Hugging Face
@huggingface
The AI community building the future.
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When @jensenhuang asks for a somersault, you deliver. 🦆
See how AI agents on NVIDIA RTX Spark help bring @huggingface's Microduck from simulation to the real world.
Today we’re releasing two decision models, elephant-1 (9B) and mammoth-1 (27B), trained by the @baselabs team, available for free for one week on our Model APIs.
mammoth-1 is #1# on the Jev Decision Index and elephant-1 is tied for #1# on the Artificial Analysis Decision Index.
It’s a new model, but more importantly, this is an open-source model you can access, deploy, and post-train on Baseten (or wherever you do your best work).
The free week ends 10/27/26 at 11:59 PM PT. Try it and let us know what you think.
Try mammoth-1 here:
Try elephant-1 here:
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It feels pretty fitting for @JensenHuang & @nvidia to end their European tour with Microducks on stage.
Microduck was designed and engineered in Europe (more precisely Bordeaux, France) by the @pollenrobotics and @huggingface teams. With American capital and ambition + Chinese manufacturing genius, it became the most popular robot of 2026 with over 15,000 sold all over the world!
I believe Europe has more opportunity to shine in AI than in any previous tech wave. We intend to prove it with @huggingface @nvidia and the whole open-source AI community in the coming years!
Let's go European AI 🇪🇺🇪🇺🇪🇺
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Here is the Microduck up close
You can program it work DGX spark
This baby duck represents the future of physical Ai
it relies on reinforcement learning to transition AI-trained movements from digital simulations into the real world
Small duck now large robots later 🐥🤖💚
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So many SO-ARMs! 💪
Here is the robotics workshop organized at @NVIDIAGTC Berlin that uses SO-ARM101 and LeRobot to control real and simulated robotic arms. The light box makes it easier to get policies working in any location and light conditions.
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Microducks on stage for Jensen Huang’s famous keynote!
We couldn’t be prouder of our little ones. 🥹
I've made some big improvements to the PDF reader on Papers with Code!
> The AI assistant now cites its sources, full RAG-style
> This is powered by Chandra-OCR 2 by @datalabto which is openly available on @huggingface
> This model returns bounding boxes besides markdown
> Allows you to click on citations and highlight sections
Try it out on any PDF, like
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🧵 I've just released Sentence Transformers v6.2.0!
Featuring model merging with @arcee_ai mergekit, load models faster, & evaluators share work across GPUs automatically.
Plus lower-memory losses, multi-vector improvements & plenty of fixes.
Thread with highlights 👇
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Jensen spent 10 min of his keynote asking microducks to sit, then explained Nvidia's roadmap to them 🥹
Even Microducks need a nap. 😴
"There's no question everybody will become an AI company and we're going to have a thriving AI economy here in Europe."
We built a robotics data engine with Astra-level performance at 125x lower cost. And we are open-sourcing the weights today! Introducing Flora, a suite of open models to parse the physical world and turn raw footage into rich training signals.
Flora already powers our in-house data engine, labeling 200K hours of videos a day on 500 GPUs. We created it to scale the frontier for robot learning. Now you own the weights too.
Flora gives you:
- High-quality, timestamped captions to curate and filter human egocentric and robot data;
- Dense progress rewards for quality control and RL post-training;
- Rubrics scoring for real-world benchmarking
Try it today, links in thread:
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Meet Fibo 2.0 from @bria_ai_ 🔥
The #1# *open weights* model in image editing is supported in Diffusers (day-0, of course)!
1. JSON-native control
2. Pretty good text rendering
3. Comes in two variants: Turbo and Dev
Check out the model here:
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Agent Manager is finally out!
Orchestrate dozens of agents across harnesses (Claude Code, Codex, OpenCode etc.) effortlessly.
Agents communicate and self-organize to help you achieve complex tasks and always keep running on HF spaces.
Try it:
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Muse Spark open weights (even in NVFP4) is an amazing computer use agent
I shipped an e2e tutorial on building an env for a browser agent
(with OpenEnv, Helium and @huggingface Sandboxes)
enjoy 🤗 here's a small visualized run ⤵️
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Local AI 202: Get Massive Speed-ups with Speculative Decoding
Fun and crazy napkin math: Meta, NVIDIA, Alibaba, DeepSeek, Moonshot, Mistral and ~30 other labs have shared close to $1B worth of training compute on the Hugging Face Hub
Free to download
End of 2023 it was under $40M
Wikipedia was written in volunteer hours. This AI age commons is written in GPU-hours and data
(it's a floor btw: about half comes from official compute numbers rest is educated guess and it still miss the ~900k community fine-tunes)
Space with data to explore:
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Curious to keep up to date with auto-research papers?
Just added a category for it on Papers with Code!
Find it here:
friendly reminder: this is on Monday 🤝
→ learn about speculative decoding to speed-up inference
→ if you're a beginner and not interested, just use that does this for you automatically 🦙
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anyone at the Hugging Face thing? Ping mee
