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1024m  authored a paper about 23 hours ago
Robust and Fine-Grained Detection of AI Generated Texts
gigant  authored a paper 2 days ago
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
gigant  authored a paper 2 days ago
Summarization of Multimodal Presentations with Vision-Language Models: Study of the Effect of Modalities and Structure
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open-acc's activity

burtenshaw 
posted an update about 15 hours ago
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Hacked my presentation building with inference providers, Cohere command a, and sheer simplicity. Use this script if you’re burning too much time on presentations:

🔗 https://github.com/burtenshaw/course_generator/blob/main/scripts/create_presentation.py

This is what it does:
- uses command a to generates slides and speaker notes based on some material.
- it renders the material in remark open format and imports all images, tables, etc
- you can then review the slides as markdown and iterate
- export to either pdf or pptx using backslide

🚀 Next steps are: add text to speech for the audio and generate a video. This should make Hugging Face educational content scale to a billion AI Learners.
clem 
posted an update 1 day ago
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You can now bill your inference costs from all our inference partners (together, fireworks, fal, sambanova, cerebras, hyperbolic,...) to your Hugging Face organization.

Useful to drive more company-wide usage of AI without the billing headaches!
bartowski 
posted an update 3 days ago
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Access requests enabled for latest GLM models

While a fix is being implemented (https://github.com/ggml-org/llama.cpp/pull/12957) I want to leave the models up for visibility and continued discussion, but want to prevent accidental downloads of known broken models (even though there are settings that could fix it at runtime for now)

With this goal, I've enabled access requests. I don't really want your data, so I'm sorry that I don't think there's a way around that? But that's what I'm gonna do for now, and I'll remove the gate when a fix is up and verified and I have a chance to re-convert and quantize!

Hope you don't mind in the mean time :D
merterbak 
posted an update 4 days ago
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OpenAI published 2 benchmark datasets on Hugging Face 🔥
openai/mrcr
openai/graphwalks
MRCR tests how well a model can find the right answer when many similar questions are spread out in a long context. Graphwalks checks if a model can follow steps in a big graph and find the correct nodes by thinking through the structure
thomwolf 
posted an update 4 days ago
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If you've followed the progress of robotics in the past 18 months, you've likely noticed how robotics is increasingly becoming the next frontier that AI will unlock.

At Hugging Face—in robotics and across all AI fields—we believe in a future where AI and robots are open-source, transparent, and affordable; community-built and safe; hackable and fun. We've had so much mutual understanding and passion working with the Pollen Robotics team over the past year that we decided to join forces!

You can already find our open-source humanoid robot platform Reachy 2 on the Pollen website and the Pollen community and people here on the hub at pollen-robotics

We're so excited to build and share more open-source robots with the world in the coming months!
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merterbak 
posted an update 7 days ago
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OpenAI has released BrowseComp an open source benchmark designed to evaluate the web browsing capabilities of AI agents. This dataset comprising 1,266 questions challenges AI models to navigate the web and uncover complex and obscure information. Crafted by human trainers, the questions are intentionally difficult. (unsolvable by another person in under ten minutes and beyond the reach of existing models like ChatGPT with and without browsing and an early version of OpenAI's Deep Research tool.)

Blog Post: https://openai.com/index/browsecomp/
Paper: https://cdn.openai.com/pdf/5e10f4ab-d6f7-442e-9508-59515c65e35d/browsecomp.pdf
Code in simple eval repo: https://github.com/openai/simple-evals
jsulz 
posted an update 9 days ago
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As xet-team infrastructure begins backing hundreds of repositories on the Hugging Face Hub, we’re getting to put on our researcher hats and peer into the bytes. 👀 🤓

IMO, one of the most interesting ideas Xet storage introduces is a globally shared store of data.

When you upload a file through Xet, the contents are split into ~64KB chunks and deduplicated, but what if those same chunks already exist in another repo on the Hub?

If we can detect and reuse them, we skip them as well saving time and bandwidth for AI builders. More on how that works here:
🔗 https://huggingface.co/blog/from-chunks-to-blocks#scaling-deduplication-with-aggregation

Because of this, different repositories can share bytes we store. That opens up something cool - we can draw a graph of which repos actually share data at the chunk level, where:

- Nodes = repositories
- Edges = shared chunks
- Edge thickness = how much they overlap

xet-team/repo-graph

Come find the many BERT islands. Or see how datasets relate in practice, not just in theory. See how libraries or tasks can tie repositories together. You can play around with node size using storage/likes/downloads too.

The result is a super fun visualization from @saba9 and @znation that I’ve already lost way too much time to. I'm excited to see how the networks grow as we add more repositories!
merterbak 
posted an update 9 days ago
jsulz 
posted an update 9 days ago
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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.
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thomwolf 
authored a paper 10 days ago
reach-vb 
authored a paper 10 days ago
thomwolf 
authored a paper 10 days ago
jsulz 
posted an update 11 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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BrigitteTousi 
posted an update 11 days ago
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AI agents are transforming how we interact with technology, but how sustainable are they? 🌍

Design choices — like model size and structure — can massively impact energy use and cost. ⚡💰 The key takeaway: smaller, task-specific models can be far more efficient than large, general-purpose ones.

🔑 Open-source models offer greater transparency, allowing us to track energy consumption and make more informed decisions on deployment. 🌱 Open-source = more efficient, eco-friendly, and accountable AI.

Read our latest, led by @sasha with assists from myself + @yjernite 🤗
https://huggingface.co/blog/sasha/ai-agent-sustainability
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AtAndDev 
posted an update 12 days ago
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Llama 4 is out...
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clem 
posted an update 12 days ago
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Llama 4 is in transformers!

Fun example using the instruction-tuned Maverick model responding about two images, using tensor parallel for maximum speed.

From https://huggingface.co/blog/llama4-release
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jsulz 
posted an update 12 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!
merterbak 
posted an update 12 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
clem 
posted an update 14 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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clem 
posted an update 16 days ago
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Now in Enterprise Hub organizations, you can centralize your billing not only for HF usage but also inference through our inference partners.

Will prevent some headaches for your finance & accounting teams haha (so feel free to share that with them).
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