Practical overview
When it fits
Use Ludwig when the main requirement is a configurable, repeatable training workflow spanning several model types, especially when YAML-based setup is preferable to writing training-loop code directly.
Who it is for
- AI and machine-learning developers
- Teams building configurable LLM fine-tuning workflows
- Practitioners working with multimodal, tabular, time-series or computer-vision models
- Developers seeking low-code model experimentation
Documented uses
- Define model architecture and feature configuration declaratively
- Fine-tune LLMs with supervised or preference-oriented training settings
- Apply parameter-efficient adapters and quantization options
- Build multimodal, tabular, forecasting and computer-vision training workflows
- Inspect models and configure optimization or deployment integrations
Requirements & limits
The repository is public and carries an Apache-2.0 license. Installation documentation requires Python 3.12 or later and lists core, full and LLM-focused package extras.
- Python 3.12 or later is required by the installation material.
- The framework uses YAML configuration and a CLI-oriented training workflow.
- Optional package extras separate core installation from broader dependencies and LLM-focused use.
- The documented stack includes PyTorch, Pydantic, Transformers and Ray.
- DockerHub distribution is shown alongside the Python package route.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Configure an LLM fine-tuning workflow
Create a documented YAML configuration for an LLM fine-tuning run.
Before you start
- Python 3.12 or later
- Ludwig installed with the core or LLM-focused package extra
- A base model identifier
- A dataset with fields matching the configured input and output features
Steps
Install Ludwig with the documented Python package command.
Create a YAML configuration specifying model_type: llm and a base model.
Define input and output features, then configure the trainer for fine-tuning.
Optionally add a LoRA adapter or quantization settings.
Associate the YAML file with a dataset using the documented Ludwig training command.
Expected outcome
A declarative configuration for an LLM fine-tuning workflow, with training settings expressed without boilerplate Python.
Guide limitations
- The supplied material documents configuration and invocation, but does not establish that a particular model, dataset or hardware environment will run successfully.
- Model and dataset requirements remain dependent on the selected configuration.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 2 October 2026
- Original website title
- ludwig-ai/ludwig · Official homepage title · retained in its source language