Ludwig

Free

A low-code framework for building custom AI models like LLMs and other deep neural networks. [#opensource](https://github.com/ludwig-ai/ludwig)

FreeFree tier

Inputs: text, imageOutputs: text

Type

Open Source

Visit Website GitHub (40 stars)

About Ludwig

Ludwig is a declarative deep learning framework that allows users to train, fine-tune, and deploy AI models using a YAML configuration file without writing boilerplate code. It supports a wide range of tasks including LLM fine-tuning (with LoRA and advanced PEFT adapters), tabular classification, multimodal model training, timeseries forecasting, image segmentation, and more. Built on PyTorch and Hugging Face Transformers, Ludwig is open-source and hosted by the Linux Foundation AI & Data. The framework also features advanced capabilities like GRPO alignment, torchao quantization, multi-adapter PEFT, and built-in model inspection tools.

Key Features

Declarative YAML-based configuration with zero boilerplate Python code

Fine-tune large language models (e.g., Llama-3.1) with LoRA and other PEFT adapters

Support for multimodal models (vision-language, e.g., LLaVA, Qwen2-VL)

Tabular AI: classification, regression, and feature engineering

Timeseries forecasting with PatchTST, N-BEATS encoders and MASE/sMAPE metrics

Advanced PEFT adapters including PiSSA, TinyLoRA, OFT, HRA, WaveFT, and more

GRPO alignment for reward-model-free RLHF

torchao quantization (int4/int8/float8) with Quantization-Aware Training

Multi-adapter PEFT with weighted merging (TIES, DARE, SVD)

Native Optuna hyperparameter tuning with GPT/TPE/CMA-ES samplers

Pros & Cons

Pros

Low-code / declarative approach reduces boilerplate and accelerates development
Open source and community-driven under the Linux Foundation AI & Data
Comprehensive model zoo with support for LLMs, vision, multimodal, and timeseries
Active development with frequent releases (e.g., v0.16) and a large GitHub community (11.7k stars)
Seamless integration with PyTorch and Hugging Face Transformers
Advanced optimization techniques like GRPO, quantization, and multi-adapter merging

Cons

Requires Python 3.12, limiting compatibility with older environments
Steep learning curve for advanced features (custom adapters, distributed training)
Less widespread adoption compared to frameworks like Hugging Face Transformers or PyTorch Lightning
Documentation may be dense for beginners; some features are experimental

Best For

Fine-tuning LLMs for domain-specific tasks (chat, instruction, text generation)Tabular data classification and regression for business analyticsMultimodal model training (image-text tasks like VQA, captioning)Timeseries forecasting for stock prices, weather, or demand predictionSemantic image segmentation for computer vision applicationsRapid experimentation and prototyping with declarative configsDeploying AI models in production with Kubernetes-native serving

FAQ

What is Ludwig?

Ludwig is a declarative deep learning framework that lets you train, fine-tune, and deploy AI models using a YAML config file and zero boilerplate Python code.

How do I install Ludwig?

Install via pip: 'pip install ludwig' for the core; 'pip install ludwig[full]' for all dependencies; 'pip install ludwig[llm]' for LLM fine-tuning only. Requires Python 3.12+.

What models can I fine-tune with Ludwig?

Ludwig supports fine-tuning of large language models like Llama-3.1, and vision-language models like LLaVA, Qwen2-VL, and InternVL via its declarative config.

Is Ludwig free and open source?

Yes, Ludwig is open source and hosted by the Linux Foundation AI & Data. The code is available on GitHub under the ludwig-ai organization.

Does Ludwig support timeseries forecasting?

Yes, Ludwig includes state-of-the-art timeseries forecasting encoders (PatchTST, N-BEATS) and metrics (MASE, sMAPE) via the TimeseriesOutputFeature.