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.
This is no Woodstock AI but will be fun nonetheless haha. I’ll be hosting a live workshop with team members next week about the Enterprise Hugging Face hub.
1,000 spots available first-come first serve with some surprises during the stream!
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!
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reacted to DmitryRyumin's
post with 🤗🔥10 months ago
😀😲😐😡 New Research Alert - FER-YOLO-Mamba (Facial Expressions Recognition Collection)! 😡😥🥴😱 📄 Title: FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space 🔝
📝 Description: FER-YOLO-Mamba is a novel facial expression recognition model that combines the strengths of YOLO and Mamba technologies to efficiently recognize and localize facial expressions.
👥 Authors: Hui Ma, Sen Lei, Turgay Celik, and Heng-Chao Li
Just wanted to shout out a massive thank you to all 2000 of you who've followed me on Hugging Face! 🎉 It's incredible to have such an awesome crew backing me up as I dive into all these LLM experiments.
Even though not all my models turn out perfect, I've found some real gems and methods along the way 💎. It's like digging for treasure – sometimes you found nothing, but sometimes you find a pearl, and sometimes you find a new method to try.
Your support and encouragement mean the world to me, and I'm really stoked to keep experimenting and learning. If you told me some years ago I would have so much people following me for what I do, I wouldn't have believed it. Here's to more discoveries and adventures ahead! 🚀
Also, big thanks once again, and a huge shoutout to @IkariDev for being there through this journey and supporting me. I'm excited for our future work together and hope we will continue to make people happy! 👏
I want to thank @Gryphe too, since my early work was heavily inspired from MythoMax and the RP/ERP vibe of it. If I'm here today it's probably because of you 😂
I was so close to forget @chargoddard and his amazing tool too! What will we do without mergekit in our life? Thank you! 🙏
See y'all at 3k!
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reacted to isidentical's
post with ❤️10 months ago
Very excited to share the first two official Gemma variants from Google! Today at Google Cloud Next, we announced cutting-edge models for code and research!
First, google/codegemma-release-66152ac7b683e2667abdee11 - a new set of code-focused Gemma models at 2B and 7B, in both pretrained and instruction-tuned variants. These exhibit outstanding performance on academic benchmarks and (in my experience) real-life usage. Read more in the excellent HuggingFace blog: https://huggingface.co/blog/codegemma
Second, (google/recurrentgemma-release-66152cbdd2d6619cb1665b7a), which is based on the outstanding Google DeepMind research in Griffin: https://arxiv.org/abs/2402.19427. RecurrentGemma is a research variant that enables higher throughput and vastly improved memory usage. We are excited about new architectures, especially in the lightweight Gemma sizes, where innovations like RecurrentGemma can scale modern AI to many more use cases.
For details on the launches of these models, check out our launch blog -- and please do not hesitate to send us feedback. We are excited to see what you build with CodeGemma and RecurrentGemma!
Huge thanks to the Hugging Face team for helping ensure that these models work flawlessly in the Hugging Face ecosystem at launch!
DeepLearning.AI just announced a new short course: Open Source Models with Hugging Face 🤗, taught by Hugging Face's own Maria Khalusova, Marc Sun and Younes Belkada!
As many of you already know, Hugging Face has been a game changer by letting developers quickly grab any of hundreds of thousands of already-trained open source models to assemble into new applications. This course teaches you best practices for building this way, including how to search and choose among models.
You'll learn to use the Transformers library and walk through multiple models for text, audio, and image processing, including zero-shot image segmentation, zero-shot audio classification, and speech recognition. You'll also learn to use multimodal models for visual question answering, image search, and image captioning. Finally, you’ll learn how to demo what you build locally, on the cloud, or via an API using Gradio and Hugging Face Spaces.
Thank you very much to Hugging Face's wonderful team for working with us on this.
