ludwig

ludwig is a fine-tuning toolkit - Low-code framework for building custom LLMs, neural networks, and other AI models.

Overview

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

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.

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

• 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

• 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

ludwig is open-source, written primarily in Python, with 11,774 GitHub stars under the Apache 2.0 license. The latest release is v0.17.9 (2026-08-16).

Key capabilities

From the project's documentation:

• 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
• Quantization: 4-bit/8-bit QLoRA (bitsandbytes), torchao int4/int8/float8 with QAT
• VLM fine-tuning: LLaVA, Qwen2-VL, InternVL via is_multimodal: true

Install

A quick way to get started (always check the official docs for the latest):

pip install ludwig # core

How it fits a local-AI stack

ludwig runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related fine-tuning toolkits in the directory:

• "airllm"
• Axolotl
• "ColossalAI"
• "DeepSpeed"

Sources

• Source code & docs: ludwig-ai/ludwig
• Official website: http://ludwig.ai

Stats from GitHub, 2026-10-19.