#1818th of 22 in Training and fine-tuning
ludwig
YAML-configured training and fine-tuning for LLMs, multimodal and tabular models
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- Stars
- 11.8k
- License
- Apache-2.0
- Last commit
- Sep 2026
- Last release
- Aug 2026
- Language
- Python
11,603 on 31 Mar 202611,775 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Ludwig trains, fine-tunes and serves models from a YAML config and a CLI, with no training loop code. It covers LLM fine-tuning (SFT, DPO, KTO, ORPO, GRPO, LoRA and other PEFT adapters, 4-bit QLoRA, torchao QAT), plus multimodal, tabular, timeseries and image models. Runs scale from local to multi-GPU and Ray clusters by changing the backend setting.
Who it is for: ML engineers training or fine-tuning LLMs and tabular models from config
Strengths
- Whole experiment defined in one YAML file; ludwig train, predict and serve cover the workflow
- Wide LLM tuning coverage: SFT, DPO, KTO, ORPO, GRPO, many PEFT adapters, QLoRA
- Mixes text, image, audio, number and timeseries inputs in one config
- Distributed training via Accelerate (DDP, FSDP, DeepSpeed) and Ray; Optuna and Ray Tune for HPO
Weaknesses
- Requires Python 3.12+ and recent PyTorch, Transformers and Ray versions
- Config schema is large; advanced options need reading the docs
- Gated base models such as Llama need a Hugging Face token
- GPU, VRAM and RAM requirements are not stated in the README
What it needs
- GPU optional
- Docker
- Needs PyTorch 2.7+, Transformers 5, Ray 2.54, Hugging Face Hub token (gated models)
- Models: Hugging Face Transformers, LoRA, QLoRA, DoRA, VeRA
- port 8000
Also in Training and fine-tuning
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|---|---|---|
| 1 | unslothDesktop and web app to run, fine-tune and export open models | 88 out of 100 |
| 2 | deepspeedPyTorch library for distributed training of large models with ZeRO | 83 out of 100 |
| 3 | verlReinforcement learning training library for LLM post-training with PPO and GRPO | 82 out of 100 |
| #4 | peftPython library for LoRA and other parameter-efficient fine-tuning of Transformers models | 81 out of 100 |
| #5 | trlPost-training library with SFT, DPO, GRPO and KTO trainers | 78 out of 100 |