huggingface/transformers
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
Ranks #36 of 2,845 repositories GStars tracks this week (top 1%).
5 releases in the last 90 days, the latest 6 days ago, with 437 contributors on GitHub.
Peaked at #14 in the weekly ranking the week of Sep 13, its best in the past year.
Use Cases & Benefits
- Provides a unified framework for defining, training, and running state-of-the-art machine learning models across text, vision, audio, and multimodal tasks.
- Centralizes model definitions to ensure compatibility across multiple training frameworks, inference engines, and adjacent libraries, simplifying model reuse and deployment.
- Use for quickly deploying pretrained models for natural language processing tasks like text generation, summarization, and translation with minimal setup.
- Use for building and fine-tuning custom computer vision models such as image classification, object detection, and visual question answering.
- Use for developing audio and speech applications including automatic speech recognition, text-to-speech, and audio classification leveraging pretrained models.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
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From the README
State-of-the-art pretrained models for inference and training
Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer vision, audio, video, and multimodal model, for both inference and training.
It centralizes the model definition so that this definition is agreed upon across the ecosystem.
transformersis the pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...), and adjacent modeling libraries (llama.cpp, mlx, ...) which leverage the model definition fromtransformers.We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be simple, customizable, and efficient.
There are over 1M+ Transformers model checkpoints on the Hugging Face Hub you can use.
Explore the Hub today to find a model and use Transformers to help you get started right away.
Transformers works with Python 3.9+ PyTorch 2.1+, TensorFlow 2.6+, and Flax 0.4.1+.
Create and activate a virtual environment with venv or uv, a fast Rust-based Python package and project manager.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+4,636 stars over 80 days across 22 snapshot days; best day +487 on Sep 14.
Forks
34.9K+836
Open issues
2.3K-49
Compared with peers in Deep Learning
Fastest-growing repositories GStars tracks in this cohort for the week of Oct 11, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| p-e-w/heretic | 35.5K | +1.3K | 41 | 124 days ago |
| koharu-rs/koharu | 6.4K | +483 | 63 | 30 days ago |
| pytorch/pytorch | 105.0K | +449 | 64 | 22 days ago |
| sgl-project/sglang | 37.6K | +156 | 75 | 3 days ago |
| huggingface/transformers | 167.6K | +0 | 75 | 6 days ago |
| CorentinJ/Real-Time-Voice-Cloning | 60.2K | +0 | 75 | — |
| Category median | 37.6K | +449 | 64 | — |
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