Hugging Face

Hugging Face (Twitter) RT @Hesamation: Hugging Face just made fine-tuning 10x easier. they released HF skills that you can plug into Claude Code, Codex, and Gemini to: > write training scripts > submit jobs to cloud GPUs > monitor the progress > push the models to HF hubs it's not just for fine-tuning, but also model evaluation, paper publishing, and dataset curation. building a solid HF profile can get you very far in AI space, and there's no excuses anymore (unless for the 💸) you can read the guide on how to use this skill on this blog: https://huggingface.co/blog/hf-skills-training@huggingface

‌Hugging Face (Twitter) RT @drmapavone: We’ve just released @nvidia#DRIVE Alpamayo-R1 (AR1) — the world’s first industry-scale open #reasoning#VLA model for autonomous-vehicle (AV) research. AR1 integrates Chain-of-Causation reasoning with trajectory planning to improve decision-making in complex driving scenarios. Built on @nvidia#Cosmos#Reason, AR1 is designed as a customizable foundation for a broad range of AV applications — from instantiating an end-to-end backbone for autonomous driving to powering advanced, reasoning-based auto-labeling tools. Resources: Model: https://huggingface.co/nvidia/Alpamayo-R1-10B Inference Code: github.com/NVlabs/alpamayo Paper: https://research.nvidia.com/publication/2025-10_alpamayo-r1 Blog Post: https://blogs.nvidia.com/blog/neurips-open-source-digital-physical-ai/ A subset of the data used to train and evaluate AR1 is available in the @nvidia Physical AI Open Datasets: https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles AR1 can be evaluated using AlpaSim (github.com/NVlabs/alpasim), @nvidia's newly released open-source AV simulation framework built specifically for research and development. (Separate post on AlpaSim coming soon.) This release completes @nvidia’s trifecta — model, data, and simulator — to accelerate research and development in the autonomous-vehicle domain. Happy developing, and stay tuned for more! Huge thanks to the phenomenal team that made this possible @NVIDIAAI@nvidia.

Hugging Face (Twitter) RT @abidlabs: With Neptune acquired by OpenAI and W&B by Coreweave, it's important that your experiment tracking libraries are open, free, and forever yours. That's why we built @TrackioApp, a local-first library with the same API as W&B, just: 𝚒𝚖𝚙𝚘𝚛𝚝 𝚝𝚛𝚊𝚌𝚔𝚒𝚘 𝚊𝚜 𝚠𝚊𝚗𝚍𝚋

‌Hugging Face (Twitter) RT @tavilyai: 🚀 ICYMI: Thousands of developers have already shown interest in our new `/research` endpoint. We just dropped a full deep dive on @huggingface: how it works, why it’s different, and the technical + philosophical ideas behind our #1-ranked research engine. Read the blog → The momentum has been wild. Don’t miss what everyone’s trying.

Hugging Face (Twitter) RT @calebfahlgren: WOW! @AnthropicAI released interviews with 1,250 professionals about how they use AI for work. You can find it on @huggingface as an open dataset! https://twitter.com/AnthropicAI/status/1996627123021426919#m

Hugging Face (Twitter) RT @donvito: claude code fine-tuning a model 🔥 https://huggingface.co/blog/hf-skills-training

Hugging Face (Twitter) RT @ClementDelangue: I believe open datasets are becoming the most important contributions in AI. A few reasons: - We still lack truly great open datasets across domains, modalities, languages, and techniques - and the gap is especially painful in reinforcement learning - Datasets are costly, require a lot of hard work, and deeply unsexy, which makes them hard to create for smaller teams. Without strong open datasets, the open ecosystem simply can’t compete on equal footing with closed labs - Open datasets gives you a whole new layer of transparency and replicability that allows you to study biases, efficiency, interpretability and much more. - Open datasets compound in value. You can reuse them with every new architecture, optimizer, or training breakthrough, turning a single dataset into hundreds of state-of-the-art models over time. Models depreciate whereas open datasets appreciate. That’s why I... Перейти на оригинальный пост

‌Hugging Face (Twitter) RT @_akhaliq: Microsoft just released VibeVoice-Realtime-0.5B https://huggingface.co/microsoft/VibeVoice-Realtime-0.5B

Hugging Face (Twitter) RT @LeRobotHF: 🚀 Introducing X-VLA ; LeRobot’s new soft-prompted Vision-Language-Action model. X-VLA is built to scale across many embodiments: different robots, cameras, action spaces, and environments, all handled by one unified transformer backbone. - Generalist across robots (Franka, WidowX, Agibot, sim + real) - Soft-prompt domain IDs let the model adapt to new hardware with tiny learnable embeddings - Flow-matching + transformer core for smooth, continuous 50 Hz control - Pretrained on a mixed-embodiment dataset spanning 7+ platforms and diverse tasks - Fine-tune on any dataset using one of the 6 checkpoints we provide out of the box.

Hugging Face (Twitter) RT @mervenoyann: my @huggingface wrapped just arrived

Hugging Face (Twitter) RT @kwindla: Smart Turn v3.1. Smart Turn is a completely open source, open data, open training code turn detection model for voice AI, trained on audio data across 23 languages. The model operates on the input audio in a voice agent pipeline. Each time the user pauses briefly, this model runs and returns a binary decision about whether the user has finished speaking or not. The 3.1 release has two big improvements: 1. New data sets for English and Spanish, collected and labeled by contributors Liva AI, Midcentury, and MundoAI. The majority of the training data for the Smart Turn model is synthetically generated. Using synthetic data makes it possible to scale up training for a model like this. We've done a lot of work on the synthetic data pipeline to emulate as much of the natural variability of human speech as possible. But accurately labeled human data is very valuable and has a measurable impact on model quality. The 3.1 training run... Перейти на оригинальный пост

Hugging Face (Twitter) RT @donvito: Deployed my first @huggingface Space! Moved my PDF to Images Converter app from streamlit cloud to HF Spaces Upload a PDF and get a zip file of pages as PNGs or JPEGs, perfect for posts or decks Hope it's useful! Link to space in comments 👇