New interactive viz from AI World showing OpenAI's new open model gpt-oss-120b breaking into the top 50 most liked models of all time on the Hub in under a day! ☄️☄️☄️
Hugging Face just made life easier with the new hf CLI! huggingface-cli to hf With renaming the CLI, there are new features added like hf jobs. We can now run any script or Docker image on dedicated Hugging Face infrastructure with a simple command. It's a good addition for running experiments and jobs on the fly. To get started, just run: pip install -U huggingface_hub List of hf CLI Commands
Main Commands hf auth: Manage authentication (login, logout, etc.). hf cache: Manage the local cache directory. hf download: Download files from the Hub. hf jobs: Run and manage Jobs on the Hub. hf repo: Manage repos on the Hub. hf upload: Upload a file or a folder to the Hub. hf version: Print information about the hf version. hf env: Print information about the environment. Authentication Subcommands (hf auth) login: Log in using a Hugging Face token. logout: Log out of your account. whoami: See which account you are logged in as. switch: Switch between different stored access tokens/profiles. list: List all stored access tokens. Jobs Subcommands (hf jobs) run: Run a Job on Hugging Face infrastructure. inspect: Display detailed information on one or more Jobs. logs: Fetch the logs of a Job. ps: List running Jobs. cancel: Cancel a Job.
We just released TRL v0.20 with major multimodal upgrades!
👁️ VLM support for GRPO (highly requested by the community!) 🎞️ New GSPO trainer (from @Qwen, released last week, VLM-ready) 🐙 New MPO trainer (multimodal by design, as in the paper)
💬 From Replika to everyday chatbots, millions of people are forming emotional bonds with AI, sometimes seeking comfort, sometimes seeking intimacy. But what happens when an AI tells you "I understand how you feel" and you actually believe it?
At Hugging Face, together with @frimelle and @yjernite, we dug into something we felt wasn't getting enough attention: the need to evaluate AI companionship behaviors. These are the subtle ways AI systems validate us, engage with us, and sometimes manipulate our emotional lives.
Here's what we found: 👉 Existing benchmarks (accuracy, helpfulness, safety) completely miss this emotional dimension. 👉 We mapped how leading AI systems actually respond to vulnerable prompts. 👉 We built the Interactions and Machine Attachment Benchmark (INTIMA): a first attempt at evaluating how models handle emotional dependency, boundaries, and attachment (with a full paper coming soon).
With the release of the EU data transparency template this week, we finally got to see one of the most meaningful artifacts to come out of the AI Act implementation so far (haven't you heard? AI's all about the data! 📊📚)
The impact of the template will depend on how effectively it establishes a minimum meaningful transparency standard for companies that don't otherwise offer any transparency into their handling of e.g. personal data or (anti?-)competitive practices in commercial licensing - we'll see how those play out as new models are released after August 2nd 👀
In the meantime, I wanted to see how the template works for a fully open-source + commercially viable model, so I filled it out for the SmolLM3 - which my colleagues at Hugging Face earlier this month 🤗 ICYMI, it's fully open-source with 3B parameters and performance matching the best similar-size models (I've switched all my local apps from Qwen3 to it, you should too 💡)
Verdict: congrats to the European Commission AI Office for making it so straightforward! Fully open and transparent models remain a cornerstone of informed regulation and governance, but the different organizational needs of their developers aren't always properly accounted for in new regulation. In this case, it took me all of two hours to fill out and publish the template (including reading the guidelines) - so kudos for making it feasible for smaller and distributed organizations 🙌 Definitely a step forward for transparency 🔍
Say hello to hf: a faster, friendlier Hugging Face CLI ✨
We are glad to announce a long-awaited quality-of-life improvement: the Hugging Face CLI has been officially renamed from huggingface-cli to hf!
So... why this change?
Typing huggingface-cli constantly gets old fast. More importantly, the CLI’s command structure became messy as new features were added over time (upload, download, cache management, repo management, etc.). Renaming the CLI is a chance to reorganize commands into a clearer, more consistent format.
We decided not to reinvent the wheel and instead follow a well-known CLI pattern: hf <resource> <action>. Isn't hf auth login easier to type and remember?
This is what Hugging Face is all about. We want everyone, hobbyists, researchers and industry alike, to be able to contribute to AI because everyone is affected by it. Kudos to HF's @irenesolaiman for spreading the word!🔥🤗
✨Baidu & MiniMax both launched open foundation models - Baidu: Ernie 4.5 ( from 0.3B -424B ) 🤯 - MiniMax: MiniMax -M1 ( Hybrid MoE reasoning model )
✨Multimodal AI is moving from fusion to full-stack reasoning: unified Any-to-Any pipelines across text, vision, audio, and 3D - Baidu: ERNIE-4.5-VL-424B - Moonshot AI: Kimi-VL-A3B - Alibaba: Ovis-U1 - BAAI: Video-XL-2/OmniGen2 - AntGroup: Ming-Lite-Omni - Chinese Academy of Science: Stream-Omni - Bytedance: SeedVR2-3B - Tencent: Hunyuan 3D 2.1/ SongGeneration - FishAudio: Openaudio-s1-mini
✨Domain specific models are rapidly emerging - Alibaba DAMO: Lingshu-7B (medical MLLM) - BAAI: RoboBrain (Robotics)
✨ So many small models! - OpenBMB: MiciCPM4 ( on device ) - Qwen: Embedding/Reranker (0.6B) - Alibaba: Ovis-U1-3B - Moonshot AI: Kimi-VL-A3B - Bytedance: SeedVR2-3B
reacted to burtenshaw's
post with ❤️about 2 months ago
Inference for generative ai models looks like a mine field, but there’s a simple protocol for picking the best inference:
🌍 95% of users >> If you’re using open (large) models and need fast online inference, then use Inference providers on auto mode, and let it choose the best provider for the model. https://huggingface.co/docs/inference-providers/index
👷 fine-tuners/ bespoke >> If you’ve got custom setups, use Inference Endpoints to define a configuration from AWS, Azure, GCP. https://endpoints.huggingface.co/
This is what efficient AI looks like: Gemma 3n just dropped - a natively multimodal model that runs entirely on your device. No cloud. No API calls.
🧠 Text, image, audio, and video - handled locally. ⚡️Only needs 2B in GPU memory to run 🤯 First sub-10B model to hit 1300+ Elo ✅ Plug-and-play with Hugging Face, MLX, llama.cpp, and more.
Plus: Multilingual out of the box (140+ languages), fine-tune in a free Colab notebook.
It's been a bit since I took a step back and looked at xet-team progress to migrate Hugging Face from Git LFS to Xet, but every time I do it boggles the mind.
A month ago there were 5,500 users/orgs on Xet with 150K repos and 4PB. Today? 🤗 700,000 users/orgs 📈 350,000 repos 🚀 15PB
Meanwhile, our migrations have pushed throughput to numbers that are bonkers. In June, we hit upload speeds of 577Gb/s (crossing 500Gb/s for the first time).
These are hard numbers to put into context, but let's try:
The latest run of the Common Crawl from commoncrawl was 471 TB.
We now have ~32 crawls stored in Xet. At peak upload speed we could move the latest crawl into Xet in about two hours.
We're moving to a new phase in the process, so stay tuned.
This shift in gears means it's also time to roll up our sleeves and look at all the bytes we have and the value we're adding to the community.
I already have some homework from @RichardErkhov to look at the dedupe across their uploads, and I'll be doing the same for other early adopters, big models/datasets, and frequent uploaders (looking at you @bartowski 👀)
Let me know if there's anything you're interested in; happy to dig in!
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reacted to freddyaboulton's
post with 🔥about 2 months ago
