Hugging Face

Hugging Face (Twitter) RT @ChromiumDev: 2025 was a landmark year for local machine learning. We're looking back at how Transformers.js and WebGPU enabled state-of-the-art models right in the browser. See how to build the future of the web with @huggingface's @xenovacom → goo.gle/48Y4akB

Hugging Face (Twitter) RT @NVIDIAAIDev: The NVIDIA Nemotron family just crossed 5M downloads on @huggingface🤗 A massive thank you to the community for your work and enthusiasm. 🏗️ Get started here: nvda.ws/4pNTvQE

Hugging Face (Twitter) RT @Alibaba_Qwen: 🎨 Qwen-Image-Layered is LIVE — native image decomposition, fully open-sourced! ✨ Why it stands out ✅ Photoshop-grade layering Physically isolated RGBA layers with true native editability ✅ Prompt-controlled structure Explicitly specify 3–10 layers — from coarse layouts to fine-grained details ✅ Infinite decomposition Keep drilling down: layers within layers, to any depth of detail 🤗 Hugging Face: https://huggingface.co/Qwen/Qwen-Image-Layered 🧩 ModelScope: https://www.modelscope.cn/models/Qwen/Qwen-Image-Layered 💻 GitHub: https://github.com/QwenLM/Qwen-Image-Layered 📝 Blog: https://qwen.ai/blog?id=qwen-image-layered 📄 Technical Report: arxiv.org/abs/2512.15603 🚀 Demo (HF): https://huggingface.co/spaces/Qwen/Qwen-Image-Layered 🚀 Demo (ModelScope): https://www.modelscope.cn/studios/Qwen/Qwen-Image-Layered

Hugging Face (Twitter) RT @fe_parodi: i will cherish it forever - thanks @huggingface / @pollenrobotics! https://twitter.com/ClementDelangue/status/2000207679969435712#m

Hugging Face (Twitter) RT @mishig25: At @huggingface, how we created doc chatbot system using open weights LLMs & open source software stack: 1. chunk & embed docs markdowns using open weights embedding models (BAAI/bge-base-en-v1.5, google/embeddinggemma-300m, we usually use http://hf.co/spaces/mteb/leaderboard to find the hottest models), use @meilisearch (http://github.com/meilisearch/meilisearch) as our semantic search db 2. hf.co/mcp that acts as gateway to semantically search hf docs (among other things). http://github.com/huggingface/hf-mcp-server by @evalstate 3. add hf.co/mcp to hf.co/chat, which uses latest open weights LLMs such as zai-org/GLM-4.6 (hf.co/models) through HF Inference Providers (http://hf.co/docs/inference-providers) that runs your inference through @FireworksAI_HQ, @togethercompute, @GroqInc, @novita_labs

Hugging Face (Twitter) RT @novita_labs: 🤗 MiMo-V2-Flash is live on @huggingface , supported by Novita Test it yourself👇

Hugging Face (Twitter) RT @osanseviero: Introducing Gemma Scope 2 🤗Largest open release of interpretability tools (over 1 trillion parameters trained!) 🔬Works as a microscope to analyze all Gemma 3 models' internal activations 🗣️Advanced tools for analyzing chat behaviors

Hugging Face (Twitter) RT @jeffboudier: New perks for Enterprise organizations to engage with the AI community on @huggingface 📢 show your company with your profile activity 💼 show you're hiring on your profile I show you how in the video

Hugging Face (Twitter) RT @donvito: Google not done T5 Gemma 2 https://twitter.com/googleaidevs/status/2001723428810764625#m

Hugging Face (Twitter) RT @NielsRogge: In collaboration with @AIatMeta, we added support for Pixio in the Transformers library! It proposes 4 changes to Masked AutoEncoders (MAE), including scaling it to 2B images. It outperforms/matches DINOv3 trained at similar scales Find the models here: https://huggingface.co/collections/facebook/pixio

Hugging Face (Twitter) RT @andimarafioti: The Reachy mini desktop app is so aesthetically pleasing. Huge fan.

Hugging Face (Twitter) RT @osanseviero: Introducing T5Gemma 2, the next generation of encoder-decoder models 🚀 Built on top of Gemma 3, we were able to build compact models at sizes of 270m-270m, 1B-1B, and 4B-4B sizes. While most models today are decoder-only, T5Gemma 2 is the first (I'm aware of) multimodal, long-context, and heavily multilingual (140 languages) encoder-decoder model out there. We hope this model enables the model research community as well as the community of devs ready to explore with new architectures. Blog: https://blog.google/technology/developers/t5gemma-2 Models: https://huggingface.co/collections/google/t5gemma-2 Paper: arxiv.org/abs/2512.14856