it's definitely the future:)
Julien Chaumond PRO
julien-c
AI & ML interests
<3 ML/AI for everyone, building products to propel communities fwd
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about 9 hours ago
huggingface/InferenceSupport:ByteDance/InfiniteYou
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about 9 hours ago
huggingface/InferenceSupport:Qwen/Qwen2.5-Omni-7B
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about 10 hours ago
huggingface/HuggingDiscussions:Qwen/Qwen2.5-Omni-7B
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julien-c's activity
reacted to
jsulz's
post with ❤️🚀
14 days ago
Post
1382
It's finally here ❤️
Build faster than ever with lightning fast upload and download speeds starting today on the Hub ⚡
Xet storage is rolling out access across the Hub - join the waitlist here https://huggingface.co/join/xet
You can apply for yourself, or your entire organization. Head over to your account settings for more information or join anywhere you see the Xet logo on a repository you know.
Have questions? Join the conversation below 👇 or open a discussion on the Xet team page xet-team/README
Build faster than ever with lightning fast upload and download speeds starting today on the Hub ⚡
Xet storage is rolling out access across the Hub - join the waitlist here https://huggingface.co/join/xet
You can apply for yourself, or your entire organization. Head over to your account settings for more information or join anywhere you see the Xet logo on a repository you know.
Have questions? Join the conversation below 👇 or open a discussion on the Xet team page xet-team/README
-
- 6 replies
posted
an
update
16 days ago
Post
2644
Important notice 🚨
For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference – with more coming soon), we've started enabling Pay as you go (=PAYG)
What this means is that you can use those Inference Providers beyond the free included credits, and they're charged to your HF account.
You can see it on this view: any provider that does not have a "Billing disabled" badge, is PAYG-compatible.
For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference – with more coming soon), we've started enabling Pay as you go (=PAYG)
What this means is that you can use those Inference Providers beyond the free included credits, and they're charged to your HF account.
You can see it on this view: any provider that does not have a "Billing disabled" badge, is PAYG-compatible.
- 2 replies
reacted to
jsulz's
post with 🔥❤️🚀
28 days ago
Post
3487
Time flies!
Six months after joining Hugging Face the Xet team is kicking off the first migrations from LFS to our storage for a number of repositories on the Hub.
More on the nitty gritty details behind the migration soon, but here are the big takeaways:
🤖 We've successfully completed the first migrations from LFS -> Xet to test the infrastructure and prepare for a wider release
✅ No action on your part needed - you can work with a Xet-backed repo like any other repo on the Hub (for now - major improvements on their way!)
👀 Keep an eye out for the Xet logo to see if a repo you know is on our infra! See the screenshots below to spot the difference 👇
⏩ ⏩ ⏩ Blazing uploads and downloads coming soon. W’re gearing up for a full integration with the Hub's Python library that will make building on the Hub faster than ever - special thanks to @celinah and @Wauplin for their assistance.
🎉 Want Early Access? If you’re curious and want to test it out the bleeding edge that will power the development experience on the Hub, we’d love to partner with you. Let me know!
This is the culmination of a lot of effort from the entire team. Big round of applause to @sirahd @brianronan @jgodlewski @hoytak @seanses @assafvayner @znation @saba9 @rajatarya @port8080 @yuchenglow
Six months after joining Hugging Face the Xet team is kicking off the first migrations from LFS to our storage for a number of repositories on the Hub.
More on the nitty gritty details behind the migration soon, but here are the big takeaways:
🤖 We've successfully completed the first migrations from LFS -> Xet to test the infrastructure and prepare for a wider release
✅ No action on your part needed - you can work with a Xet-backed repo like any other repo on the Hub (for now - major improvements on their way!)
👀 Keep an eye out for the Xet logo to see if a repo you know is on our infra! See the screenshots below to spot the difference 👇
⏩ ⏩ ⏩ Blazing uploads and downloads coming soon. W’re gearing up for a full integration with the Hub's Python library that will make building on the Hub faster than ever - special thanks to @celinah and @Wauplin for their assistance.
🎉 Want Early Access? If you’re curious and want to test it out the bleeding edge that will power the development experience on the Hub, we’d love to partner with you. Let me know!
This is the culmination of a lot of effort from the entire team. Big round of applause to @sirahd @brianronan @jgodlewski @hoytak @seanses @assafvayner @znation @saba9 @rajatarya @port8080 @yuchenglow
- 1 reply
Also, If I disable the other Inference API's It wont be used? So I wont get billed?
that's correct
We’ll fix it
reacted to
hexgrad's
post with 🚀❤️🔥
about 2 months ago
Post
8451
hexgrad/Kokoro-82M got an upgrade! ⬆️ More voices, more languages,
GitHub: https://github.com/hexgrad/kokoro
PyPI: https://pypi.org/project/kokoro/
Space: hexgrad/Kokoro-TTS
pip install kokoro, and still 82M parameters.GitHub: https://github.com/hexgrad/kokoro
PyPI: https://pypi.org/project/kokoro/
Space: hexgrad/Kokoro-TTS
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- 11 replies
reacted to
florentgbelidji's
post with 🔥
2 months ago
Post
1551
𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗦𝗸𝗶 𝗔𝗱𝘃𝗲𝗻𝘁𝘂𝗿𝗲 𝗝𝘂𝘀𝘁 𝗚𝗼𝘁 𝗦𝗺𝗮𝗿𝘁𝗲𝗿: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗔𝗹𝗽𝗶𝗻𝗲 𝗔𝗴𝗲𝗻𝘁!🏔️⛷️
With the big hype around AI agents these days, I couldn’t stop thinking about how AI agents could truly enhance real-world activities.
What sort of applications could we build with those AI agents: agentic RAG? self-correcting text-to-sql? Nah, boring…
Passionate about outdoors, I’ve always dreamed of a tool that could simplify planning mountain trips while accounting for all potential risks. That’s why I built 𝗔𝗹𝗽𝗶𝗻𝗲 𝗔𝗴𝗲𝗻𝘁, a smart assistant designed to help you plan safe and enjoyable itineraries in the French Alps and Pyrenees.
Built using Hugging Face's 𝘀𝗺𝗼𝗹𝗮𝗴𝗲𝗻𝘁𝘀 library, Alpine Agent combines the power of AI with trusted resources like 𝘚𝘬𝘪𝘵𝘰𝘶𝘳.𝘧𝘳 (https://skitour.fr/) and METEO FRANCE. Whether it’s suggesting a route with moderate difficulty or analyzing avalanche risks and weather conditions, this agent dynamically integrates data to deliver personalized recommendations.
In my latest blog post, I share how I developed this project—from defining tools and integrating APIs to selecting the best LLMs like 𝘘𝘸𝘦𝘯2.5-𝘊𝘰𝘥𝘦𝘳-32𝘉-𝘐𝘯𝘴𝘵𝘳𝘶𝘤𝘵, 𝘓𝘭𝘢𝘮𝘢-3.3-70𝘉-𝘐𝘯𝘴𝘵𝘳𝘶𝘤𝘵, or 𝘎𝘗𝘛-4.
⛷️ Curious how AI can enhance adventure planning? Try the app and share your thoughts: florentgbelidji/alpine-agent
👉 Want to build your own agents? Whether for cooking, sports training, or other passions, the possibilities are endless. Check out the blog post to learn more: https://huggingface.co/blog/florentgbelidji/alpine-agent
Many thanks to @m-ric for helping on building this tool with smolagents!
With the big hype around AI agents these days, I couldn’t stop thinking about how AI agents could truly enhance real-world activities.
What sort of applications could we build with those AI agents: agentic RAG? self-correcting text-to-sql? Nah, boring…
Passionate about outdoors, I’ve always dreamed of a tool that could simplify planning mountain trips while accounting for all potential risks. That’s why I built 𝗔𝗹𝗽𝗶𝗻𝗲 𝗔𝗴𝗲𝗻𝘁, a smart assistant designed to help you plan safe and enjoyable itineraries in the French Alps and Pyrenees.
Built using Hugging Face's 𝘀𝗺𝗼𝗹𝗮𝗴𝗲𝗻𝘁𝘀 library, Alpine Agent combines the power of AI with trusted resources like 𝘚𝘬𝘪𝘵𝘰𝘶𝘳.𝘧𝘳 (https://skitour.fr/) and METEO FRANCE. Whether it’s suggesting a route with moderate difficulty or analyzing avalanche risks and weather conditions, this agent dynamically integrates data to deliver personalized recommendations.
In my latest blog post, I share how I developed this project—from defining tools and integrating APIs to selecting the best LLMs like 𝘘𝘸𝘦𝘯2.5-𝘊𝘰𝘥𝘦𝘳-32𝘉-𝘐𝘯𝘴𝘵𝘳𝘶𝘤𝘵, 𝘓𝘭𝘢𝘮𝘢-3.3-70𝘉-𝘐𝘯𝘴𝘵𝘳𝘶𝘤𝘵, or 𝘎𝘗𝘛-4.
⛷️ Curious how AI can enhance adventure planning? Try the app and share your thoughts: florentgbelidji/alpine-agent
👉 Want to build your own agents? Whether for cooking, sports training, or other passions, the possibilities are endless. Check out the blog post to learn more: https://huggingface.co/blog/florentgbelidji/alpine-agent
Many thanks to @m-ric for helping on building this tool with smolagents!
- 1 reply
replied to
burtenshaw's
post
3 months ago
works for orgs too!
reacted to
burtenshaw's
post with 🤗❤️
3 months ago
Post
3051
People are flexing their end of year stats, so I made this app to show hub stats in a tidy design!
Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap
Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap
-
- 1 reply
yes
@DouglasMourray
imo the Local apps menu should solve part of what you're looking for:
https://www.reddit.com/r/LocalLLaMA/comments/1cwjc4n/hugging_face_adds_an_option_to_directly_launch/
Keen on hearing more feedback!
reacted to
Kseniase's
post with 🔥
3 months ago
Post
2905
TL;DR: The Story of Attention's Development by
@karpathy
Origin: First proposed in 2014 by @Dzmitry Bahdanau, @KyunghyunCho , and Yoshua Bengio in Neural Machine Translation by Jointly Learning to Align and Translate (1409.0473) . Inspired by cognitive processes and later renamed from "RNNSearch."
Key Idea: A data-dependent weighted average for pooling and communication, enabling flexible and powerful neural network connections.
Breakthrough: Bahdanau's "soft search" mechanism (softmax + weighted averaging) solved encoder-decoder bottlenecks in machine translation.
Transformer Revolution: Attention Is All You Need (1706.03762) (2017) by @ashishvaswanigoogle et al. simplified architectures by stacking attention layers, introducing multi-headed attention and positional encodings.
Legacy: Attention replaced RNNs, driving modern AI systems like ChatGPT. It emerged independently but was influenced by contemporaneous work like Alex Graves’s Neural Turing Machines (1410.5401) and Jason Weston’s Memory Networks (1410.3916) .
Attention to history: Jürgen Schmidhuber claims his 1992 Fast Weight Programmers anticipated modern attention mechanisms. While conceptually similar, the term “attention” was absent, and there’s no evidence it influenced Bahdanau, Cho, and Bengio’s 2014 work. Paying attention (!) to history might have brought us to genAI earlier – but credit for the breakthrough still goes to Montreal.
Referenced Papers:
Attention Origin: Neural Machine Translation by Jointly Learning to Align and Translate (1409.0473)
Transformers: Attention Is All You Need (1706.03762)
Alex Graves' Work: Neural Turing Machines (1410.5401), Generating Sequences With Recurrent Neural Networks (1308.0850)
Jason Weston @spermwhale 's Memory Networks (1410.3916)
Sequence to Sequence Learning with Neural Networks (1409.3215) by Ilya Sutskever ( @ilyasut ), Oriol Vinyals, Quoc V. Le
Who else deserves recognition in this groundbreaking narrative of innovation? Let’s ensure every contributor gets the credit they deserve. Leave a comment below 👇🏻🤗
Origin: First proposed in 2014 by @Dzmitry Bahdanau, @KyunghyunCho , and Yoshua Bengio in Neural Machine Translation by Jointly Learning to Align and Translate (1409.0473) . Inspired by cognitive processes and later renamed from "RNNSearch."
Key Idea: A data-dependent weighted average for pooling and communication, enabling flexible and powerful neural network connections.
Breakthrough: Bahdanau's "soft search" mechanism (softmax + weighted averaging) solved encoder-decoder bottlenecks in machine translation.
Transformer Revolution: Attention Is All You Need (1706.03762) (2017) by @ashishvaswanigoogle et al. simplified architectures by stacking attention layers, introducing multi-headed attention and positional encodings.
Legacy: Attention replaced RNNs, driving modern AI systems like ChatGPT. It emerged independently but was influenced by contemporaneous work like Alex Graves’s Neural Turing Machines (1410.5401) and Jason Weston’s Memory Networks (1410.3916) .
Attention to history: Jürgen Schmidhuber claims his 1992 Fast Weight Programmers anticipated modern attention mechanisms. While conceptually similar, the term “attention” was absent, and there’s no evidence it influenced Bahdanau, Cho, and Bengio’s 2014 work. Paying attention (!) to history might have brought us to genAI earlier – but credit for the breakthrough still goes to Montreal.
Referenced Papers:
Attention Origin: Neural Machine Translation by Jointly Learning to Align and Translate (1409.0473)
Transformers: Attention Is All You Need (1706.03762)
Alex Graves' Work: Neural Turing Machines (1410.5401), Generating Sequences With Recurrent Neural Networks (1308.0850)
Jason Weston @spermwhale 's Memory Networks (1410.3916)
Sequence to Sequence Learning with Neural Networks (1409.3215) by Ilya Sutskever ( @ilyasut ), Oriol Vinyals, Quoc V. Le
Who else deserves recognition in this groundbreaking narrative of innovation? Let’s ensure every contributor gets the credit they deserve. Leave a comment below 👇🏻🤗
- 5 replies
replied to
Duskfallcrew's
post
4 months ago
Public storage- y'all ... HF are you nuts?
i can neither confirm nor deny