22nd of 120 in ML frameworks and libraries
transformers
Python library defining open models for text, vision, audio and multimodal tasks
- Stars
- 168.1k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Python
157,045 on 27 Mar 2026168,057 on 23 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Transformers is a Python model-definition library for text, vision, audio, video and multimodal models, covering inference and training. It provides a high-level Pipeline API and loads checkpoints from the Hugging Face Hub (README cites over 1M). Training frameworks and inference engines such as vLLM, SGLang, Axolotl and llama.cpp reuse its model definitions.
Who it is for: Python developers and researchers running or fine-tuning open models
Strengths
- Pipeline API runs text, audio, vision and multimodal tasks in a few lines
- Model definitions are reused by vLLM, SGLang, Axolotl, DeepSpeed and others
- Loads checkpoints from the Hugging Face Hub, which the README says has over 1M
- Model files are deliberately unabstracted, so they can be edited or used standalone
Weaknesses
- README says it is not a modular toolbox of neural net building blocks
- Training API is optimized for Transformers' own PyTorch models
- Requires Python 3.10+ and PyTorch 2.6+
- Installing from source may give unstable code, per the README
What it needs
- GPU optional
- Docker
- Needs Python 3.10+, PyTorch 2.6+
- Models: Hugging Face Hub checkpoints, PyTorch
Quick answers
Is transformers open source?
Yes. Its code is on GitHub under the Apache-2.0 license.
Can you run transformers with Docker?
Yes. Its repository has a Docker setup: a Dockerfile, a Compose file or a published image.
Does transformers need a GPU?
No. It runs on a CPU and can use a GPU when you have one.
Head to head
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