Open R1: Update #4
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lewtun
updated
a
Space
7 minutes ago
Post
1266
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!
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!
- 1 reply
Post
1925
Want AI that truly understands your country's culture? Public institutions are sitting on the next AI revolution - and here's the practical guide to unlock it.
I've had fascinating conversations recently about sovereign AI, with people trying to solve this recurring question: "How do we build AI that truly understands our culture?"
This guide by @evijit and @yjernite brings lots of insights about this question. It's not just about throwing data at models. It's about partnering cultural expertise with tech infrastructure in ways we're just starting to figure out.
An example? The National Library of Norway already has 150+ AI models on Hugging Face. They're not just digitizing books - they're building AI that thinks in Norwegian, understands Norwegian values, and serves Norwegian citizens.
This is sovereign AI in practice: technology that understands your culture, values, and languages.
Especially loved the practical examples on how to do this:
- Real examples from museums, libraries, and government agencies
- How to convert complex documents (PDFs, PowerPoints) into ML-ready formats
- Code templates for processing public data
- Technical recipes for sharing datasets on open platforms
The stakes? Citizens' ability to leverage their collective digital intelligence.
The technology is ready. The infrastructure exists. The guide shows exactly how to use it. What's needed is your cultural expertise to shape these tools.
Check it out: https://huggingface.co/blog/evijit/public-org-data-ai
P.s.: Building cool projects in a public institution? Share them in the comments for others to learn from!
I've had fascinating conversations recently about sovereign AI, with people trying to solve this recurring question: "How do we build AI that truly understands our culture?"
This guide by @evijit and @yjernite brings lots of insights about this question. It's not just about throwing data at models. It's about partnering cultural expertise with tech infrastructure in ways we're just starting to figure out.
An example? The National Library of Norway already has 150+ AI models on Hugging Face. They're not just digitizing books - they're building AI that thinks in Norwegian, understands Norwegian values, and serves Norwegian citizens.
This is sovereign AI in practice: technology that understands your culture, values, and languages.
Especially loved the practical examples on how to do this:
- Real examples from museums, libraries, and government agencies
- How to convert complex documents (PDFs, PowerPoints) into ML-ready formats
- Code templates for processing public data
- Technical recipes for sharing datasets on open platforms
The stakes? Citizens' ability to leverage their collective digital intelligence.
The technology is ready. The infrastructure exists. The guide shows exactly how to use it. What's needed is your cultural expertise to shape these tools.
Check it out: https://huggingface.co/blog/evijit/public-org-data-ai
P.s.: Building cool projects in a public institution? Share them in the comments for others to learn from!
Post
2679
Do chatbots lie about Céline Dion? We now have answers, not speculation.
Ai2 just released OLMoTrace and it's a game-changer for transparency. You can literally see where an AI's responses come from in its training data - in real time.
The demo shows results about Céline. So I tried it out myself! Watch what happens in the video.
For journalists, researchers studying hallucinations and anyone who needs to trust their AI, this is like getting X-ray vision into AI systems. When the model made claims, I could instantly verify them against original sources. When it hallucinated, I could see why.
You can finally 1) understand how LLMs actually work and 2) verify if what they're saying is true. No more blind trust.
This pushes the open data movement to the next level.
👉 Blog post: https://allenai.org/blog/olmotrace
👉 Paper: https://www.datocms-assets.com/64837/1743890415-olmotrace.pdf
P.S.: A word of caution: never use a chatbot as a knowledge base. It's not Google. Better use it with a connection to the internet.
Ai2 just released OLMoTrace and it's a game-changer for transparency. You can literally see where an AI's responses come from in its training data - in real time.
The demo shows results about Céline. So I tried it out myself! Watch what happens in the video.
For journalists, researchers studying hallucinations and anyone who needs to trust their AI, this is like getting X-ray vision into AI systems. When the model made claims, I could instantly verify them against original sources. When it hallucinated, I could see why.
You can finally 1) understand how LLMs actually work and 2) verify if what they're saying is true. No more blind trust.
This pushes the open data movement to the next level.
👉 Blog post: https://allenai.org/blog/olmotrace
👉 Paper: https://www.datocms-assets.com/64837/1743890415-olmotrace.pdf
P.S.: A word of caution: never use a chatbot as a knowledge base. It's not Google. Better use it with a connection to the internet.
- 1 reply
Post
3964
🎨 Designers, meet OmniSVG! This new model helps you create professional vector graphics from text/images, generate editable SVGs from icons to detailed characters, convert rasters to vectors, maintain style consistency with references, and integrate into your workflow.
@OmniSVG
@OmniSVG
- 2 replies
davanstrien
posted
an
update
6 days ago
Post
1484
I've created a v1 dataset (
davanstrien/reasoning-required) and model (
davanstrien/ModernBERT-based-Reasoning-Required) to help curate "wild text" data for generating reasoning examples beyond the usual code/math/science domains.
- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions
My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.
This significantly reduces computation costs while expanding reasoning dataset domain coverage.
- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions
My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.
This significantly reduces computation costs while expanding reasoning dataset domain coverage.
thomwolf
authored
a
paper
7 days ago
lewtun
authored
a
paper
7 days ago
nouamanetazi
authored
a
paper
7 days ago
anton-l
authored
a
paper
7 days ago
cyrilzakka
authored
a
paper
7 days ago
eliebak
authored
a
paper
7 days ago
lvwerra
authored
a
paper
7 days ago
reach-vb
authored
a
paper
7 days ago
loubnabnl
authored
a
paper
7 days ago
andito
authored
a
paper
7 days ago
thomwolf
authored
a
paper
7 days ago
Post
3562
I read the 456-page AI Index report so you don't have to (kidding). The wild part? While AI gets ridiculously more accessible, the power gap is actually widening:
1️⃣ The democratization of AI capabilities is accelerating rapidly:
- The gap between open and closed models is basically closed: difference in benchmarks like MMLU and HumanEval shrunk to just 1.7% in 2024
- The cost to run GPT-3.5-level performance dropped 280x in 2 years
- Model size is shrinking while maintaining performance - Phi-3-mini hitting 60%+ MMLU at fraction of parameters of early models like PaLM
2️⃣ But we're seeing concerning divides deepening:
- Geographic: US private investment ($109B) dwarfs everyone else - 12x China's $9.3B
- Research concentration: US and China dominate highly-cited papers (50 and 34 respectively in 2023), while next closest is only 7
- Gender: Major gaps in AI skill penetration rates - US shows 2.39 vs 1.71 male/female ratio
The tech is getting more accessible but the benefits aren't being distributed evenly. Worth thinking about as these tools become more central to the economy.
Give it a read - fascinating portrait of where AI is heading! https://hai-production.s3.amazonaws.com/files/hai_ai_index_report_2025.pdf
1️⃣ The democratization of AI capabilities is accelerating rapidly:
- The gap between open and closed models is basically closed: difference in benchmarks like MMLU and HumanEval shrunk to just 1.7% in 2024
- The cost to run GPT-3.5-level performance dropped 280x in 2 years
- Model size is shrinking while maintaining performance - Phi-3-mini hitting 60%+ MMLU at fraction of parameters of early models like PaLM
2️⃣ But we're seeing concerning divides deepening:
- Geographic: US private investment ($109B) dwarfs everyone else - 12x China's $9.3B
- Research concentration: US and China dominate highly-cited papers (50 and 34 respectively in 2023), while next closest is only 7
- Gender: Major gaps in AI skill penetration rates - US shows 2.39 vs 1.71 male/female ratio
The tech is getting more accessible but the benefits aren't being distributed evenly. Worth thinking about as these tools become more central to the economy.
Give it a read - fascinating portrait of where AI is heading! https://hai-production.s3.amazonaws.com/files/hai_ai_index_report_2025.pdf
- 3 replies
BrigitteTousi
posted
an
update
8 days ago
Post
2871
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
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
- 1 reply
Post
2332
See that purple banner on the Llama 4 models? It's Xet storage, and this is actually huge for anyone building with AI models. Let's geek out a little bit 🤓
Current problem: AI models are massive files using Git LFS. But with models getting bigger and downloads exploding, we needed something better.
Xet lets you version large files like code, with compression and deduplication, all Git-compatible. That means less bandwidth, faster sharing, and smoother collaboration.
Real numbers: ~25% deduplication on Llama 4 models, hitting ~40% for finetunes.
Scale matters here - the Hub served 2B model downloads in 30 days, Llama models alone at 60M. The upcoming Llama 4 Behemoth has 2T parameters! Xet's chunk-based system was built exactly for this.
This is the kind of engineering that makes the next wave of large models actually usable. Kudos to the team! 🧨
Check out the models collection: meta-llama/llama-4-67f0c30d9fe03840bc9d0164
Current problem: AI models are massive files using Git LFS. But with models getting bigger and downloads exploding, we needed something better.
Xet lets you version large files like code, with compression and deduplication, all Git-compatible. That means less bandwidth, faster sharing, and smoother collaboration.
Real numbers: ~25% deduplication on Llama 4 models, hitting ~40% for finetunes.
Scale matters here - the Hub served 2B model downloads in 30 days, Llama models alone at 60M. The upcoming Llama 4 Behemoth has 2T parameters! Xet's chunk-based system was built exactly for this.
This is the kind of engineering that makes the next wave of large models actually usable. Kudos to the team! 🧨
Check out the models collection: meta-llama/llama-4-67f0c30d9fe03840bc9d0164