🏗️ Building on HF

Pablo Montalvo PRO

Molbap

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molbap

AI & ML interests

Tensors, vision, data

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liked a model about 1 hour ago

baidu/Unlimited-OCR

upvoted an article 6 days ago

Building Moon Bot: A Slack-Native Coding Agent Backed by HuggingFace Buckets

liked a Space 8 days ago

huggingface/physics-intern

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reacted to tomaarsen's post with 🤗🚀 8 months ago

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🤗 Sentence Transformers is joining Hugging Face! 🤗 This formalizes the existing maintenance structure, as I've personally led the project for the past two years on behalf of Hugging Face! Details:

Today, the Ubiquitous Knowledge Processing (UKP) Lab is transferring the project to Hugging Face. Sentence Transformers will remain a community-driven, open-source project, with the same open-source license (Apache 2.0) as before. Contributions from researchers, developers, and enthusiasts are welcome and encouraged. The project will continue to prioritize transparency, collaboration, and broad accessibility.

Read our full announcement for more details and quotes from UKP and Hugging Face leadership: https://huggingface.co/blog/sentence-transformers-joins-hf

We see an increasing wish from companies to move from large LLM APIs to local models for better control and privacy, reflected in the library's growth: in just the last 30 days, Sentence Transformer models have been downloaded >270 million times, second only to transformers.

I would like to thank the UKP Lab, and especially Nils Reimers and Iryna Gurevych, both for their dedication to the project and for their trust in myself, both now and two years ago. Back then, neither of you knew me well, yet you trusted me to take the project to new heights. That choice ended up being very valuable for the embedding & Information Retrieval community, and I think this choice of granting Hugging Face stewardship will be similarly successful.

I'm very excited about the future of the project, and for the world of embeddings and retrieval at large!

1 reply

posted an update 9 months ago

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🚀 New blog: Maintain the unmaintainable – 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 🤗 Transformers, we do it with explicit software-engineering tenets, principles that make the codebase hackable at scale.

🔍 Inside the post:
– One Model, One File: readability first — you can still open a modeling file and see the full logic, top to bottom.
– Modular Transformers: visible inheritance that cuts maintenance cost by ~15× while keeping models readable.
– 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 → transformers-community/Transformers-tenets

reacted to lysandre's post with 🚀 10 months 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!

6 replies

reacted to dvilasuero's post with 🤗🔥🚀 about 2 years ago

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Today is a huge day in Argilla’s history. We couldn’t be more excited to share this with the community: we’re joining Hugging Face!

We’re embracing a larger mission, becoming part of a brilliant and kind team and a shared vision about the future of AI.

Over the past year, we’ve been collaborating with Hugging Face on countless projects: launching partner of Docker Spaces, empowering the community to clean Alpaca translations into Spanish and other languages, launching argilla/notus-7b-v1 building on Zephyr’s learnings, the Data is Better Together initiative with hundreds of community contributors, or releasing argilla/OpenHermesPreferences, one of the largest open preference tuning datasets

After more than 2,000 Slack messages and over 60 people collaborating for over a year, it already felt like we were part of the same team, pushing in the same direction. After a week of the smoothest transition you can imagine, we’re now the same team.

To those of you who’ve been following us, this won’t be a huge surprise, but it will be a big deal in the coming months. This acquisition means we’ll double down on empowering the community to build and collaborate on high quality datasets, we’ll bring full support for multimodal datasets, and we’ll be in a better place to collaborate with the Open Source AI community. For enterprises, this means that the Enterprise Hub will unlock highly requested features like single sign-on and integration with Inference Endpoints.

As a founder, I am proud of the Argilla team. We're now part of something bigger and a larger team but with the same values, culture, and goals. Grateful to have shared this journey with my beloved co-founders Paco and Amélie.

Finally, huge thanks to the Chief Llama Officer @osanseviero for sparking this and being such a great partner during the acquisition process.

Would love to answer any questions you have so feel free to add them below!

28 replies

reacted to danaaubakirova's post with ❤️🚀 about 2 years ago

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The Document AI team (@Molbap , @rwightman , @danaaubakirova ) at Hugging Face is developing a new multimodal data augmentation pipeline utilising both visual and textual aspects of document images.

Check out my latest blog post for more details:
https://huggingface.co/blog/danaaubakirova/doc-augmentation

Please, share your thoughts and suggestions with us.
And stay tuned for the updates!

reacted to Narsil's post with 🔥 about 2 years ago

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2027

text-generation-inference v2.0.3 is out.

Main new features:
- Falcon2 support
- PaliGemma support
- New faster speculation method from IBM !

https://github.com/huggingface/text-generation-inference/releases

reacted to merve's post with 🚀🔥 about 2 years ago

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New open Vision Language Model by @Google : PaliGemma 💙🤍

📝 Comes in 3B, pretrained, mix and fine-tuned models in 224, 448 and 896 resolution
🧩 Combination of Gemma 2B LLM and SigLIP image encoder
🤗 Supported in transformers

PaliGemma can do..
🧩 Image segmentation and detection! 🤯
📑 Detailed document understanding and reasoning
🙋 Visual question answering, captioning and any other VLM task!

Read our blog 🔖 hf.co/blog/paligemma
Try the demo 🪀 hf.co/spaces/google/paligemma
Check out the Spaces and the models all in the collection 📚 google/paligemma-release-6643a9ffbf57de2ae0448dda
Collection of fine-tuned PaliGemma models google/paligemma-ft-models-6643b03efb769dad650d2dda

13 replies

reacted to Wauplin's post with ❤️🚀 about 2 years ago

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🚀 Just released version 0.23.0 of the huggingface_hub Python library!

Exciting updates include:
📁 Seamless download to local dir!
💡 Grammar and Tools in InferenceClient!
🌐 Documentation full translated to Korean!
👥 User API: get likes, upvotes, nb of repos, etc.!
🧩 Better model cards and encoding for ModelHubMixin!

Check out the full release notes for more details:
Wauplin/huggingface_hub#6
👀

reacted to albertvillanova's post with 🔥🚀 about 2 years ago

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🚀 We recently released datasets 2.19.0! 📦

🔥 What's New:
- Polars integration 🐻‍❄️
- fsspec support for conversion to JSON, CSV, and Parquet
- Mode parameter for Image feature
- CLI function to convert script-datasets to Parquet
- Dataset.take and Dataset.skip

Plus, a bunch of general improvements & bug fixes!

Check out the release notes: https://github.com/huggingface/datasets/releases/tag/2.19.0

Upgrade now and power up your data workflows! 💥

2 replies

reacted to Pclanglais's post with 🔥 about 2 years ago

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Announcing that we are on our way to solve a long standing issue of document processing: correction of OCR mistakes. Pleias publishes the largest dataset to date with automated OCR correction, 1 billion words in English, French, German and Italian.

OCR quality is long-standing issue of digitization. Cultural heritage texts are especially concerned due to the primary sources being old documents (with many artifacts, blots, degradation) and to the limitation of OCR technology for historical scripts. When we released Common Corpus, a 500 Billion words corpus in the public domain, this was the primary criticism.

Recent breakthrough in post-OCR correction has been made possible thanks to progress in open LLM research and several months of dedicated training and alignment by Pleias as well as the HPC resources from GENCI–IDRIS (Grant 2023-AD011014736) on Jean-Zay.

Announcement: https://huggingface.co/blog/Pclanglais/post-ocr-correction

Post-OCR-Correction dataset: PleIAs/Post-OCR-Correction

reacted to VictorSanh's post with 🔥🚀 about 2 years ago

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New open multimodal model in town: Idefics2!

💪 Strong 8B-parameters model: often on par with open 30B counterparts.
🔓Open license: Apache 2.0.
🚀 Strong improvement over Idefics1: +12 points on VQAv2, +30 points on TextVQA while having 10x fewer parameters.
📚 Better data: boosting OCR capabilities with 6TB of documents to transcribe, and improving QA capabilities on charts/figures/diagrams.
🕵️‍♀️ Transparent training data: inspect and build upon all the data (10s of TB of data) we trained on.
🔲 More natural image processing: Incorporating strategies to treat images in their native resolution and native aspect ratio.
📸 High-resolution images: image resolutions up to 980 x 980 and integrating strategies that allow to trade computational efficiency for performance.
😎 2 checkpoints: Releasing both base checkpoint and instruction fine-tuned checkpoint. Chat version to come.

Ressources: HuggingFaceM4/idefics2-661d1971b7c50831dd3ce0fe
Blogpost: https://huggingface.co/blog/idefics2

reacted to HugoLaurencon's post with 🚀 about 2 years ago

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We release Idefics2-8B, a foundation vision language model with SOTA results for its size on many benchmarks.

For Idefics2, we adopted a simple architecture:
-Images are fed to a vision encoder, then to a modality projection to match the input dimension of the LLM, and finally to a perceiver resampler for efficient pooling.
-Interleaved image-text data are then passed to the LLM.

During the pre-training:
-The modality projection and perceiver resampler weights are newly initialized.
-We start with pre-trained models for the vision encoder and the LLM, and continue the training with LoRA.
-In total, we see 1.5T images!

We pre-train on 3 types of data, all publicly available:
-Interleaved image-text documents: our dataset OBELICS HuggingFaceM4/OBELICS
-Image caption pairs: only synthetic captions!
-PDF documents: IDL and PDFA

We kept the aspect ratio of the images with the Patch n' Pack strategy, with a resolution of up to 980x980.
At inference, it's also more efficient for lower-resolution images.

For the SFT, we build The Cauldron, a collection of 50 high-quality datasets in the user/assistant format.
It is a ready-to-use dataset for the fine-tuning of any VLM.
HuggingFaceM4/the_cauldron

Most current models, like LLaVA-NeXT, encode images with an excessive number of tokens, like 2880.
Instead, we put a focus on being efficient at inference by training on a mix of images encoded with 64 tokens, and 320 tokens.
The result is that we perform favorably compared to the best models in our size class, while being efficient at inference.

Pablo Montalvo PRO

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