Hugging Face

Hugging Face (Twitter) RT @chichengcc: Can we collect robot data without any robots? Introducing Universal Manipulation Interface (UMI) An open-source $400 system from @Stanford designed to democratize robot data collection 0 teleop -> autonomously wash dishes (precise), toss (dynamic), and fold clothes (bimanual)

Hugging Face (Twitter) RT @multimodalart: Qwen Image Multiple Angles LoRA is an exquisitely trained LoRA! 📐˚₊‧꒰ა Keep character and scenes consistent, and flies the camera around! Open source got there! One of the best LoRAs I've come across lately 🙌

‌Hugging Face (Twitter) RT @willmcgugan: Thank you @huggingface for sponsoring me on @github. You can join them at my sponsors profile:

Hugging Face (Twitter) RT @alvarobartt: One of the best parts of working at @huggingface is seeing how much the community drives open-source forward. On Text Embeddings Inference (TEI), we're just one PR away from 300 contributions thanks to all the people who've helped make embedding inference and serving better, faster, and safer. Nice reminder that open-source only works because people care enough to build together.

Hugging Face (Twitter) RT @victormustar: New: Qwen-Image-2509-MultipleAngles. Very solid model and probably a lot of creative use cases to find with it. ⬇️ Free demo available on Hugging Face

Hugging Face (Twitter) RT @ClementDelangue: .@nvidia is not holding back!

Hugging Face (Twitter) RT @_lewtun: Or ... you could just host them on hf.cohttps://twitter.com/AnthropicAI/status/1985752012189728939#m

Hugging Face (Twitter) RT @ClementDelangue: When you run AI on your device, it is more efficient and less big brother and free! So it's very cool to see the new llama.cpp UI, a chatgpt-like app that fully runs on your laptop without needing wifi or sending any data external to any API. It supports: - 150,000+ GGUF models - Drop in PDFs, images, or text documents - Branch and edit conversations anytime - Parallel chats and image processing - Math and code rendering - Constrained generation with JSON schema supported Well done @ggerganov and team!

Hugging Face (Twitter) RT @allen_ai: Introducing OlmoEarth 🌍, state-of-the-art AI foundation models paired with ready-to-use open infrastructure to turn Earth data into clear, up-to-date insights within hours—not years.

Hugging Face (Twitter) RT @Xianbao_QIAN: This work from @BytedanceTalk seed team will be transformative and open the era of iterative latent reasoning: why do model have to only think in human languages? At least I don't. The result is also significant: - 2.6B R4 (4 steps) model achieved comparable performance with qwen3 8B and Gemma 3 12B The project was supervised by @Yoshua_Bengio@jasoneshraghian How do you feel about this work?

Hugging Face (Twitter) RT @vanstriendaniel: Open models (GLM-4.6, Kimi K2, DeepSeek, etc.) + @opencode + @huggingface Inference Providers = automated GitHub code reviews. Tested on real repos. `/oc fix this` → bot creates PR. Works great, costs pennies. 5 minutes to set up! Guide: https://huggingface.co/docs/inference-providers/guides/github-actions-code-review

Hugging Face (Twitter) RT @yukangchen_: We open-sourced QeRL — Quantization-enhanced Reinforcement Learning ! 🧠 4-bit quantized RL training 💪 Train a 32B LLM on a single H100 GPU ⚙️ 1.7× faster overall training 🎯 Accuracy on par with bfloat16-level accuracy 🔥 Supports NVFP4 quantization format Moreover, we show that quantization helps exploration in RL training. Paper: https://huggingface.co/papers/2510.11696 Code: github.com/NVlabs/QeRL #NVIDIA#AIResearch#ReinforcementLearning#Quantization#LLM#EfficientAI