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jsulz 
posted an update 5 days ago
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2584
We've crossed 1 million repositories backed by Xet storage on Hugging Face! 🚀🚀🚀

You can follow along our progress converting the Hub from Git LFS to Xet at jsulz/ready-xet-go

We have a lot of repos left to migrate, which means I have plenty of time to add more animations 🤪
ariG23498 
posted an update 15 days ago
danieldk 
posted an update 20 days ago
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kernels 0.8.0 is out: https://github.com/huggingface/kernels/releases/tag/v0.8.0

This release refines kernel selection in the kernelize function:

• You can now register kernels for certain CUDA capability ranges.
• Rather than doing exact mating of modes, fall back to other compatible modes. If you are kernelizing for inference, but you only registered a training + torch.compile kernel, it will use that kernel since it is compatible with inference as well.
  • 1 reply
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jsulz 
posted an update 21 days ago
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We've moved over 20PB from Git LFS to Xet on the Hub without downtime or data loss. Having things "just work" on a migration of this scale is about as good as it gets.

Now, we're migrating the rest of the Hub https://huggingface.co/blog/migrating-the-hub-to-xet

But how did we get here?

In the early days of joining Hugging Face, we made a few key design decisions:
* There would be no "hard cut-over" from Git LFS to Xet
* A Xet-enabled repository should be able to contain both Xet and LFS files
* Repository migrations from LFS to Xet can run in the background without disrupting downloads or uploads

These were largely driven by our desire to ensure the community could keep working without interruption.

We cover the infrastructure making this all go in this post, specifically:
* An integral piece of infrastructure known internally as the Git LFS Bridge
* Background content migrations that run around the clock

To skip the wait and join Xet now, sign up here https://huggingface.co/join/xet
danieldk 
posted an update 24 days ago
danieldk 
posted an update 24 days ago
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Kernels 0.7.0 is out: https://github.com/huggingface/kernels/releases/tag/v0.7.0 🚀

This release makes it possible to register multiple kernels for a layer. Do you have a super-fast kernel for inference and another kernel for training? Register them both and kernelize will pick the kernel depending on whether you are going to do training or inference.
jsulz 
posted an update about 1 month ago
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4652
It's been a bit since I took a step back and looked at xet-team progress to migrate Hugging Face from Git LFS to Xet, but every time I do it boggles the mind.

A month ago there were 5,500 users/orgs on Xet with 150K repos and 4PB. Today?
🤗 700,000 users/orgs
📈 350,000 repos
🚀 15PB

Meanwhile, our migrations have pushed throughput to numbers that are bonkers. In June, we hit upload speeds of 577Gb/s (crossing 500Gb/s for the first time).

These are hard numbers to put into context, but let's try:

The latest run of the Common Crawl from commoncrawl was 471 TB.

We now have ~32 crawls stored in Xet. At peak upload speed we could move the latest crawl into Xet in about two hours.

We're moving to a new phase in the process, so stay tuned.

This shift in gears means it's also time to roll up our sleeves and look at all the bytes we have and the value we're adding to the community.

I already have some homework from @RichardErkhov to look at the dedupe across their uploads, and I'll be doing the same for other early adopters, big models/datasets, and frequent uploaders (looking at you @bartowski 👀)

Let me know if there's anything you're interested in; happy to dig in!
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reach-vb 
posted an update about 2 months ago
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Excited to onboard FeatherlessAI on Hugging Face as an Inference Provider - they bring a fleet of 6,700+ LLMs on-demand on the Hugging Face Hub 🤯

Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! 💥

Go, play with it today: https://huggingface.co/blog/inference-providers-featherless

P.S. They're also bringing on more GPUs to support all your concurrent requests!
ariG23498 
posted an update 2 months ago
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🚨 Implement KV Cache from scratch in pure PyTorch. 🚨

We have documented all of our learning while implementing KV Cache to nanoVLM. Joint work with @kashif @lusxvr @andito @pcuenq

Blog: hf.co/blog/kv-cache
  • 1 reply
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danieldk 
posted an update 2 months ago
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We have been working on a project called 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
jsulz 
posted an update 2 months ago
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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.
jsulz 
posted an update 3 months ago
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Heyo @RichardErkhov the xet-team at Hugging face was wondering if you wanted to join the fun and jump over to Xet storage. 🤗

We've been onboarding folks https://huggingface.co/blog/xet-on-the-hub know the backend can scale (Llama 4 and Qwen 3 are on Xet), is great for working with quants (see xet-team/quantization-dedup ), and we're pushing on inviting impactful orgs and users on the Hub. You fit the bill.

We'd love to onboard you, get some feedback, and create some excitement 🎉

The steps are pretty straightforward - join the waitlist at hf.co/join/xet and we'll take care of the rest.

The system is fully backward compatible, so you shouldn't notice a thing. BUT to get the best experience when uploading/downloading, make sure you have hf_xet installed alongside the latest huggingface_hub

What do you think?
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reach-vb 
posted an update 3 months ago
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hey hey @mradermacher - VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! 💥

as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!

in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.

p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage

p.p.s. this is fully backwards compatible so everything will work as it should! 🤗
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jsulz 
posted an update 3 months ago
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At xet-team we've been hard at work bringing a new generation of storage to the Hugging Face community, and we’ve crossed some major milestones:

👷 Over 2,000 builders and nearing 100 organizations with access to Xet
🚀 Over 70,000 model and dataset repositories are Xet-backed
🤯 1.4 petabytes managed by Xet

As we move repos from LFS to Xet for everyone we onboard, we’re pushing our content-addressed store (CAS). Check out the chart below 👇 of CAS hitting up to 150 Gb/s throughput this past week.

All of this growth is helping us build richer insights. We expanded our repo graph, which maps how Xet-backed repositories on the Hub share bytes with each other.

Check out the current network in the image below (nodes are repositories, edges are where repos share bytes) and visit the space to see how different versions of Qwen, Llama, and Phi models are grouped together xet-team/repo-graph

Join the waitlist to get access! https://huggingface.co/join/xet
betodepaola 
in meta-llama/Llama-Guard-4-12B 3 months ago

Removing new lines
#9 opened 3 months ago by
betodepaola

Updating chat template

1
#8 opened 3 months ago by
betodepaola

Delete chat_template.json
#6 opened 3 months ago by
pcuenq

Updating chat template
#7 opened 3 months ago by
betodepaola
betodepaola 
updated a model 3 months ago
pcuenq 
in meta-llama/Llama-Guard-4-12B 3 months ago

Delete chat_template.json
#6 opened 3 months ago by
pcuenq