AutoTrain
No-code tool by Hugging Face for training ML models automatically.
About
AutoTrain Advanced is Hugging Face's no-code tool for training machine learning models through a web UI or command-line interface. It handles LLM fine-tuning with SFT, DPO, ORPO, reward modeling, and generic trainers, plus text classification and regression, token classification, sequence-to-sequence tasks, extractive question answering, and image classification and scoring. Jobs run on Hugging Face Spaces, in Google Colab, or locally after installing the Python package, which requires Python 3.10 or newer; the software itself is free, with charges applying only to compute. The project is released under the Apache 2.0 license. An important caveat: the maintainers have declared the project no longer maintained, with no new features or bug fixes planned, and they recommend alternatives such as Axolotl, TRL, or the transformers Trainer for new work. It remains relevant to practitioners who want push-button fine-tuning on familiar Hugging Face infrastructure and to those maintaining existing AutoTrain pipelines.
Should you use AutoTrain?
Pick it when
Pick it when you already run jobs on Hugging Face Spaces or Colab and must keep an existing AutoTrain pipeline alive, or want a quick no-code baseline for text or image classification before committing to a real training stack.
Look elsewhere when
The maintainers have ended development, with no new features or bug fixes planned, so newer models and open bugs stay unaddressed. For new LLM work use LLaMA-Factory or H2O LLM Studio for a GUI, or TRL or Axolotl if you write code or YAML.
Alternatives to AutoTrain
- LLaMA-Factory
Actively maintained web UI with LoRA, QLoRA, and DPO for 100+ LLMs, but LLM-only, with no token classification or image classification trainers.
- H2O LLM Studio
No-code browser GUI with LoRA, DoRA, DPO, and experiment tracking, but LLM-only and it needs a local NVIDIA GPU rather than Spaces.
- TRL
The maintained Hugging Face library the AutoTrain team points to; far more flexible, but you write Python instead of clicking.
- Ludwig
Declarative YAML covers LLMs plus tabular and multimodal tasks and scales out on Ray, at the cost of learning a config schema instead of clicking.
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Details
- Category
- Model Training & Fine-Tuning
- Price
- Free
- Platform
- Local/Desktop
- Difficulty
- Beginner (1/5)
- License
- Apache-2.0
- Minimum VRAM
- 8 GB
- Added
- Apr 3, 2026