Hugging Face

Hugging Face (Twitter) RT @AdinaYakup: New model from @Meituan_LongCat🚀 LongCat-Video-Avatar🔥 Audio driven character animation with text, image, and video inputs, all in one! ✨ MIT license ✨ Audio > talking video (single & multi-person) ✨ Natural motion and lip sync ✨ Fewer repeats, stable identity ✨ Available on @huggingface

Hugging Face (Twitter) RT @ClementDelangue: Time to follow huggingface.co/tencent to get the notification! https://twitter.com/DylanTFWang/status/2000943329928945985#m

Hugging Face (Twitter) RT @allen_ai: Last year Molmo set SOTA on image benchmarks + pioneered image pointing. Millions of downloads later, Molmo 2 brings Molmo’s grounded multimodal capabilities to video 🎥—and leads many open models on challenging industry video benchmarks. 🧵

Hugging Face (Twitter) RT @ben_burtenshaw: Fine-tune Nemotron 3 Nano in TRL with coding agents like claude code, colab, locally or on the hub. To fine tune, pick one of these tools: - Combine HF skills with a coding agent like claude code. - Use this colab notebook. - Train it on HF jobs using the Hugging Face hub - If you can, run this script on your own setup with uv This should get anyone started with fine tuning, and this is the perfect model to start with.

Hugging Face (Twitter) RT @_weiping: 🚀 Introducing Nemotron-Cascade! 🚀 We’re thrilled to release Nemotron-Cascade, a family of general-purpose reasoning models trained with cascaded, domain-wise reinforcement learning (Cascade RL), delivering best-in-class performance across a wide range of benchmarks. 💻 Coding powerhouse After RL, our 14B model: • Surpasses DeepSeek-R1-0528 (671B) on LiveCodeBench v5/v6/Pro. • Achieves silver-medal performance at IOI 2025 🥈. • Reaches a 43.1% pass@1 on SWE-Bench Verified, and 53.8% with test-time scaling. 🧠 What is Cascade RL? Instead of mixing heterogeneous prompts across domains, Cascade RL trains sequentially, domain by domain, which reduces engineering complexity, mitigates heterogeneous verification latencies, and enables domain-specific curricula and tailored hyperparameter tuning. ✨ Key insight Using RLHF for alignment as a pre-step dramatically boosts complex reasoning—far beyond preference optimization. Subsequent... Перейти на оригинальный пост

Hugging Face (Twitter) RT @DylanTFWang: 🎮Get a first look at Tencent HY World 1.5 (WorldPlay)! 🎮 Our newest world model with real-time interaction and long-term memory. It’s going *open-source* tomorrow.

Hugging Face (Twitter) RT @vanstriendaniel: 🎁 Datasets Wrapped 2025! Picked out some important @huggingface datasets from this year Part 1 today: Reasoning 2025 was the year reasoning exploded, some of the datasets that contributed to this...

Hugging Face (Twitter) RT @allen_ai: 🗓️ Tue Dec 16, 1–2pm PT: AMA with researchers + engineers from our Olmo & Molmo teams, hosted by r/LocalLLaMA (@LocalLLaMAsub). 💬 Ask your questions now—we’ll start answering when the AMA begins!

‌Hugging Face (Twitter) RT @IBMDeveloper: 🚨CUGA is now live on @huggingface! A new, open-source generalist agent built for complex, real-world workflows and enterprise experimentation: ibm.co/6011BtMLZ🤗

Hugging Face (Twitter) RT @ivan_bezdomny: All training data released as well. I'm sure getting that cleared isn't easy, but someone's gotta do it. @huggingface does the same as well. https://twitter.com/ctnzr/status/2000567576683012564#m

Hugging Face (Twitter) RT @0xDevShah: back at it again with Chatterbox turbo. #1 on @huggingface

Hugging Face (Twitter) RT @zohaibahmed: back at it again with Chatterbox turbo. #1 on @huggingface happy holidays! go build!