Hugging Face

Robotics is built on collaboration. 🤝 Hear @huggingface CSO & Co-founder Thomas Wolf explain how open source gives developers a stronger foundation to build on. Learn more about the @lerobothf v0.6 release ➡️ nvda.ws/3Ry5hmd

the dream: getting perfect edits from your favorite image model reality: missed details, choppy edits, forcing you to take it to photoshop etc Today, we are open sourcing our image editing trajectory data set on @huggingface The dataset includes: > 14 complete design trajectories > 294 annotated expert actions > First-person designer reasoning > Screen recordings > @Photoshop MCP tool calls > Keyboard shortcuts > Menu paths > Coordinate clicks > Screen recording > Designer reasoning > Structured action > Executable grounding (Photoshop MCP tool call, keyboard shortcut, menu path, or coordinate click) This dataset is useful for: > Computer-use agent SFT > Mid-training reasoning > Tool use & function calling > Human expert benchmarking If we want AI to edit like the pros, you need reasoning data from professional creatives

Hugging Face’s CEO on why companies are done renting their AI techcrunch.com/2026/07/10/hug…

The same way, we're probably one of the few AI startups with user network effects, we might become the first one with agent network effects!

I'm excited to share that I'm joining @huggingface as a ML Research Engineer on the science team 🚀🤗 My goal is to bridge the gap between researchers and the Hugging Face tools by collaborating with researchers and making it easier for the scientific community to use open-source data and models! If you're working at the intersection of AI and research or you want to help growing the AI4science community, feel free to reach out!

llama.cpp recently added DFlash support to its speculative decoding arsenal. Along with MTP, Eagle3 and various ngram-based techniques, the local model performance takes another step up. Special thanks to NVIDIA team and Ruixiang Wang specifically for leading this effort! github.com/ggml-org/llama…

the build small hackathon results are in 🏆 946 apps. 817 builders. 10 days. nothing bigger than 32B parameters 22 crazy winner, a thread 🧵

Hugging Face Gemma Challenge results are in! 📈 Over 6 days, more than 100 AI agents and humans collaborated to make Gemma 4 inference 5x faster on a single NVIDIA A10G GPU. - Fastest result: 491.8 TPS (fastest overall, but resulted in a drop in model quality in other areas) - Fastest lossless: 315 TPS A great example of what humans and agents can achieve when they work together.

Keeping up with AI news is becoming a full-time job. So my friend @ivan_bezdomny built HuggingNews, an AI-curated feed that surfaces the news actually worth reading. Soon, it will even personalize the feed using your Hugging Face profile. Been using it for weeks! Bookmark it, or ask your agent to send you the top 10 stories every morning or night. Less noise. More signal. More building! huggingnews.com

Build Small Winners Reveal x.com/i/broadcasts/1…

Very cool to see @netflix releasing video datasets and models on @huggingface. They have a strong video AI team and would be amazing to see them open-source more! huggingface.co/netflix

🤗 MOSS-Transcribe-Diarize-0.9B is now open source on @huggingface. Built with an end-to-end audio-to-structured-transcript paradigm: >0.9B open-source ASR model >Apache license 2.0 >128k long-context transcription >Up to ~90-min audio input >Speaker labels + timestamps in one generation >Multi-speaker diarization for meetings, interruptions, and overlapping voices >Hotword biasing for names, terms, and domain-specific vocabulary >~100 token/s on NVIDIA RTX 4090, RTF ~0.017 Thank you @sgl_project @vllm_project @Prince_Canuma @lllucas for day-0 support! 🚀 Github: github.com/OpenMOSS/MOSS-… Huggingface: huggingface.co/spaces/OpenMOS… API: shorturl.at/DWwe3 Live demo: shorturl.at/wRZ3j Technical Report：arxiv.org/abs/2601.01554 HF Space: huggingface.co/spaces/OpenMOS… AtomGit：ai.atomgit.com/OpenMOSS/MOSS-… SGLang-Omni: github.com/sgl-project/sg… vLLM: github.com/vllm-project/v… MLX-audio: github.com/Blaizzy/mlx-au… Discord:discord.gg/SmVQHGffZU

🤗 we just opensourced our tiny beast on @huggingface >0.9B ASR model >Apache license 2.0 >128k long-context transcription >Up to ~90-min audio input >Speaker labels + timestamps in one generation >Multi-speaker diarization for meetings, interruptions, and overlapping voices >Hotword customization for names, terms, and domain-specific vocabulary >~100 token/s on NVIDIA RTX 4090, RTF ~0.017 thank you @sgl_project @vllm_project @Prince_Canuma @lllucas for day-0 support 💖 try it live👇

GR00T 1.7 just landed in LeRobot 🟢 We upgraded our @NVIDIARobotics GR00T integration to 1.7, the newest open generation of NVIDIA's cross-embodiment foundation model. It swaps the previous VLM for Cosmos-Reason2-2B feeding a flow-matching action head, and we parity-tested it against NVIDIA's own Isaac GR00T implementation: same inputs, same outputs. Flash-attention is now optional too, so pip install 'lerobot[groot]' just works, and you can load NVIDIA's published checkpoints directly. Isaac Teleop is now in the ecosystem too, letting you drive an SO-101 with a VR controller over CloudXR/OpenXR.

19 million developers can now do more with frontier physical AI tools. 🤗 We're expanding open robotics with @HuggingFace, bringing GR00T 1.7, an open VLA model for humanoid robots and Isaac Teleop directly into LeRobot. Read the blog ➡️ nvda.ws/4eWYkTn #MACHINA2026

Run AI workloads on any cloud, store on Hugging Face, no egress fees to worry about. @skypilot_org and @huggingface built this together: • Models and datasets stay in HF Hub. SkyPilot runs the compute wherever your GPUs are • Reading data onto your GPUs adds no egress fees • Mount at a local path hf:// URL and use the HF_TOKEN you already have Huge thanks to @huggingface team and @jhanikhil for making it possible. Read the blog: huggingface.co/blog/skypilot-…

Storage and egress fees have been one of the biggest cloud lock-in traps in AI. When models and datasets are hard and expensive to move, infrastructure choice stops being a real choice. Today, we’re changing that! Hugging Face private storage is partnering with @skypilot_org to make storage as cloud-agnostic as possible, so you can choose GPUs based on performance, reliability, and cost, not because your data is locked into one vendor. More freedom. More competition. Better AI infrastructure. Let’s go!

Congrats to the @zml_ai team on the release of LLMD One thing I absolutely love is that the model layers are loaded directly from the network (from @huggingface OFC), in streaming mode (no local file) which makes a lot of sense for production deployment x.com/zml_ai/status/…

Huggingface put 106 agents on optimising Gemma-4 inference. Watch it go woosh huggingface.co/spaces/agent-c…

Three datasets trending on @huggingface! What dataset would you like to see next? 👀