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Molbapย 
posted an update 11 days ago
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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

tense

#3 opened 11 days ago by
reach-vb

tense

#3 opened 11 days ago by
reach-vb

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#2 opened 11 days ago by
reach-vb

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#2 opened 11 days ago by
reach-vb
lysandreย 
posted an update about 1 month ago
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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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reach-vbย 
posted an update 4 months ago
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Excited to onboard FeatherlessAI on Hugging Face as an Inference Provider - they bring a fleet of 6,700+ LLMs on-demand on the Hugging Face Hub ๐Ÿคฏ

Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! ๐Ÿ’ฅ

Go, play with it today: https://huggingface.co/blog/inference-providers-featherless

P.S. They're also bringing on more GPUs to support all your concurrent requests!
  • 1 reply
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joaoganteย 
posted an update 5 months ago
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Let's go! Custom generation code has landed in transformers ๐Ÿš€

Have you designed a new cool KV cache? Maybe you're comparing new test-time compute ideas you've been researching? Have you found a way to do diffusion with existing models? You can now easily share your findings with the community with custom generation code, sharing the well-known generate interface ๐Ÿค“

In a nutshell, we have expanded the support of custom modeling code on the Hub with *model-agnostic* custom generation code. Write for one model, reuse with any model -- hopefully, this will democratize access to new generation ideas ๐Ÿซก

As a creator, you gain the ability to get your ideas in transformers with minimal effort. You'll also have access to all Hub features: a landing page for your creation, discussions, usage metrics, ... ๐Ÿค“

๐Ÿ’Ž Resources ๐Ÿ’Ž
- docs: https://huggingface.co/docs/transformers/generation_strategies#custom-decoding-methods
- minimal example: transformers-community/custom_generate_example
- discussion: transformers-community/support#10
reach-vbย 
posted an update 5 months ago
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hey hey @mradermacher - VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! ๐Ÿ’ฅ

as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!

in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.

p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage

p.p.s. this is fully backwards compatible so everything will work as it should! ๐Ÿค—
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mcpotatoย 
posted an update 7 months ago
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Stoked to announce we've partnered with JFrog to continue improving safety on the Hub! ๐Ÿธ

Their model scanner brings new scanning capabilities to the table, aimed at reducing alert fatigue.

More on that in our blog post: https://huggingface.co/blog/jfrog
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lysandreย 
posted an update 8 months ago
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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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reach-vbย 
posted an update 10 months ago
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VLMs are going through quite an open revolution AND on-device friendly sizes:

1. Google DeepMind w/ PaliGemma2 - 3B, 10B & 28B: google/paligemma-2-release-67500e1e1dbfdd4dee27ba48

2. OpenGVLabs w/ InternVL 2.5 - 1B, 2B, 4B, 8B, 26B, 38B & 78B: https://huggingface.co/collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c

3. Qwen w/ Qwen 2 VL - 2B, 7B & 72B: Qwen/qwen2-vl-66cee7455501d7126940800d

4. Microsoft w/ FlorenceVL - 3B & 8B: @jiuhai

5. Moondream2 w/ 0.5B: https://huggingface.co/vikhyatk/

What a time to be alive! ๐Ÿ”ฅ