Hugging Face
1069
18
Build a Physical AI model factory with NVIDIA Cosmos 3 on SageMaker HyperPod
Building a Physical AI system takes a continuous pipeline, not a single training job. This post shows how to run that model factory (synthetic data generation, post-training, and closed-loop evaluation with NVIDIA Cosmos 3) on a persistent, resilient Amazon SageMaker HyperPod cluster on Amazon EKS, with GPU goodput as the metric that matters.
Run agent-driven Amazon SageMaker HyperPod operations with InstantStart
HyperPod InstantStart is an open source control plane that composes Amazon EKS orchestration with the managed capabilities of Amazon SageMaker HyperPod. It drives the same guarded operations through both a web interface and an AI agent, turning cluster bootstrap, capacity, training, inference, and storage into dependable, agent-driven infrastructure.
Nvidia confirms it will buy Hugging Face for $12.9 billion
Nvidia said Hugging Face hosts over 3 million models and is used by over 18 million developers.
Nvidia is buying Hugging Face for almost $13 billion
Nvidia has agreed to buy Hugging Face for $12.93 billion, bringing one of the most popular hosting platforms for open-source AI models, datasets, and tools…
NVIDIA to Acquire Hugging Face
I’m excited to announce that NVIDIA has agreed to acquire Hugging Face for $12,930,300,000. Together, we will scale Hugging Face’s platform, strengthen its infrastructure and expand access to AI for developers and institutions worldwide. Over the past decade, Clem, Julien, Thomas and the team at Hugging Face have built something remarkable: a vibrant home for […]
SonicCaps: Large-Scale Diverse and Fine-Grained Captioning for Improved Audio-Retrieval
Recent advances in audio-language modeling have been driven by large-scale audio captioning datasets. However, existing datasets remain limited by low semantic diversity, generic descriptions lacking acoustic details, and one-to-one audio-caption mappings that poorly reflect the inherent ambiguity of auditory perception. We introduce SonicCaps, a large-scale audio captioning dataset comprising ~15M captions paired with ~700k audio clips, generated using a multi-modal large language model (Qwen3-Omni) conditioned on both audio and text. To explicitly promote diversity, we generate around 24 captions per audio via structured prompt engineering and few- shot generation, spanning main descriptions, rephrased variants (verbosity, style) and semantic tags. Human evaluation shows that SonicCaps is rated significantly higher than existing captioning datasets, with fine-grained analyses indicating that our captions are perceived as more descriptive and precise, which strongly correlates with quality judgments. Finally, training CLAP models on SonicCaps with a multi-caption sampling strategy consistently improves audio retrieval and zero-shot classification, with stronger generalization across public and commercial benchmarks. We release both SonicCaps and two specialized CLAP models on hugging face: https://huggingface.co/datasets/Zineb/SonicCaps.
OpenAI delayed its new model’s development after the Hugging Face hack
After an unreleased OpenAI model wreaked enough havoc to make international headlines, OpenAI delayed the development of a different unreleased model suite, Astra, in order…
The rise of AI ‘civilizations’ and the fall of corporate responsibility
Depending on who you ask, developer platform Hugging Face was recently attacked by OpenAI - after it lost control of its own AI tools …