Hugging Face

1/ Introducing HIW-500 (Humanoids-in-the-Wild 500): the largest open-source humanoid teleop dataset collected in real homes Built w/ @UnitreeRobotics @huggingface across 12 homes in Southeast Asia, it covers: > 500+ hrs > 23K+ episodes > 10+ TB > 10+ household tasks

lesgo!

HF is quietly becoming the best place to store data, public AND private, especially for brutal domains like robotics and video AI where the files are massive, append-only, and never stop growing. Example? Public robotics datasets exploded from 1,000 in early 2025 to 60,000 today, and there's twice as many private ones. Why? A single robot records at 140 MB/s, all day, forever. That data has to be stored, streamed to GPUs, and shipped back to hardware on repeat. Get it wrong and your GPUs sit idle at 0 MB/s waiting for a dataset to land. Get it right (stream straight from the Hub, pre-warmed cache) and those same GPUs scream along at ~1,326 MB/s, fully fed. 🚀 Here's how LeRobot + Hugging Face Storage Buckets pull it off: #phase-1-collect-and-ingest" target="_blank" rel="nofollow noopener">huggingface.co/spaces/imsteve…

Shot a video this morning to announce a new collaboration with ...

AI agents are easy to demo. Production is a different problem. Enter CUGA. 🦉 An open-source agent harness that lets you focus on building instead of plumbing: ibm.co/6011EO64S

It's raining OCR models again! @Baidu_Inc's Unlimited-OCR is one of the more interesting. You can try it without much effort via a throwaway GPU endpoint on @huggingface Jobs (which recently added port forwarding support) with one command It's OpenAI-compatible, your HF token is the API key, and --timeout makes it self-destruct so you can't leave a GPU running by accident Once it's warm, it's quick and @sgl_project batches concurrent requests, so an agent can boot the model, fire a big async batch at it (say, a whole bucket of newspaper scans), then cancel it. I pointed it at the front page of a 1901 newspaper, "The Commoner" + 6 PDF pages in a single request: tables came back as HTML, equations as LaTeX, figures with captions, reading order preserved across pages. Docs here: #1-start-the-server" target="_blank" rel="nofollow noopener">huggingface.co/datasets/uv-sc…

you can download them now from Huggingface: - huggingface.co/krea/Krea-2-Raw - huggingface.co/krea/Krea-2-Tu…

Live Stream: Welcome to open source AI Lots of new folk are starting out on their journey with open models. Come join our livestream with all your questions about local models, open coding agents, and owning your AI. Thursday 8am PST/ 5pm CEST HF X and YT

Here's how to use Claude Code with GLM-5.2 via @huggingface Inference Providers: 1. Create a token at huggingface.co/settings/tokens (fine-grained, enable "Make calls to Inference Providers") 2. Set the environment variables below 3. ??? 4. Profit

Going to cross 3M public models & 1M public datasets on @huggingface in a few days. Open-source AI is on fire!

🚨 7,600,000 downloads. 100,000 more every day. When I started OpenMed it was one person. This week it trended on GitHub: 2,500+ stars in 7 days, 15+ new contributors who just showed up and started building. Not a founder anymore. A community.

GLM 5.2 continues to impress me. Here's its result on Vending Bench, which measures an AI's performance on running a business over a long time. GLM 5.2 came in second, while costing less than HALF of Opus.

Holy moly almost 1000 followers on Huggingface

- 2016-2024: 🇺🇸leads in open-source AI - 2024-2027: 🇺🇸 leads in general AI & massively benefits - 2024-2026: 🇨🇳 leads in open-source AI - 2026-2030: ?? It's not open-source AI leadership OR general AI leadership, it's open-source AI leadership BEFORE general AI leadership! Open-source AI is the foundation of all AI. It does not only creates more innovation, competition, jobs, and prosperity now, it's also the best (only?) way for a national tech ecosystem to accelerate and ultimately reach the frontier of AI in general. Because open-source AI reduces siloes, shares learning and innovation, intensify emulation which all lead to an acceleration of the local ecosystem progress that no others can match if they're less open and collaborative. Same seems to be true for companies btw, OpenAI/Google started with open science and open-source AI which led to their (and Anthropic who spun off from OAI) domination. Meta could have done the same but decided to change course for some reason.

There's a new rust client for Storage Buckets 🦀 OpenDAL is awesome

To all the newcomers excited to try Opus 4.8-level models at home: welcome to OpenWeightLand! Things work a little differently here than in ClosedSourcistan. Might seem strange at first but you'll quickly get used to it: - there are many providers for the same model and they compete on price and features. - as a result intelligence is abundant and typically much cheaper - you can run the model on-prem, in your region, locally, or with the provider of your choice - you can fine-tune it, modify it, and build businesses on top of it without asking anyone for permission Turns out open weights create markets, not kingdoms. A good central train station to start exploring is the Hugging Face page for GLM-5.2 under "Use this model": -> huggingface.co/zai-org/GLM-5.2 And if you just want to chat with it, it's free on HuggingChat: -> huggingface.co/chat/

Desert island survival list: ✅ Solar panel / battery ✅ 256 GB Mac Studio ✅ GLM 5.2 Civilization in a backpack

Pretty remarkable what’s happening with open weights AI right now. We’re seeing models achieve SOTA results on specific tasks, and getting close to frontier on some areas of coding and other domains. The more that open weights is able to maintain only a marginal gap from the frontier, instead of a widening gap, the more value that can be created with AI. Incidentally, this is actually fine for the frontier labs as well; if we can lower the cost of an overall task then AI usage goes up in general. You’re still likely using frontier models for planning, orchestration, reviewing, and other parts of work. But this is all very good for the applied layer of AI, which is now in a great position to cost optimize workloads with cheaper models or use tailored open models post-trained for specific tasks to improve performance.

Hot take: GLM 5.2 might be the first open/public model that actually changes the enterprise AI cost equation. I played with it for a few hours today after friends told me to stop ignoring it. I expected the usual open model experience: “Cool, but clearly not frontier.” That’s not what I felt. Across a bunch of different tasks, it was the first public open model that didn’t feel obviously worse than the best closed models. Not perfect. Not fully benchmarked. But shockingly capable. The first open model that made me think: “Wait, this could actually replace a meaningful chunk of frontier model usage.” That matters because the economics are wild. Yes, serving it properly is expensive. Something like 8 Nvidia H200s, around $400K to buy or $20K/month to rent. But many enterprises are spending millions per month on Anthropic/OpenAI. If GLM 5.2 is even close enough for 30-50% of workloads, that’s not a model launch. That’s a market disruption.

continuous batching just landed in TRL for GRPO at 64 generations it runs faster and uses less VRAM than plain generate, no vLLM needed how it works and when to reach for it, below