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Molbapย 
posted an update about 1 month ago
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3049
๐Ÿš€ New blog: Maintain the unmaintainable โ€“ 1M+ Python LOC, 400+ models

How do you stop a million-line library built by thousands of contributors from collapsing under its own weight?
At ๐Ÿค— Transformers, we do it with explicit software-engineering tenets, principles that make the codebase hackable at scale.

๐Ÿ” Inside the post:
โ€“ One Model, One File: readability first โ€” you can still open a modeling file and see the full logic, top to bottom.
โ€“ Modular Transformers: visible inheritance that cuts maintenance cost by ~15ร— while keeping models readable.
โ€“ Config-Driven Performance: FlashAttention, tensor parallelism, and attention scheduling are config-level features, not rewrites.

Written with @lysandre ,@pcuenq and @yonigozlan , this is a deep dive into how Transformers stays fast, open, and maintainable.

Read it here โ†’ transformers-community/Transformers-tenets
lysandreย 
posted an update about 2 months ago
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6470
We're kick-starting the process of Transformers v5, with @ArthurZ and @cyrilvallez !

v5 should be significant: we're using it as a milestone for performance optimizations, saner defaults, and a much cleaner code base worthy of 2025.

Fun fact: v4.0.0-rc-1 came out on Nov 19, 2020, nearly five years ago!
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lysandreย 
posted an update 9 months ago
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8172
SmolVLM-2 and SigLIP-2 are now part of transformers in dedicated releases!

They're added on top of the v4.49.0 release, and can be installed from the following tags: v4.49.0-SmolVLM-2 and v4.49.0-SigLIP-2.

This marks a new beginning for the release process of transformers. For the past five years, we've been doing monthly releases featuring many models (v4.49.0, the latest release, features 9 new architectures).

Starting with SmolVLM-2 & SigLIP2, we'll now additionally release tags supporting new models on a stable branch. These models are therefore directly available for use by installing from the tag itself. These tags will continue to be updated with fixes applied to these models.

Going forward, continue expecting software releases following semantic versioning: v4.50.0 will have ~10 new architectures compared to v4.49.0, as well as a myriad of new features, improvements and bug fixes. Accompanying these software releases, we'll release tags offering brand new models as fast as possible, to make them accessible to all immediately.
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ArthurZย 
posted an update 12 months ago
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5216
Native tensor parallel has landed in transformers!!! https://github.com/huggingface/transformers/pull/34184 thanks a lot to the torch team for their support!

Contributions are welcome to support more models! ๐Ÿ”ฅ
Molbapย 
posted an update over 1 year ago
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5520
๐Ÿš€๐Ÿš€ Exciting times for the document AI community!

We're thrilled to announce the release of some of the largest OCR datasets available to the public.
๐Ÿ”ฅ With over 26 million pages , 18 billion text tokens, and 6TB of data, these resources are a significant leap forward for document AI research.

Here's how to access these datasets quickly:

from datasets import load_dataset

pdfa_dataset = load_dataset('pixparse/pdfa-eng-wds', streaming=True)
IDL_dataset = load_dataset('pixparse/idl-wds', streaming=True)

This enables you to stream them directly, integrating seamlessly with your projects using the Hugging Face datasets library. On the hub, you can find them here:

pixparse/pdfa-eng-wds
pixparse/idl-wds

For lean data loading, the new [chug](https://github.com/huggingface/chug) library offers a solution with pdf decoding:


import chug

task_cfg = chug.DataTaskDocReadCfg(
    page_sampling='all',
)
data_cfg = chug.DataCfg(
    source='pixparse/pdfa-eng-wds',
    split='train',
    batch_size=None,
    format='hfids',
    num_workers=0,
)
data_loader = chug.create_loader(
    data_cfg,
    task_cfg,
)
sample = next(iter(data_loader))



We owe a huge thank you to Peter Wyatt, Kate Tasker, Rachel Taketa, Ali Furkan Biten, Ruben Tito, and their colleagues for their contributions. Their work putting these datasets together has been invaluable. ๐Ÿค—

Looking Ahead:

We're on a mission to enhance document AI capabilities, and these datasets are just the beginning. With your engagement and innovation, we're confident in the community's ability to develop robust OCR solutions. We encourage you to explore these datasets, experiment with the code, and contribute to the collective progress in document AI.

For detailed information on usage and licensing, please refer to the dataset cards on the Hugging Face hub.
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ArthurZย 
posted an update over 1 year ago
ArthurZย 
posted an update over 1 year ago
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Just when I was about to go to bed....... Here we go again