Aurelien-Morgan (Aurélien-Morgan CLAUDON)

posted an update 7 days ago

Post

3093

The Almighty function-caller

How would you like to build smart GenAi infrastructure ?
Give extensive tools memory to your edge agentic system,
And optimize the resources it takes to run yet a high-performance set of agents ?

We came up with a novel approach to function-calling at scale for smart companies and corporate-grade use-cases.

Read our full-fledged blog article on this here on Hugging Face :
https://huggingface.co/blog/Aurelien-Morgan/the-almighty-function-caller

reacted to danielhanchen's post with 🔥 8 days ago

Post

5682

🦥 Introducing Unsloth Dynamic v2.0 GGUFs!
Our v2.0 quants set new benchmarks on 5-shot MMLU and KL Divergence, meaning you can now run & fine-tune quantized LLMs while preserving as much accuracy as possible.

Llama 4: unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
DeepSeek-R1: unsloth/DeepSeek-R1-GGUF-UD
Gemma 3: unsloth/gemma-3-27b-it-GGUF

We made selective layer quantization much smarter. Instead of modifying only a subset of layers, we now dynamically quantize all layers so every layer has a different bit. Now, our dynamic method can be applied to all LLM architectures, not just MoE's.

Blog with Details: https://docs.unsloth.ai/basics/dynamic-v2.0

All our future GGUF uploads will leverage Dynamic 2.0 and our hand curated 300K–1.5M token calibration dataset to improve conversational chat performance.

For accurate benchmarking, we built an evaluation framework to match the reported 5-shot MMLU scores of Llama 4 and Gemma 3. This allowed apples-to-apples comparisons between full-precision vs. Dynamic v2.0, QAT and standard iMatrix quants.

Dynamic v2.0 aims to minimize the performance gap between full-precision models and their quantized counterparts.

posted an update 8 days ago

Post

645

retrain-pipelines 0.1.2 finally dropped. It comes with a hot Hugging Face Hub integration. Go check it out. We have 2 articles about it coming up. One already fully written so, be on the lookout !
@retrain-pipelines

Also, I'll be volunteering at GOSIM AI Paris 2025. If you're interested in chatting, hmu.

reacted to jsulz's post with 🧠 27 days ago

Post

3102

What does it mean when models share the same bytes?

We've investigated some quants and have seen that a considerable portion of quantizations of the same model share the same bytes and can be deduplicated to save considerable upload time for quantizers on the Hub.

This space where we crack open a repo from @bartowski shows we can get significant dedupe xet-team/quantization-dedup

You can get a sense of why by reading this write-up: https://github.com/bartowski1182/llm-knowledge/blob/main/quantization/quantization.md

But what about finetuned models?

Since going into production the

xet-team has migrated hundreds of repositories on the Hub to our storage layer, including classic "pre-Hub" open-source models like FacebookAI/xlm-roberta-large (XLM-R) from

FacebookAI

XLM-R, introduced in 2019, set new benchmarks for multilingual NLP by learning shared representations across 100 languages. It was then fine-tuned on English, Spanish, Dutch, and German, generating language-specific derivations for each - check out the paper here Unsupervised Cross-lingual Representation Learning at Scale (1911.02116)

These finetunes share much of the same architecture and layout as XLM-R with similar training methods and goals. It makes sense that they would share bytes, but it's still fascinating to see.

We put together a similar space to explore these models to see where they overlap - check it out for yourself xet-team/finetune-dedupe

The darker each block in the heatmap, the more the bytes are shared. Clicking on a repos blocks shows all other repos that share blocks.

1 reply

·

posted an update about 1 month ago

Post

1990

Almost there !
https://test.pypi.org/project/test-010-retrain-pipelines/

reacted to AdinaY's post with 😎 about 2 months ago

Post

1456

Step-Video-TI2V 🔥 text driven Image-to-Video Model released by StepFun_ai

Paper:
Step-Video-TI2V Technical Report: A State-of-the-Art Text-Driven Image-to-Video Generation Model (2503.11251)
Model:
stepfun-ai/stepvideo-ti2v

✨ 30B with MIT license
✨ Up to 102 frames from text + image inputs
✨ Supports motion control and anime-style

reacted to jsulz's post with ❤️ about 2 months ago

Post

1982

If you've been following along with the Xet Team's (

xet-team ) work, you know we've been working to migrate the Hugging Face Hub from Git LFS and to Xet.

Recently, we launched a waitlist to join the movement to Xet (join here! https://huggingface.co/join/xet ) but getting to this point was a journey.

From the initial proof of concept in August, to launching on the Hub internally, to migrating a set of repositories and routing a small chunk of download traffic on the Hub through our infrastructure. Every step of the way has been full of challenges, big and small, and well worth the effort.

Over the past few weeks, with real traffic flowing through our services we’ve tackled some truly gnarly issues (unusual upload/download patterns, memory leaks, load imbalances, and more) and resolved each without major disruptions.

If you're curious about how this sliver of Hub infrastructure looks as we routed traffic through it for the first time (and want a deep dive full of Grafana and Kibana charts 🤓) I have a post for you.

Here's an inside look into the day of our first migrations and the weeks following, where we pieced together solutions in real time.

https://huggingface.co/blog/xet-on-the-hub

reacted to AdinaY's post with 😎 about 2 months ago

Post

2146

FlexWorld 🔥 an open framework that generates 3D scenes from a single image!

Model: GSAI-ML/FlexWorld
Paper: FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis (2503.13265)

✨ 360° rotation & zooming
✨ High quality novel views powered by video-to-video diffusion model
✨ Progressive 3D expansion

2 replies

·

replied to jsulz's post about 2 months ago

The retrain-pipelines org and I joined the waitlist today. Been looking forward to this for some time. Curious to see the outcome. The promise got me hooked from day 1. The tech as presented does have potential.

reacted to jsulz's post with ❤️ about 2 months ago

Post

1447

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

6 replies

·

reacted to thomwolf's post with 🚀 about 2 months ago

Post

2869

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 2 months ago

Post

3732

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.

9 replies

·

replied to fdaudens's post about 2 months ago

You gonna revive the LinkedIn newsletter someday ? Think it was cool 😭😭
https://www.linkedin.com/newsletters/7233909926606053377/

replied to AdinaY's post 2 months ago

Seen many astonishing results from people posting examples of this model in action.

reacted to AdinaY's post with 🤯 2 months ago

Post

2740

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

·

reacted to freddyaboulton's post with 🤗🔥 2 months ago

Post

3271

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

reacted to lysandre's post with ❤️ 2 months ago

Post

6530

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.

1 reply

·

reacted to jsulz's post with 🚀❤️ 2 months ago

Post

3606

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

1 reply

·

Aurélien-Morgan CLAUDON

AI & ML interests

Recent Activity

Organizations

Aurelien-Morgan's activity