseries forecasting (PatchTST, N-BEATS)examples/forecasting
Weather forecastingexamples/weather
Image classification (MNIST)examples/mnist
Semantic segmentationexamples/semantic_segmentation

NLP & Audio

Use CaseLink
Text classificationexamples/text_classification
Named entity recognitionexamples/ner_tagging
Machine translationexamples/machine_translation
Speech recognitionexamples/speech_recognition
Speaker verificationexamples/speaker_verification

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.

Publications

Community

Discord

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