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huggingface_hub v1.0: Five Years of Building the Foundation of Open Machine Learning
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Hafedh Hichri PRO
not-lain
AI & ML interests
custom AI models with HF integration, HuggingFace fellow 🤗
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huggingface_hub v1.0: Five Years of Building the Foundation of Open Machine Learning
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Meet OpenEnv 👋, an open ecosystem of environments for intelligent agents. Build, share, and test agents safely and consistently.
Ideal for training with TRL (we include examples🤓), deployment, and community collaboration via the HF Hub
Blog: https://huggingface.co/blog/openenv
Hub for Environments: https://huggingface.co/openenv
OpenEnv repo: https://github.com/meta-pytorch/OpenEnv
Try it out using TRL: https://huggingface.co/docs/trl/main/en/openenv
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Meet OpenEnv 👋, an open ecosystem of environments for intelligent agents. Build, share, and test agents safely and consistently.
Ideal for training with TRL (we include examples🤓), deployment, and community collaboration via the HF Hub
Blog: https://huggingface.co/blog/openenv
Hub for Environments:
openenv
OpenEnv repo: https://github.com/meta-pytorch/OpenEnv
Try it out using TRL: https://huggingface.co/docs/trl/main/en/openenv
Ideal for training with TRL (we include examples🤓), deployment, and community collaboration via the HF Hub
Blog: https://huggingface.co/blog/openenv
Hub for Environments:
openenv
OpenEnv repo: https://github.com/meta-pytorch/OpenEnv
Try it out using TRL: https://huggingface.co/docs/trl/main/en/openenv
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Article
LightOnOCR-1B: The Case for End-to-End and Efficient Domain-Specific Vision-Language Models for OCR
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Supercharge your OCR Pipelines with Open Models
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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!
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!
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Sentence Transformers is joining Hugging Face!
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deepseek-ai/DeepSeek-OCR is out! 🔥 my take ⤵️
> pretty insane it can parse and re-render charts in HTML
> it uses CLIP and SAM features concatenated, so better grounding
> very efficient per vision tokens/performance ratio
> covers 100 languages
> pretty insane it can parse and re-render charts in HTML
> it uses CLIP and SAM features concatenated, so better grounding
> very efficient per vision tokens/performance ratio
> covers 100 languages
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Model statistics of the 50 most downloaded entities on Hugging Face
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ScreenEnv: Deploy your full stack Desktop Agent
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We just released the [BiRefNet_HR](
ZhengPeng7/BiRefNet_HR) for general use on higher resolution images, which was trained with images in
HF Model: ZhengPeng7/BiRefNet_HR.
HF Demo: ZhengPeng7/BiRefNet_demo, where you need to choose General-HR and set high resolution.
PyTorch weights & ONNX: in Google Drive and the GitHub release.
Here is a comparison between the results of the original one and the new HR one on HR inputs:
And, the performance of this new HR one and the previous one trained in
2048x2048. If your images are mostly larger than 1024x1024, use BiRefNet_HR for better results! Thanks to
@Freepik
for the kind support of H200s for this huge training.HF Model: ZhengPeng7/BiRefNet_HR.
HF Demo: ZhengPeng7/BiRefNet_demo, where you need to choose General-HR and set high resolution.
PyTorch weights & ONNX: in Google Drive and the GitHub release.
Here is a comparison between the results of the original one and the new HR one on HR inputs:
And, the performance of this new HR one and the previous one trained in
1024x1024 on val set:Loved this
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We've just forked LBM to reproduce the LBM eraser results
Our fork : https://github.com/finegrain-ai/LBM
LBM paper: LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
LBM relighting demo : jasperai/LBM_relighting
Our fork : https://github.com/finegrain-ai/LBM
LBM paper: LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
LBM relighting demo : jasperai/LBM_relighting
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