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Aurélien-Morgan CLAUDON

Aurelien-Morgan
Mi6paulino's profile picture davidaparicio's profile picture thomwolf's profile picture
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https://huggingface.co/retrain-pipelines

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new activity about 4 hours ago
huggingface/HuggingDiscussions:[FEEDBACK] Follow
reacted to thomwolf's post with 🚀 about 24 hours ago
We've kept pushing our Open-R1 project, an open initiative to replicate and extend the techniques behind DeepSeek-R1. And even we were mind-blown by the results we got with this latest model we're releasing: ⚡️OlympicCoder (https://huggingface.co/open-r1/OlympicCoder-7B and https://huggingface.co/open-r1/OlympicCoder-32B) It's beating Claude 3.7 on (competitive) programming –a domain Anthropic has been historically really strong at– and it's getting close to o1-mini/R1 on olympiad level coding with just 7B parameters! And the best part is that we're open-sourcing all about its training dataset, the new IOI benchmark, and more in our Open-R1 progress report #3: https://huggingface.co/blog/open-r1/update-3 Datasets are are releasing: - https://huggingface.co/datasets/open-r1/codeforces - https://huggingface.co/datasets/open-r1/codeforces-cots - https://huggingface.co/datasets/open-r1/ioi - https://huggingface.co/datasets/open-r1/ioi-test-cases - https://huggingface.co/datasets/open-r1/ioi-sample-solutions - https://huggingface.co/datasets/open-r1/ioi-cots - https://huggingface.co/datasets/open-r1/ioi-2024-model-solutions
reacted to julien-c's post with 😎 about 24 hours ago
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.
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Aurelien-Morgan's activity

New activity in huggingface/HuggingDiscussions about 4 hours ago

[FEEDBACK] Follow

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#14 opened over 1 year ago by
victor
reacted to thomwolf's post with 🚀 about 24 hours ago
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1355
We've kept pushing our Open-R1 project, an open initiative to replicate and extend the techniques behind DeepSeek-R1.

And even we were mind-blown by the results we got with this latest model we're releasing: ⚡️OlympicCoder ( open-r1/OlympicCoder-7B and open-r1/OlympicCoder-32B)

It's beating Claude 3.7 on (competitive) programming –a domain Anthropic has been historically really strong at– and it's getting close to o1-mini/R1 on olympiad level coding with just 7B parameters!

And the best part is that we're open-sourcing all about its training dataset, the new IOI benchmark, and more in our Open-R1 progress report #3: https://huggingface.co/blog/open-r1/update-3

Datasets are are releasing:
- open-r1/codeforces
- open-r1/codeforces-cots
- open-r1/ioi
- open-r1/ioi-test-cases
- open-r1/ioi-sample-solutions
- open-r1/ioi-cots
- open-r1/ioi-2024-model-solutions
reacted to julien-c's post with 😎 about 24 hours ago
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2054
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.
replied to fdaudens's post 4 days ago
upvoted a paper 6 days ago
New activity in retrain-pipelines/func_calls_colab 6 days ago

[bot] Conversion to Parquet
#1 opened 7 days ago by
parquet-converter
published a dataset 7 days ago
upvoted an article 12 days ago
upvoted an article 13 days ago
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Article

Trace & Evaluate your Agent with Arize Phoenix

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liked a Space 13 days ago
replied to AdinaY's post 13 days ago
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Seen many astonishing results from people posting examples of this model in action.
reacted to AdinaY's post with 🤯 13 days ago
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2711
Wan2.1 🔥📹 new OPEN video model by Alibaba Wan team!

Model: Wan-AI/Wan2.1-T2V-14B
Demo: Wan-AI/Wan2.1

✨Apache 2.0
✨8.19GB VRAM, runs on most GPUs
✨Multi-Tasking: T2V, I2V, Video Editing, T2I, V2A
✨Text Generation: Supports Chinese & English
✨Powerful Video VAE: Encode/decode 1080P w/ temporal precision
  • 1 reply
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updated a Space 13 days ago
reacted to freddyaboulton's post with 🤗🔥 15 days ago
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3151
Getting WebRTC and Websockets right in python is very tricky. If you've tried to wrap an LLM in a real-time audio layer then you know what I'm talking about.

That's where FastRTC comes in! It makes WebRTC and Websocket streams super easy with minimal code and overhead.

Check out our org: hf.co/fastrtc
upvoted an article 16 days ago
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Article

FastRTC: The Real-Time Communication Library for Python

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liked a Space 16 days ago
reacted to lysandre's post with ❤️ 18 days ago
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5608
SmolVLM-2 and SigLIP-2 are now part of transformers in dedicated releases!

They're added on top of the v4.49.0 release, and can be installed from the following tags: v4.49.0-SmolVLM-2 and v4.49.0-SigLIP-2.

This marks a new beginning for the release process of transformers. For the past five years, we've been doing monthly releases featuring many models (v4.49.0, the latest release, features 9 new architectures).

Starting with SmolVLM-2 & SigLIP2, we'll now additionally release tags supporting new models on a stable branch. These models are therefore directly available for use by installing from the tag itself. These tags will continue to be updated with fixes applied to these models.

Going forward, continue expecting software releases following semantic versioning: v4.50.0 will have ~10 new architectures compared to v4.49.0, as well as a myriad of new features, improvements and bug fixes. Accompanying these software releases, we'll release tags offering brand new models as fast as possible, to make them accessible to all immediately.
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reacted to jsulz's post with 🚀❤️ 20 days ago
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3408
Time flies!

Six months after joining HF中国镜像站 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
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