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Hugging Face
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stevhliu
updated
a
dataset
31 minutes ago
hlarcher
updated
a
Space
about 16 hours ago
[FEEDBACK] Inference Playground
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6
41
#1 opened 9 months ago
by
victor
Post
698
We have been working on a project called
We plan to give kernels a more proper introduction soon. But for those who have been following along, we are happy to announce a new release:
- New layer API with
- Experimental support for loading Apple Silicon Metal 🤘 Kernels.
- Generate wheels from Hub kernels for legacy deployments.
Full release notes here: https://github.com/huggingface/kernels/releases/tag/v0.6.0
kernels. kernels makes it possible to load compute kernels directly from the Hub! 🚀We plan to give kernels a more proper introduction soon. But for those who have been following along, we are happy to announce a new release:
- New layer API with
torch.compile support.- Experimental support for loading Apple Silicon Metal 🤘 Kernels.
- Generate wheels from Hub kernels for legacy deployments.
Full release notes here: https://github.com/huggingface/kernels/releases/tag/v0.6.0
Post
664
If you didn't yet, you should read the technical report for SmolVLA, published yesterday by the Hugging Face robotics team!
➡️ Amongst other ideas, it introduces "Async inference" to boost their robot actions.
Robots have a problem: performing the actions takes time (Unlike agents where action executions are near-instant!)
Most often, robots wait until they've finished performing actions to start thinking about hte next steps. This is a huge latency cost!
So the team decided to have the PolicyServer (aka the"thinking" part) restart early : instead of waiting for the n observations they just sent to be completed, they gather the observations after k < n steps, and start preparing the next actions based on that while the steps are running until n, to directly send their next steps.
➡️ This boosted robot throughput by ~30%! (nearly 2× tasks per time window).
gg @cadene and team! 👏
Report here: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics (2506.01844)
➡️ Amongst other ideas, it introduces "Async inference" to boost their robot actions.
Robots have a problem: performing the actions takes time (Unlike agents where action executions are near-instant!)
Most often, robots wait until they've finished performing actions to start thinking about hte next steps. This is a huge latency cost!
So the team decided to have the PolicyServer (aka the"thinking" part) restart early : instead of waiting for the n observations they just sent to be completed, they gather the observations after k < n steps, and start preparing the next actions based on that while the steps are running until n, to directly send their next steps.
➡️ This boosted robot throughput by ~30%! (nearly 2× tasks per time window).
gg @cadene and team! 👏
Report here: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics (2506.01844)
[Feedback] Hugging Face x Google Colab
🤗
❤️
2
#22 opened 1 day ago
by
reach-vb
lysandre
updated
a
dataset
1 day ago
ariG23498
updated
a
dataset
2 days ago
derekl35
updated
a
dataset
2 days ago
Post
1072
The Gradio x Agents x MCP hackathon keeps growing! We now have more $1,000,000 in credit for participants and and >$16,000 in cash prizes for winners.
We've kept registration open until the end of this week, so join and let's build cool stuff together as a community: ysharma/gradio-hackathon-registration-2025
We've kept registration open until the end of this week, so join and let's build cool stuff together as a community: ysharma/gradio-hackathon-registration-2025
Post
222
This is the story of how open source AI created a $3M business for a news company:
Clare Spencer tells on the GAIN blog how a Danish software engineer found OpenAI's Whisper model and turned it into Good Tape. It's now generating $3M ARR for news service Zetland.
Great playbook on how to build a good product:
- This idea came from a software engineer, Jakob Steinn, who was not only able to spot a new model, but also listen to feedback from his colleagues in the newsrooms (he thought they would use it for translation, but they were more interested in transcription in Danish)
- They built iteratively: they went from running the model in the terminal to a notebook to a full-fledged web interface
- They didn't just wrap the API. They rebuilt the transcription engine from scratch, moved it to TPUs for 45-second processing of hour-long audio, and added EU-based data sovereignty
Now Good Tape has 2.5M users worldwide, with only 30-35% being journalists.
Small languages (Danish, Finnish, Croatian, Hebrew) were underserved by existing tools - suddenly there's a "very very big market" when you put them together.
This shows how open source AI can solve real workflow problems and create sustainable businesses. Sometimes the best opportunities emerge from solving your own daily problems.
Worth a read: https://generative-ai-newsroom.com/how-a-danish-news-service-made-a-profit-with-its-transcription-tool-285bc05b7cf9
Clare Spencer tells on the GAIN blog how a Danish software engineer found OpenAI's Whisper model and turned it into Good Tape. It's now generating $3M ARR for news service Zetland.
Great playbook on how to build a good product:
- This idea came from a software engineer, Jakob Steinn, who was not only able to spot a new model, but also listen to feedback from his colleagues in the newsrooms (he thought they would use it for translation, but they were more interested in transcription in Danish)
- They built iteratively: they went from running the model in the terminal to a notebook to a full-fledged web interface
- They didn't just wrap the API. They rebuilt the transcription engine from scratch, moved it to TPUs for 45-second processing of hour-long audio, and added EU-based data sovereignty
Now Good Tape has 2.5M users worldwide, with only 30-35% being journalists.
Small languages (Danish, Finnish, Croatian, Hebrew) were underserved by existing tools - suddenly there's a "very very big market" when you put them together.
This shows how open source AI can solve real workflow problems and create sustainable businesses. Sometimes the best opportunities emerge from solving your own daily problems.
Worth a read: https://generative-ai-newsroom.com/how-a-danish-news-service-made-a-profit-with-its-transcription-tool-285bc05b7cf9
andito
authored
a
paper
3 days ago
pepijn223
authored
a
paper
3 days ago
fracapuano
authored
a
paper
3 days ago
cfahlgren1
posted
an
update
3 days ago
Post
222
Really nice to see AllenAI drop the Reward-Bench-2 dataset and leaderboard from their new paper all on the hub! 👏
allenai/reward-bench
allenai/reward-bench-2
allenai/reward-bench-2-results
Great work @natolambert , allenai and others!! 🤗
allenai/reward-bench
allenai/reward-bench-2
allenai/reward-bench-2-results
Great work @natolambert , allenai and others!! 🤗
sumuks
authored
a
paper
4 days ago
jeffboudier
posted
an
update
8 days ago
Post
2412
👏 Congrats
@jinanz
adding TimesFM times series forecasting to Transformers!
Learn how to use TimesFM in this blog post by the Nutanix team: https://huggingface.co/blog/Nutanix/introducing-timesfm-for-time-series-forcasting
Learn how to use TimesFM in this blog post by the Nutanix team: https://huggingface.co/blog/Nutanix/introducing-timesfm-for-time-series-forcasting
Post
2850
🎵 Dream come true for content creators! TIGER AI can extract voice, effects & music from ANY audio file 🤯
This lightweight model uses frequency band-split technology to separate speech like magic. Kudos to @fffiloni for the amazing demo! fffiloni/TIGER-audio-extraction
This lightweight model uses frequency band-split technology to separate speech like magic. Kudos to @fffiloni for the amazing demo! fffiloni/TIGER-audio-extraction
Post
308
Just dropped two bigger physics datasets (both on photonics)!
NUMBA 1: SIB-CL
This dataset of Surrogate- and Invariance-Boosted Contrastive Learning (SIB-CL) datasets for two scientific problems:
- PhC2D: 2D photonic crystal density-of-states (DOS) and bandstructure data.
- TISE: 3D time-independent Schrödinger equation eigenvalue and eigenvector solutions.
NUMBA2: 2D Photonic Topology
Symmetry-driven analysis of 2D photonic crystals: 10k random unit cells across 11 symmetries, 2 polarizations, 5 contrasts. Includes time-reversal breaking cases for 4 symmetries at high contrast.
Check them out: cgeorgiaw/sib-cl & cgeorgiaw/2d-photonic-topology
NUMBA 1: SIB-CL
This dataset of Surrogate- and Invariance-Boosted Contrastive Learning (SIB-CL) datasets for two scientific problems:
- PhC2D: 2D photonic crystal density-of-states (DOS) and bandstructure data.
- TISE: 3D time-independent Schrödinger equation eigenvalue and eigenvector solutions.
NUMBA2: 2D Photonic Topology
Symmetry-driven analysis of 2D photonic crystals: 10k random unit cells across 11 symmetries, 2 polarizations, 5 contrasts. Includes time-reversal breaking cases for 4 symmetries at high contrast.
Check them out: cgeorgiaw/sib-cl & cgeorgiaw/2d-photonic-topology
Post
349
With major model families like
Qwen
and all of Llama from
meta-llama
on Xet, the time is right for new users and organizations to say goodbye to LFS on the Hub.
Xet is now the default storage for new AI builders 🚀 🚀 🚀
Just sign up for an account, create a new model or dataset, pip install huggingface_hub and you're off to the races!
Read more here https://huggingface.co/changelog/xet-default-for-new-users
And for everyone with existing repositories, just sign up here https://huggingface.co/join/xet - we'll migrate all existing repositories to Xet and all new repos you create will be Xet-backed by default.
Qwen
and all of Llama from
meta-llama
on Xet, the time is right for new users and organizations to say goodbye to LFS on the Hub. Xet is now the default storage for new AI builders 🚀 🚀 🚀
Just sign up for an account, create a new model or dataset, pip install huggingface_hub and you're off to the races!
Read more here https://huggingface.co/changelog/xet-default-for-new-users
And for everyone with existing repositories, just sign up here https://huggingface.co/join/xet - we'll migrate all existing repositories to Xet and all new repos you create will be Xet-backed by default.