LlamaFactory vs ludwig
LlamaFactory and ludwig for configuring and fine-tuning AI models
LlamaFactory and ludwig are Python frameworks for adapting and training AI models. LlamaFactory focuses on fine-tuning language and vision models through CLI and web interfaces, while ludwig uses YAML configuration across tasks including LLM, tabular, and multimodal modeling.
LlamaFactory: Fine-Tune Large Language and Vision Models
LlamaFactory provides CLI and web interfaces for fine-tuning a broad range of language and vision models. It supports parameter-efficient methods and preference training, with workflows for training, inference, and model export.
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.
| LlamaFactory | ludwig | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 75.3k | 11.8k |
| Forks | 9.2k | 1.2k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- LlamaFactory provides CLI and Gradio-based web workflows; ludwig centers its workflow on declarative YAML and commands or APIs.
- LlamaFactory covers pre-training, supervised fine-tuning, reward modeling, and preference or reinforcement-learning methods; ludwig emphasizes configurable training, evaluation, prediction, and serving workflows.
- LlamaFactory lists inference backends such as vLLM and SGLang; ludwig lists local and distributed training integrations, hyperparameter optimization, and REST serving options.
- LlamaFactory highlights model export and merged checkpoints; ludwig also provides experiment tracking, dataset checks, and model inspection utilities.
- Both projects use Python and have Apache-2.0 licenses. LlamaFactory lists 75.3k stars and 9.2k forks, while ludwig lists 11.8k stars and 1.2k forks.
Choose LlamaFactory if you…
- need CLI and graphical workflows for fine-tuning language and vision models.
- want to use methods such as LoRA, QLoRA, DPO, or PPO in supported workflows.
- need inference and model export as part of a model-adaptation workflow.
Choose ludwig if you…
- want to define model and training settings declaratively in YAML.
- need a shared workflow for LLM fine-tuning, tabular prediction, or multimodal and time-series tasks.
- want options for hyperparameter optimization, REST serving, or distributed training integrations.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.