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diffusers
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View guide →// devtools · huggingface/transformers
Transformers
Transformers is an open-source dev tools project by huggingface (huggingface/transformers on GitHub). It has 166,942 GitHub stars and 34,748 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 4 October 2026.
The model-definition framework behind modern AI — thousands of pretrained transformers for text, vision, and audio.
★ 166.9k stars⑂ 34.7k forksApache-2.0updated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
so here's a placeholder. The guide below is complete regardless.
// project facts
Project facts
- Repository
- huggingface/transformers
- GitHub stars
- 166,942
- Forks
- 34,748
- License
- Apache-2.0
- Last updated on GitHub
- 4 October 2026
- Category
- Dev Tools
- Listing
- Full install guide
- Official site
- Official docs
// how it works
How it works
- Pick a task
Text, image, audio, or multimodal — find the pipeline that matches what you want to build.
- Load a model
One line downloads a pretrained checkpoint from the Hugging Face Hub.
- Run it
Call the pipeline on your input; batching and device placement are handled for you.
- Fine-tune or deploy
Use the Trainer API for custom training, then export for serving.
// features
Why people love it
Thousands of models
One API across BERT, GPT, Llama, Whisper, ViT, and thousands more — swap checkpoints without rewriting code.
Pipelines API
Task-based one-liners for classification, generation, transcription, and vision — from prototype to demo in minutes.
Framework agnostic
PyTorch, TensorFlow, and JAX backends; export to ONNX and TorchScript for production serving.
Trainer
A full training loop with mixed precision, distributed training, and experiment tracking built in.
Tokenizers
Blazing-fast Rust tokenizers with Python bindings — correct encoding for every model family.
Ecosystem gravity
The default dependency of the open AI world: tutorials, integrations, and answers for everything.
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// run it
Run it yourself
- Step 1 — Install
Pip-install the library plus your framework of choice.
pip install transformers torch - Step 2 — Pick a pipeline
Choose the task; the docs list every supported pipeline with examples.
from transformers import pipeline - Step 3 — Generate
Load any Hub model by name and run it locally or on your GPU.
pipe = pipeline('summarization'); print(pipe(long_text))
// use cases
What you can build
- Prototype LLM features with a few lines of Python.
- Fine-tune open models on your own data.
- Build transcription, translation, and vision pipelines.
- Ship models to production with standard serving tools.
From the tooling end of the shelf, Pacwich handles monorepos that run on bun and pnpm — the unglamorous plumbing larger projects end up needing. Readers working through the Dev Tools shelf also stop at @shadcn/lint, an agent-first linter for Tailwind design systems: you define what is allowed, and errors explain the fix in terms of your components. Also on this shelf: phoenix — a code editor designed to make coding as fun as playing a video game.
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// faq
Questions, answered
Who maintains Transformers?
It is an open-source project at huggingface/transformers with 34,742 forks. Check the repo's contributors and recent commits to see how active maintenance is.
Is Transformers free for commercial use?
Yes — Apache-2.0. Individual model checkpoints carry their own licenses, so check each one.
Do I need a GPU?
Inference on small models works on CPU; training and large models want a GPU.
PyTorch or TensorFlow?
Both are supported; PyTorch is the community default today.
Where do the models come from?
The Hugging Face Hub hosts hundreds of thousands of checkpoints loadable by name.
// keep exploring
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// more in devtools
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Get Transformers
Free and open — grab the source, star the repo, and start creating.