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erinys

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reacted to maxiw's post with 🤗❤️ 4 days ago
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4279
I was curious to see what people post here on HF so I created a dataset with all HF Posts: maxiw/hf-posts

Some interesting stats:

Top 5 Authors by Total Impressions:
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@merve : 171,783 impressions (68 posts)
@fdaudens : 135,253 impressions (81 posts)
@singhsidhukuldeep : 122,591 impressions (81 posts)
@akhaliq : 119,526 impressions (78 posts)
@MonsterMMORPG : 112,500 impressions (45 posts)

Top 5 Users by Number of Reactions Given:
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@osanseviero : 1278 reactions
@clem : 910 reactions
@John6666 : 899 reactions
@victor : 674 reactions
@samusenps : 655 reactions

Top 5 Most Used Reactions:
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❤️: 7048 times
🔥: 5921 times
👍: 4856 times
🚀: 2549 times
🤗: 2065 times
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posted an update about 1 month ago
reacted to jsulz's post with 🔥 about 1 month ago
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1624
The Hugging Face Hub hosts over 1.5M Model, Dataset, and Space repositories. To scale to 10M+, the XetHub team (https://huggingface.co/xet-team) is replacing Git LFS with a new technology that improves storage and transfer capabilities with some future developer experience benefits to boot.

Thanks to @yuchenglow and @port8080 (for their analysis covering LFS usage from March 2022–Sept 2024), we now have insights into what we’re storing. Check out the Gradio app to explore:
- Storage growth over time
- File types over all repositories
- Some simple optimizations we're investigating

xet-team/lfs-analysis
replied to their post about 2 months ago
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This is great feedback @John6666 - and I've seen your suggestions in the other thread as well. As a non-ML engineer myself, it's been really interesting to explore HF with fresh eyes! We're doing some early exploration on HF understandability and discoverability in our team - would you be open to chatting sometime about potential approaches? We'd love to get your feedback!

posted an update about 2 months ago
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1926
We shut down XetHub today after almost 2 years. What we learned from launching our Git-scaled product from scratch:
- Don't make me change my workflow
- Data inertia is real
- ML best practices are still evolving

Closing the door on our public product lets us focus on our new goal of scaling HF Hub's storage backend to improve devX for a larger community. We'd love to hear your thoughts on what experiences we can improve!

Read the full post: https://xethub.com/blog/shutting-down-xethub-learnings-and-takeaways
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reacted to merve's post with 🔥 about 2 months ago
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3974
If you feel like you missed out for ECCV 2024, there's an app to browse the papers, rank for popularity, filter for open models, datasets and demos 📝

Get started at ECCV/ECCV2024-papers
posted an update about 2 months ago
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1369
We did a thing! Eight weeks into our Hugging Face tenure, we can demo a round-trip of Xet-backed files from our local machine to a prod Hugging Face S3 bucket and back. 🚀

It’s been exciting to dive into how the Hub is built and design our steel thread through the infrastructure. Now that the thread is up, we can kick off project Capacious Extremis 🪄 to add all the other goodies: authentication, authorization, deduplication, privacy, and more.

What does this mean for you? You’re one step closer to ⚡ faster downloads, uploads, and iterative development on Hugging Face Hub!
This is our first step toward replacing Git LFS as the Hub's storage backend: https://huggingface.co/blog/xethub-joins-hf

Check out the demo on LinkedIn to see the transfer in action: https://www.linkedin.com/posts/annux_youve-heard-of-blue-steel-but-have-activity-7245062126535405568-3cvJ
reacted to jsulz's post with 🚀 about 2 months ago
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1816
In August, the XetHub team joined Hugging Face
- https://huggingface.co/blog/xethub-joins-hf - and we’ve been rolling up our sleeves to bring the best of both worlds together. We started with a deep dive into the current state of files stored with Git LFS on the Hub.

Getting this information was no small feat. We had to:
* Analyze a complete database dump of all repositories and files stored in Git LFS across Hugging Face.
* Parse through metadata on file sizes and types to accurately map the storage breakdown across Spaces, Models, and Datasets.

You can read more about the findings (with some jaw-dropping stats + charts) here https://www.linkedin.com/feed/update/urn:li:activity:7244486280351285248