What types of models can I train with Ludwig?

Ludwig supports fine-tuning LLMs using methods like SFT, DPO, KTO, ORPO, and GRPO, as well as multi-modal training across text, images, audio, tabular data, and time series.

Is Ludwig free to use?

Yes, Ludwig is free and open source under the Apache 2.0 license. It does not offer a hosted tier, so users must provide their own compute resources.

How does Ludwig handle scaling from local to distributed training?

You can scale from a laptop to a Ray cluster by changing one line in the configuration, allowing you to adjust the backend without modifying the core code.

Who should avoid using Ludwig?

It is not suitable for users seeking a hosted, click-to-fine-tune service or those whose models require deeply custom layers that do not fit within a declarative YAML schema.

Ludwig

✓ Editorially verified

Declarative, YAML-driven deep learning framework for fine-tuning LLMs and multi-modal models without writing training loops.

Free· Free, Apache 2.0 open sourceFine-tuningMulti-model (PyTorch + HuggingFace Transformers)8.2 / 10

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In short

Ludwig allows users to define entire training pipelines in a single YAML file, supporting LLM fine-tuning and multi-modal tasks without writing custom code. It is best for ML engineers seeking reproducible, config-driven experiments who can manage their own compute infrastructure.

Best for

Pick Ludwig if you want reproducible, config-driven fine-tuning runs across LLMs and multi-modal tasks without writing training loops from scratch.

Skip if

Skip it if you want a hosted, click-to-fine-tune service or if your model needs deeply custom layers that don't fit a YAML schema.

Ludwig is an open-source deep learning framework that lets you define an entire training pipeline (preprocessing, encoders, architecture, training, evaluation) in a single YAML file instead of writing custom PyTorch code. Originally built at Uber and now hosted by the Linux Foundation AI & Data project, it has matured into a serious LLM fine-tuning toolkit supporting SFT, DPO, KTO, ORPO and GRPO with LoRA/QLoRA and other PEFT methods, plus native multi-modal training across text, images, audio, tabular data and time series.

It suits ML engineers and applied researchers who want repeatable, config-driven experiments without rebuilding boilerplate for every project. You can train on a laptop and scale to a Ray cluster by changing one line, export to SafeTensors or ONNX, and serve models via a built-in REST API. It's free under Apache 2.0 with no hosted tier - you bring your own compute - which makes it cheap to run but harder to adopt for teams without infrastructure skills.

Integrations cover HuggingFace Transformers and Hub, Weights & Biases, MLflow, TensorBoard, Docker and Ray. The trade-off versus writing raw PyTorch is the usual declarative one: fast iteration when your task fits the abstraction, friction when you need to escape it.

Editor's take

Ludwig is one of the few open-source frameworks that takes the YAML-as-config idea seriously for modern LLM fine-tuning, not just classical ML. The Linux Foundation stewardship and steady releases make it a safer long-term bet than most solo-maintainer fine-tuning libraries. Best for teams that already own their training infra.

— The AI Tool Bible editorial team

Pros

✅ Entire pipeline defined in one YAML file - no boilerplate training code
✅ First-class LLM fine-tuning with SFT, DPO, ORPO, GRPO and LoRA/QLoRA
✅ True multi-modal: text, images, audio, tabular and time series in one model
✅ Scale from laptop to Ray cluster by changing the backend, not the code
✅ Open source under Apache 2.0, backed by Linux Foundation

Cons

⚠️ Self-hosted only - no managed tier, you supply the GPUs
⚠️ Declarative abstraction can be limiting for highly custom architectures
⚠️ Steeper ramp for teams without PyTorch or Ray familiarity

Use cases

llm-fine-tuningmulti-modal-trainingtext-classificationtabular-mlmodel-servingdistributed-training

Frequently asked

What types of models can I train with Ludwig?: Ludwig supports fine-tuning LLMs using methods like SFT, DPO, KTO, ORPO, and GRPO, as well as multi-modal training across text, images, audio, tabular data, and time series.
Is Ludwig free to use?: Yes, Ludwig is free and open source under the Apache 2.0 license. It does not offer a hosted tier, so users must provide their own compute resources.
How does Ludwig handle scaling from local to distributed training?: You can scale from a laptop to a Ray cluster by changing one line in the configuration, allowing you to adjust the backend without modifying the core code.
Who should avoid using Ludwig?: It is not suitable for users seeking a hosted, click-to-fine-tune service or those whose models require deeply custom layers that do not fit within a declarative YAML schema.