Transformers
Transformers is a framework maintained by Hugging Face13, version 5.17.0. Its source code is public1 under the Apache License 2.03.
Transformers is Hugging Face's open-source Python library of model definitions for text, vision, audio, video, and multimodal models, used for both inference and training. Its README presents it as a shared model-definition layer that other training frameworks and inference engines build on, and it loads pretrained checkpoints from the Hugging Face Hub.12
- Repository: Repository (external site: github.com)
- Documentation: Documentation (external site: huggingface.co)
- License: LICENSE (external site: raw.githubusercontent.com)
- Release notes: Release v5.17.0 (external site: github.com)
Availability and license
Component reuse rights
- weights
- Unknown — no complete fact-level rights review
- code
- Unknown — no complete fact-level rights review
- data
- Unknown — no complete fact-level rights review
- documentation
- Unknown — no complete fact-level rights review
No complete system-rights review is recorded for this release.
Public materials checklist
| Item | Status | Notes and evidence |
|---|---|---|
| Source codeIs the source code publicly readable? | Public | 1 |
| DocumentationIs user documentation published? | Public | Installation, quickstart, and API documentation are published on the Hugging Face docs site.42 |
| InstallationAre installation instructions or packages publicly available? | Public | Documented installation with pip or uv (for example the transformers[torch] extra), from source, as an editable install, and from conda-forge.4 |
| Supported platformsAre supported operating systems or hardware documented? | Public | The installation guide states the library works with PyTorch and is tested on Python 3.10+ and PyTorch 2.5+, and documents setups for NVIDIA GPUs (CUDA), Intel GPUs (XPU), CPU-only use, and NVIDIA ARM64 devices.4 |
| Release statusAre versioned releases published? | Public | Versioned releases are published on GitHub; v5.17.0 was published on 2026-09-09.5 |
What it is useful for
Loading and running pretrained models through the high-level Pipeline API or model classes, fine-tuning them, and serving or chatting with a model from the command line.2
Run and use notes
- The installation guide documents offline use: download a model repository in advance and set HF_HUB_OFFLINE=1 (or pass local_files_only=True) so that loading does not contact the Hub.4
Organization context
In the news
Transformers 5.19.0 adds the EmbeddingGemma2 multimodal embedding model
On October 6, 2026 Hugging Face published version 5.19.0 of its Transformers library on GitHub. The release notes add support for EmbeddingGemma2, which the notes describe as a multimodal embedding model from Google built on the Gemma 4 architecture that encodes text, images, audio, and video into a shared 768-dimensional vector space and uses Matryoshka Representation Learning so embeddings can be truncated to 512, 256, or 128 dimensions. The notes also list breaking changes: every mixture-of-experts model that computes router logits now returns them when
output_router_logits=True, the"paged|"prefix for SDPA and flash attention implementations is deprecated in favor of continuous batching on the regular functions, andOwlv2ForObjectDetection.embed_image_querynow selects the query box by objectness score. EmbeddingGemma2 itself is a Google model and is not a separate catalog entry; USASI has not tested these changes.
U.S. eligibility
Eligible · basis: U.S.-governed project
Transformers is developed in Hugging Face's GitHub organization and its LICENSE names the Hugging Face team as copyright holder. Hugging Face's terms of service identify Hugging Face, Inc., a Delaware corporation, as the provider of its services, under New York law and courts, and its privacy policy states that the company and its servers are located in the United States (the French entity is named as its EU main establishment). See the hugging-face organization record for the full dual-country assessment.1367
Sources
This listing is not an endorsement, a safety assessment, or a federal approval.
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Cite this page
APA style
USASI. (2026, September 29). Transformers. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/transformers/
BibTeX
@misc{usasi_transformers,
author = {{USASI}},
title = {Transformers},
year = {2026},
month = sep,
howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/transformers/}},
note = {United States of America Superintelligence. Last updated 2026-09-29}
}The date is when this page was last updated. Add the date you read it if your style needs one.
For maintainers: a “Listed in USASI” badge
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