Hugging Face

‌Hugging Face (Twitter) RT @linoy_tsaban: The first open source Video-Audio generation model just LANDED 🔥 https://huggingface.co/Lightricks/LTX-2

Hugging Face (Twitter) RT @Thom_Wolf: Reachy Mini starring in Jensen's CES keynote 🌟 really proud is was so prominently featured on stage and humbled that our product is getting so many AI builders excited and building you don't have to make humanoids just because everyone else is talking about them – be contrarian - build what you think is the right thing to create now

Hugging Face (Twitter) RT @NVIDIADRIVE: Curious how reasoning-based autonomous vehicles are built in practice? See how NVIDIA Alpamayo brings together open models, datasets, and simulation in a full reasoning-based AV workflow: 🔹 Generate trajectory predictions with reasoning traces using Alpamayo 1, available on @huggingface 🔹 Train and evaluate models with Physical AI Open Datasets 🔹 Test end-to-end performance in AlpaSim, an open-source closed-loop simulator Get started → nvda.ws/45z9PMP #CES2026

Hugging Face (Twitter) RT @ArmSoftwareDev: Hands-on edge AI with robotics 🤖 @DominicPajak prototyped an on-device AI app for @huggingface@pollenrobotics Reachy Mini. Running locally on @Arm-based @Raspberry_Pi 5, it shows how low-latency AI enables responsive interaction. Sometimes the best way to understand what's possible with AI is to build it! Code linked in comments 💻

Hugging Face (Twitter) RT @NVIDIAAIDev: NVIDIA Cosmos Reason 2 is here. 🥳 An open, highly accurate reasoning vision language model for physical AI, featuring: ✅ Improved spatio-temporal understanding and timestamp precision ✅ Flexible deployment with 2B and 8B model sizes ✅ Long-context reasoning with up to 256K tokens ✅ Expanded visual perception across complex environments We also have new Cosmos releases: Predict 2.5, Transfer 2.5, and the NVIDIA GR00T N1.6 robot foundation model. 📗Read our technical blog: nvda.ws/4swwC68 🤗 Download Cosmos Reason 2 on @HuggingFace: nvda.ws/3L4B6Qy

Hugging Face (Twitter) RT @pranamanam: Introducing PeptiVerse 🚀, our open-source platform for therapeutic peptide property prediction. We support WT and modified SMILES inputs, and can predict solubility💧, permeability🔬, hemolysis🩸, non-fouling👯, half-life⏱️, tox ☠️, and binding affinity🔗 -- try it out! 🤗: https://huggingface.co/spaces/ChatterjeeLab/PeptiVerse 📜: https://www.biorxiv.org/content/10.64898/2025.12.31.697180v1 🧵👇

Hugging Face (Twitter) RT @liquidai: Today, we release LFM2.5, our most capable family of tiny on-device foundation models. It’s built to power reliable on-device agentic applications: higher quality, lower latency, and broader modality support in the ~1B parameter class. > LFM2.5 builds on our LFM2 device-optimized hybrid architecture > Pretraining scaled from 10T → 28T tokens > Expanded reinforcement learning post-training > Higher ceilings for instruction following 🧵

Hugging Face (Twitter) RT @NVIDIARobotics: NVIDIA and @huggingface are integrating NVIDIA’s open Isaac technologies into the LeRobot library. 🤖 See how Isaac Lab-Arena, now available in @LeRobotHF Environment Hub, enables developers to evaluate VLA policies while creating robot environments that can be authored once and reused across the community. Designed for scalable, open-source physical AI workflows. 🔗nvda.ws/4qKy9Up

Hugging Face (Twitter) RT @NVIDIARobotics: 9M+ downloads worldwide. 🎉 In 2025, NVIDIA’s open robotics datasets topped the charts. Leading the way was the GR00T post-training dataset, @HuggingFace’s most downloaded robotics dataset with 835K downloads last month. Helping robots learn faster, everywhere. 🦾 Read the @aiworld_eu blog: nvda.ws/4jnKg7e

Hugging Face (Twitter) RT @TechCrunch: Nvidia CEO Jensen Huang showcases AI use cases that have become "utterly trivial" with the pace of advancement, during #CES2026.

Hugging Face (Twitter) RT @ClementDelangue: Super cool to see Jensen @nvidia showcasing Reachy Mini at his #CES26 keynote. Paired with a DGX Spark & Brev, it can make the perfect local home AI robotics setup!

Hugging Face (Twitter) RT @alvarobartt: 👾 `hf-mem` is all you need to estimate the required VRAM for inference of any model on @huggingface based on Safetensors metadata. - Written in Python - Lightweight, only depends on `httpx` - Runs w/ @astral_sh `uvx` as `uvx hf-mem --model-id ...` - Works with any Safetensors repository - Output inspired by @usgraphics TR-100 Machine Report