axolotl vs ludwig
LLM post-training and configurable AI workflows compared
axolotl and ludwig are Python frameworks that use YAML to configure model workflows. axolotl focuses on LLM post-training, while ludwig also covers tasks such as tabular prediction, time series, and model serving.
axolotl: Fine-Tune Large Language Models
Axolotl is a Python framework for fine-tuning and post-training language models through configurable workflows. It supports methods from LoRA and QLoRA to preference tuning and reinforcement learning, with options for multimodal and distributed training.
ludwig: Configure and Train AI Models with YAML
Ludwig is a Python framework for configuring, training, evaluating, and deploying AI models through declarative YAML rather than custom training loops. It suits teams that want one workflow for LLM fine-tuning, tabular prediction, and multimodal modeling.
| axolotl | ludwig | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 12.5k | 11.8k |
| Forks | 1.5k | 1.2k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- axolotl focuses on LLM supervised fine-tuning, preference tuning, and reinforcement learning; ludwig spans LLM fine-tuning, tabular prediction, and other AI tasks.
- axolotl lists methods including DPO, IPO, KTO, ORPO, GRPO, and reward-model training; ludwig highlights alignment workflows, adapters, and quantization options.
- axolotl supports distributed workflows with FSDP2, DeepSpeed, and multi-node options; ludwig lists Ray and Hugging Face Accelerate integrations.
- ludwig includes hyperparameter optimization, experiment tracking, prediction commands, and serving integrations; axolotl describes a consistent workflow for training, evaluation, quantization, and inference.
- Both projects use Python and Apache-2.0 licensing. axolotl lists 12.5k stars and 1.5k forks, while ludwig lists 11.8k stars and 1.2k forks.
Choose axolotl if you…
- need LLM post-training methods such as preference tuning or GRPO in one framework.
- want YAML-configured workflows for supported models, including multimodal or distributed training.
- need a framework centered on adapting language models on GPU hardware.
Choose ludwig if you…
- want one configurable workflow for LLMs, tabular prediction, time-series, and multimodal tasks.
- need hyperparameter optimization, experiment tracking, or prediction and serving integrations.
- prefer a declarative workflow that covers model configuration, training, and deployment options.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.