ludwig 0.16.1

Declarative machine learning: End-to-end machine learning pipelines using data-driven configurations.

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

Docs · Getting Started · Examples · Discord

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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
• 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, …)

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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