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Jared Sulzdorf PRO

jsulz

https://www.jsulz.com/

AI & ML interests

Infrastructure, law, policy

Recent Activity

updated a model 2 days ago

jsulz/random-upload-tests

updated a model 2 days ago

jsulz/test

reacted to danieldk's post with 🤗 2 days ago

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

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reacted to danieldk's post with 🤗🔥 2 days ago

Post

1490

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

replied to their post 6 days ago

Hey @RichardErkhov we've begun onboarding you to Xet! 🚀

All new repos you create will be Xet-enabled by default and your existing repos are being migrated as we speak.

Since you have a lot of repos the migration of existing content may take some time. While it's ongoing you may notice instances where a repo is a mixture of LFS and Xet-backed files. This shouldn't be an problem due to how we manage backwards compatibility, but if you have any issues, please let me know here.

For new repos you create, just make sure to follow the instructions here to get the full benefits of using Xet storage.

I'll follow up here once all of your repos have been moved over!

replied to reach-vb's post 6 days ago

Hey @mradermacher sorry for the delay; just wanted to let you know that your migration to Xet should be complete!

Feel free to ping me here if you have any questions or feedback. Excited to hear how it's been going! 🤗

Also, if you have any issues, don't hesitate to open a discussion here https://huggingface.co/spaces/xet-team/README/discussions or an issue on this repo https://github.com/huggingface/xet-core

posted an update 12 days ago

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369

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.

reacted to merve's post with 🤗🔥🚀 16 days ago

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Bu post'u çevirebilirsiniz 🤗💗

6 replies

replied to reach-vb's post 16 days ago

Hey @mradermacher just wanted to let you know that we've begun onboarding you to Xet!

All new repos that you create will be Xet-enabled by default. We are still migrating existing repos, so you will see times when there are a mixture of LFS and Xet files side-by-side, but as the migration progresses everything will become Xet.

As I mentioned in my last message, none of this is an issue due to how we've designed the system for backward compatibility, but if you have any questions or concerns, please let me know. Otherwise, I'll follow up here once all your repos are migrated!

reacted to celinah's post with 😎🤗🚀 16 days ago

Post

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✨ Today we’re releasing Tiny Agents in Python — an MCP-powered Agent in ~70 lines of code 🐍

Inspired by Tiny Agents in JS from @julien-c , we ported the idea to Python and integrated it directly into huggingface_hub — with a built-in MCP Client and a Tiny Agents CLI.

TL;DR: With MCP (Model Context Protocol), you can expose tools like web search or image generation and connect them directly to LLMs. It’s simple — and surprisingly powerful.

pip install "huggingface_hub[mcp]>=0.32.0"

We wrote a blog post where we show how to run Tiny Agents, and dive deeper into how they work and how to build your own.
👉 https://huggingface.co/blog/python-tiny-agents

1 reply

replied to their post 18 days ago

Woohoo!! Thanks for joining ❤️ I'll onboard you from the waitlist soon and follow up here when done.

Will do on the storage side - I'm also quite curious.

If you have any questions or feedback, don't hesitate to ping me here 🤗

posted an update 19 days ago

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2138

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?

4 replies

replied to reach-vb's post 19 days ago

Woohoo! Xet team member here. Thanks for signing up @mradermacher 🤗

The migration process should be very seamless. Because of the way Xet supports backward compatibility - can read about it here if you're interested https://huggingface.co/docs/hub/storage-backends#backward-compatibility-with-lfs - everyone will continue to be able to access the repos before, during, and after the migration.

I'll onboard you from the waitlist this week and then follow up once everything is moved over! If you have any questions, don't hesitate to follow up here and @ me, happy to keep supporting all the work you're doing :)

reacted to reach-vb's post with 🔥👍 20 days 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! 🤗

16 replies

reacted to merve's post with 🔥 about 1 month ago

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A ton of impactful models and datasets in open AI past week, let's summarize the best 🤩 merve/releases-apr-21-and-may-2-6819dcc84da4190620f448a3

💬 Qwen made it rain! They released Qwen3: new dense and MoE models ranging from 0.6B to 235B 🤯 as well as Qwen2.5-Omni, any-to-any model in 3B and 7B!
> Microsoft AI released Phi4 reasoning models (that also come in mini and plus sizes)
> NVIDIA released new CoT reasoning datasets
🖼️ > ByteDance released UI-TARS-1.5, native multimodal UI parsing agentic model
> Meta released EdgeTAM, an on-device object tracking model (SAM2 variant)
🗣️ NVIDIA released parakeet-tdt-0.6b-v2, a smol 600M automatic speech recognition model
> Nari released Dia, a 1.6B text-to-speech model
> Moonshot AI released Kimi Audio, a new audio understanding, generation, conversation model
👩🏻‍💻 JetBrains released Melium models in base and SFT for coding
> Tesslate released UIGEN-T2-7B, a new text-to-frontend-code model 🤩

reacted to BramVanroy's post with 🚀❤️ about 1 month ago

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📢💾 Introducing the Common Crawl Creative Commons Corpus (C5)!

C5 is a large-scale effort to heavily filter web-crawled data, as collected by the non-profit Common Crawl, to only documents that are Creative Commons-licensed such as cc-by-4.0 or public domain cc0. At this stage 150 billion tokens have been collected.

---
📄 data: BramVanroy/CommonCrawl-CreativeCommons
🧰 software: https://github.com/BramVanroy/CommonCrawl-CreativeCommons
---

</> To build C5, HTML pages are scrutinized and all links (if any) to CC licenses are collected, both in regular hyperlinks as well as in metadata. Additional data fields are included such as "was the license found in the head?" or "if multiple licenses were found, do they contradict each other?", which makes further filtering a breeze.

🌐 In this first version of C5, 8 languages are included (Afrikaans, German, English, French, Frysian, Italian, Dutch and Spanish). The language set was limited for two reasons: computational and storage limitations, and a collaboration with GPT-NL, which requested CC data for these languages to train a Dutch-focused, copyright-conscious LLM. In total, this V1 release contains almost 150 thousand documents and 150 billion tokens. This data was not filtered on quality nor deduplicated so that you can decide for yourself how much data to keep. To give some quality indication, a dataset field is present to describe whether a document is included in the FineWeb(-2) datasets, which are of high quality.

🔍 More work needs to be done! Only 7 out of 100+ Common Crawl crawls have been processed so far. That's encouraging because it means there is a lot more Creative Commons data to be collected! But to get there I need help in terms of compute. The current processing was already heavily sponsored by the Flemish Supercomputer but more is needed. If you have the compute available and which to collaborate in an open and transparent manner, please get in touch!

1 reply