Today is a huge day in Argillaâs history. We couldnât be more excited to share this with the community: weâre joining Hugging Face!
Weâre 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, weâve 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 Zephyrâs 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, weâre now the same team.
To those of you whoâve been following us, this wonât be a huge surprise, but it will be a big deal in the coming months. This acquisition means weâll double down on empowering the community to build and collaborate on high quality datasets, weâll bring full support for multimodal datasets, and weâll 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.
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!
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!
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â Today weâre releasing The Stack v2 & StarCoder2: a series of 3B, 7B & 15B code generation models trained on 3.3 to 4.5 trillion tokens of code:
- StarCoder2-15B matches or outperforms CodeLlama 34B, and approaches DeepSeek-33B on multiple benchmarks. - StarCoder2-3B outperforms StarCoderBase-15B and similar sized models. - The Stack v2 a 4x larger dataset than the Stack v1, resulting in 900B unique code tokens đ As always, we released everything from models and datasets to curation code. Enjoy!
@HugoLaurencon@Leyo & @VictorSanh are introducing HuggingFaceM4/WebSight , a multimodal dataset featuring 823,000 pairs of synthetically generated HTML/CSS codes along with screenshots of the corresponding rendered websites to train GPT4-V-like models đđ»
While crafting their upcoming foundation vision language model, they faced the challenge of converting website screenshots into usable HTML/CSS codes. Most VLMs suck at this and there was no public dataset available for this specific task, so they decided to create their own.
They prompted existing LLMs to generate 823k HTML/CSS codes of very simple websites. Through supervised fine-tuning of a vision language model on WebSight, they were able to generate the code to reproduce a website component, given a screenshot.
