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new activity 25 minutes ago
nyuuzyou/archiveofourown:🚩 Report: Copyright infringement
published a dataset about 13 hours ago
nyuuzyou/rule34world
reacted to Steven10429's post with 👀 about 13 hours ago
I got rejected from llama4. So that means I can use quantinized model without following their TOS. Interesting.
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reacted to Steven10429's post with 👀 about 13 hours ago
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2580
I got rejected from llama4.
So that means I can use quantinized model without following their TOS.
Interesting.
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reacted to jsulz's post with 🔥 2 days ago
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The Llama 4 release - meta-llama/llama-4-67f0c30d9fe03840bc9d0164 - was a big one for the xet-team with every model backed by the storage infrastructure of the future for the Hub.

It's been a wild few days, and especially 🤯 to see every tensor file with a Xet logo next to it instead of LFS.

The attached graph shows requests per second to our content-addressed store (CAS) right as the release went live.

yellow = GETs; dashed line = launch time.

You can definitely tell when the community started downloading 👀

h/t to @rajatarya for the graph, the entire Xet crew to bring us to this point, and special shoutout to Rajat, @port8080 , @brianronan , @seanses , and @znation who made sure the bytes kept flying all weekend ⚡️
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reacted to DawnC's post with 🔥 3 days ago
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New in PawMatchAI🐾 : Turn Your Dog Photos into Art!

I’m excited to introduce a brand-new creative feature — Dog Style Transfer is now live on PawMatchAI!

Just upload your dog’s photo and transform it into 5 artistic styles:
🌸 Japanese Anime
📚 Classic Cartoon
🖼️ Oil Painting
🎨 Watercolor
🌆 Cyberpunk

All powered by Stable Diffusion and enhanced with smart prompt tuning to preserve your dog’s unique traits and breed identity , so the artwork stays true to your furry friend.

Whether you're creating a custom portrait or just having fun, this feature brings your pet photos to life in completely new ways.

And here’s a little secret: although it’s designed with dogs in mind, it actually works on any photo — cats, plush toys, even humans. Feel free to experiment!

Results may not always be perfectly accurate, sometimes your photo might come back looking a little different, or even beyond your imagination. But that’s part of the fun! It’s all about creative surprises and letting the AI do its thing.

Try it now: DawnC/PawMatchAI

If this new feature made you smile, a ❤️ for this space would mean a lot.

#AIArt #StyleTransfer #StableDiffusion #ComputerVision #MachineLearning #DeepLearning
reacted to luigi12345's post with 👍 4 days ago
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🚀 Meta’s Llama 4 Models Now on Hugging Face!

Meta has released Llama 4 Scout and Llama 4 Maverick, now available on Hugging Face:
• Llama 4 Scout: 17B active parameters, 16-expert Mixture of Experts (MoE) architecture, 10M token context window, fits on a single H100 GPU. 
• Llama 4 Maverick: 17B active parameters, 128-expert MoE architecture, 1M token context window, optimized for DGX H100 systems. 

🔥 Key Features:
• Native Multimodality: Seamlessly processes text and images. 
• Extended Context Window: Up to 10 million tokens for handling extensive inputs.
• Multilingual Support: Trained on 200 languages, with fine-tuning support for 12, including Arabic, Spanish, and German. 

🛠️ Access and Integration:
• Model Checkpoints: Available under the meta-llama organization on the Hugging Face Hub.
• Transformers Compatibility: Fully supported in transformers v4.51.0 for easy loading and fine-tuning.
• Efficient Deployment: Supports tensor-parallelism and automatic device mapping.

These models offer developers enhanced capabilities for building sophisticated, multimodal AI applications. 
reacted to jsulz's post with 🔥 6 days ago
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Huge week for xet-team as Llama 4 is the first major model on Hugging Face uploaded with Xet providing the backing! Every byte downloaded comes through our infrastructure.

Using Xet on Hugging Face is the fastest way to download and iterate on open source models and we've proved it with Llama 4 giving a boost of ~25% across all models.

We expect builders on the Hub to see even more improvements, helping power innovation across the community.

With the models on our infrastructure, we can peer in and see how well our dedupe performs across the Llama 4 family. On average, we're seeing ~25% dedupe, providing huge savings to the community who iterate on these state-of-the-art models. The attached image shows a few selected models and how they perform on Xet.

Thanks to the meta-llama team for launching on Xet!
reacted to merterbak's post with 🔥 6 days ago
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Meta has unveiled its Llama 4 🦙 family of models, featuring native multimodality and mixture-of-experts architecture. Two model families are available now:
Models🤗: meta-llama/llama-4-67f0c30d9fe03840bc9d0164
Blog Post: https://ai.meta.com/blog/llama-4-multimodal-intelligence/
HF's Blog Post: https://huggingface.co/blog/llama4-release

- 🧠 Native Multimodality - Process text and images in a unified architecture
- 🔍 Mixture-of-Experts - First Llama models using MoE for incredible efficiency
- 📏 Super Long Context - Up to 10M tokens
- 🌐 Multilingual Power - Trained on 200 languages with 10x more multilingual tokens than Llama 3 (including over 100 languages with over 1 billion tokens each)

🔹 Llama 4 Scout
- 17B active parameters (109B total)
- 16 experts architecture
- 10M context window
- Fits on a single H100 GPU
- Beats Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1

🔹 Llama 4 Maverick
- 17B active parameters (400B total)
- 128 experts architecture
- It can fit perfectly on DGX H100(8x H100)
- 1M context window
- Outperforms GPT-4o and Gemini 2.0 Flash
- ELO score of 1417 on LMArena currently second best model on arena

🔹 Llama 4 Behemoth (Coming Soon)
- 288B active parameters (2T total)
- 16 experts architecture
- Teacher model for Scout and Maverick
- Outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM benchmarks
reacted to clem's post with 🔥 6 days ago
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Llama models (arguably the most successful open AI models of all times) just represented 3% of total model downloads on Hugging Face in March.

People and media like stories of winner takes all & one model/company to rule them all but the reality is much more nuanced than this!

Kudos to all the small AI builders out there!
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reacted to Reality123b's post with 🧠 6 days ago
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Does anyone know how to convert a replit app into a huggingface spaces app?
reacted to ritvik77's post with 🔥 6 days ago
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Hi 🤗HF Community,

I would be incredibly grateful for an opportunity to contribute — in any capacity — and learn alongside researchers here. Is there any possibility I could collaborate or assist with any of your research works ?

I’m happy to support ongoing projects, contribute to data analysis, code, documentation, or anything that adds value.

Thank you for your time and consideration!

Warm regards,
Ritvik Gaur
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reacted to AdinaY's post with 🔥 8 days ago
posted an update 8 days ago
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✈️ Thanks for the interest shown in the FlightAware Photos dataset ( nyuuzyou/flightaware). Seeing its potential, I'm working on expanding it to over 1 million images soon.

---

🎨 Introducing the PaintBerri Hand-Drawn Art Dataset - nyuuzyou/paintberri

A collection of 68,860 digital hand-drawn artworks featuring:

Unique images sourced directly from the paintberri.com online art community.
Rich metadata including creator-provided titles, descriptions, and timestamps.
Image dimensions, thumbnail URLs, and NSFW content flags.
Creator IDs (where available) and unique short identifiers for each piece.

This dataset offers a distinct visual archive capturing diverse styles and subjects from an active online drawing community, suitable for image classification and image-to-text tasks. Opt-out is available for creators wishing to remove their work.
reacted to abidlabs's post with ❤️❤️ 8 days ago
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JOURNEY TO 1 MILLION DEVELOPERS

5 years ago, we launched Gradio as a simple Python library to let researchers at Stanford easily demo computer vision models with a web interface.

Today, Gradio is used by >1 million developers each month to build and share AI web apps. This includes some of the most popular open-source projects of all time, like Automatic1111, Fooocus, Oobabooga’s Text WebUI, Dall-E Mini, and LLaMA-Factory.

How did we get here? How did Gradio keep growing in the very crowded field of open-source Python libraries? I get this question a lot from folks who are building their own open-source libraries. This post distills some of the lessons that I have learned over the past few years:

1. Invest in good primitives, not high-level abstractions
2. Embed virality directly into your library
3. Focus on a (growing) niche
4. Your only roadmap should be rapid iteration
5. Maximize ways users can consume your library's outputs

1. Invest in good primitives, not high-level abstractions

When we first launched Gradio, we offered only one high-level class (gr.Interface), which created a complete web app from a single Python function. We quickly realized that developers wanted to create other kinds of apps (e.g. multi-step workflows, chatbots, streaming applications), but as we started listing out the apps users wanted to build, we realized what we needed to do:

Read the rest here: https://x.com/abidlabs/status/1907886
reacted to clem's post with 🔥 10 days ago
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Before 2020, most of the AI field was open and collaborative. For me, that was the key factor that accelerated scientific progress and made the impossible possible—just look at the “T” in ChatGPT, which comes from the Transformer architecture openly shared by Google.

Then came the myth that AI was too dangerous to share, and companies started optimizing for short-term revenue. That led many major AI labs and researchers to stop sharing and collaborating.

With OAI and sama now saying they're willing to share open weights again, we have a real chance to return to a golden age of AI progress and democratization—powered by openness and collaboration, in the US and around the world.

This is incredibly exciting. Let’s go, open science and open-source AI!
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posted an update 11 days ago
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✈️ FlightAware Photos Dataset - nyuuzyou/flightaware

Collection of approximately 197,718 aviation photographs featuring:
- High-quality aircraft images across multiple sizes and formats
- Comprehensive metadata including aircraft registrations, types, and photographer information
- View counts, ratings, and submission timestamps for each photo
- Rich classification data preserving original titles, descriptions, and photographer badges

This dataset offers a unique visual archive of aircraft spanning commercial, military, and private aviation captured by FlightAware's community of photographers under CC BY-NC-SA 3.0 license.
replied to their post 13 days ago
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A lot of popular repositories from major companies haven't gotten Xet support yet, so we just have to wait and see
reacted to Keltezaa's post with 🔥 14 days ago
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Dear HF Staff and pro Users.

Why did you remove the "Regen" feature from the ZeroGPU feature?
Is this an error or intended?

I am now limited to 13 images per 24 hrs. Using my space.
When I upgraded to Pro, it was exclusively for the 5x more usage and the faster regen.

The reason I spend my hard earned money on your site was for this feature.
This is totally unacceptable.

########
Other Pro Users please reply and tag others
IF YOU AGREE or DISAGREE.
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@Always-cheating ,@anonymous111110987654321 ,@Arshili @bedspirit @blackedguy @John6666 ,@DavidBaloches @E-07 ,@f-14 @mindfulpeoples @multimodalart
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reacted to clem's post with 🤗 14 days ago
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What's this cool purple banner haha 😶😶😶
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reacted to JLouisBiz's post with 🤗 15 days ago
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I would like to recommend that everyone consider paying $9 for access through Hugging Face; their services provide so many benefits, it's worth both our attention and gratitude.

Click and go:
https://huggingface.co/subscribe/pro
posted an update 15 days ago