Energy is a massive constraint for AI but do you even know what energy your chatGPT convos are using?
We're trying to change this by releasing ChatUI-energy, the first interface where you see in real-time what energy your AI conversations consume. Great work from @jdelavande powered by spaces & TGI, available for a dozen of open-source models like Llama, Mistral, Qwen, Gemma and more.
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!
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
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!
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!
- 🧠 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
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.
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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