Hugging Face

Hugging Face (Twitter) RT @Thom_Wolf: Few know about it but Nvidia’s open-source AI game is insane. Just look at this lead on HF activity Jensen’s flexing at GTC today Once they dominated with Software 1.0 (more than hardware). Now they’re setting up to win Software 2.0 aka AI models + datasets.

Hugging Face (Twitter) RT @vanstriendaniel: NVIDIA Just Released 8M Sample Open Dataset + OCR Tooling on @huggingface - 3x larger than v1 (just 2 months ago!) - Image/video QA, reasoning, multilingual OCR - Commercial-ready (CC-BY-4.0) @NVIDIAAI is one of the few major AI labs releasing datasets 🤗

Hugging Face (Twitter) RT @NVIDIAAIDev: We just launched new open models and datasets to make AI research and development more accessible 🤝 You now have open foundations to build specialized intelligent agents faster, safer, and at scale — from Nemotron to Cosmos, Isaac GR00T to Clara. Over 650 open models and 250 open datasets contributed by NVIDIA are now available on @huggingface. Learn more → nvda.ws/4hwUVvB

Hugging Face (Twitter) RT @ClementDelangue: Great to see the shoutout from Jensen @nvidia for open-source AI & @huggingface. Open-source is the foundation of AI and we need much more of it in the US & around the world!

Hugging Face (Twitter) RT @PyTorch: Next in our PyTorch Compiler Video Series, Sayak Paul introduces Diffusers, a Python library for state-of-the-art video, image, and audio generation, highlighting its optimization with torch.compile for performance benefits like offloading, LoRA, and quantization. ▶️ Watch the full video on YouTube: hubs.la/Q03QkjL60

Hugging Face (Twitter) RT @andimarafioti: You can now train SOTA models without any storage!🌩️ We completely revamped the Hub’s backend to enable streaming at scale. We streamed TBs of data to 100s of H100s to train SOTA VLMs and saw serious speed-ups. But how?

Hugging Face (Twitter) RT @NielsRogge: For those having no idea what the @huggingface ecosystem looks like in 2025, I got you covered Making some slides for an upcoming presentation...

‌Hugging Face (Twitter) RT @AdinaYakup: Glyph 🔥 a framework that scales context length by compressing text into images and processing them with vision–language models, released by @Zai_org Paper:https://huggingface.co/papers/2510.17800 Model:huggingface.co/zai-org/Glyph ✨ Compresses long sequences visually to bypass token limits ✨ Reduces computational and memory costs ✨ Preserves meaning through multimodal encoding ✨ Built on GLM-4.1V-9B-Base

Hugging Face (Twitter) RT @hanouticelina: 🔥 We're thrilled to announce 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 v1.0! After five years of development, this foundational release is packed with A fully modernized HTTP backend and a complete, from-the-ground-up CLI revamp! $ pip install huggingface_hub --upgrade 🧵highly recommend going through the thread if you're a user of huggingface_hub or any library that depend on it

‌Hugging Face (Twitter) RT @AdinaYakup: LongCat-Video🐱 foundational video generation model from @Meituan_LongCat https://huggingface.co/meituan-longcat/LongCat-Video ✨ 13.6 B - MIT license ✨ Text-to-Video + Image-to-Video + Video-Continuation = all in one unified framework ✨ 720p, 30fps videos in minutes ✨ Generates long videos with stable color and quality

‌Hugging Face (Twitter) RT @Presidentlin: Well done @MiniMax__AI

‌Hugging Face (Twitter) RT @reach_vb: MiniMax-M2 just dropped - 230B MoE with 10B active; built for coding, agents, & tool use; MIT license🔥 > #1 open-source model on Artificial Analysis benchmarks, #5 overall > Excels at multi-file edits, test-repair loops, and BrowseComp tasks > Fast, cheap, deployable - runs like a 10B, thinks like a 200B > Works with transformers, vLLM and SGLang 🤗 https://huggingface.co/MiniMaxAI/MiniMax-M2