🤖 👾 Thanks so much to BBC News and the stellar Suranjana Tewari for having me on to talk about US <—> China relationship in AI, and what it means for AI ethics.
🚀 Optimum: The Last v1 Release 🚀 Optimum v1.27 marks the final major release in the v1 series. As we close this chapter, we're laying the groundwork for a more modular and community-driven future: - Optimum v2: A lightweight core package for porting Transformers, Diffusers, or Sentence-Transformers to specialized AI hardware/software/accelerators.. - Optimum‑ONNX: A dedicated package where the ONNX/ONNX Runtime ecosystem lives and evolves, faster-moving and decoupled from the Optimum core.
🎯 Why this matters: - A clearer governance path for ONNX, fostering stronger community collaboration and improved developer experience.. - Enable innovation at a faster pace in a more modular, open-source environment.
💡 What this means: - More transparency, broader participation, and faster development driven by the community and key actors in the ONNX ecosystem (PyTorch, Microsoft, Joshua Lochner 👀, ...) - A cleaner, more maintainable core Optimum, focused on extending HF libraries to special AI hardware/software/accelerators tooling and used by our partners (Intel Corporation, Amazon Web Services (AWS), AMD, NVIDIA, FuriosaAI, ...)
🛠️ Major updates I worked on in this release: ✅ Added support for Transformers v4.53 and SmolLM3 in ONNX/ONNXRuntime. ✅ Solved batched inference/generation for all supported decoder model architectures (LLMs).
✨ Big shoutout to @echarlaix for leading the refactoring work that cleanly separated ONNX exporter logic and enabled the creation of Optimum‑ONNX.
💬 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!🔥🤗
Many VLMs claim to process hours of video. But can they follow the story?🤔 Today, we introduce TimeScope: The benchmark that separates true temporal understanding from marketing hype. Let's see how much VLMs really understand!⏳
We test three skills that matter for real-world use: 🔎 Localized Retrieval: Find a specific action. 🧩 Information Synthesis: Piece together scattered clues. 🏃 Fine-Grained Perception: Analyze detailed motion (e.g., count how many times a person swings an axe).
The results are in, and they're revealing. Only Gemini 2.5 pro handles 1-hour-long videos. Performance drops sharply with duration, proving that long video understanding is still challenging. We've found the breaking points—now the community can start fixing them.📈
Want to learn more? TimeScope is 100% open-source. Benchmark your model and help us build the next generation of video AI.
🤔 Why this matters: When we use "free" online AI services, we're often the product. Our conversations become training data, our personal stories get "cooked into" models, and our privacy becomes a commodity. But there's an alternative path forward.
💡 The power shift is real: Local LLMs aren't just about privacy; they're about redistributing AI power away from a handful of tech giants. When individuals, organizations, and even entire nations can run their own models, we're democratizing access to AI capabilities.
🤗 At Hugging Face, we're proud to be at the center of this transformation. Our platform hosts the world's largest library of freely downloadable models, making cutting-edge AI accessible to everyone -- from researchers and developers to curious individuals who want to experiment on their laptops or even smartphones.
The technical barriers that once required $$$ server racks are crumbling. Today, anyone with basic computer skills can download a model, run it locally, and maintain complete control over their AI interactions. No sudden algorithm changes, no data harvesting, no corporate gatekeeping.
This is about technical convenience, but especially about technological sovereignty. When AI power is concentrated in a few hands, we risk creating new forms of digital dependency. Local models offer a path toward genuine AI literacy and independence.
🚀 The future of AI should be open, accessible, and in the hands of the many, not the few. What are your thoughts on AI democratization? Have you experimented with local models yet?
Fine-tune Gemma3n on videos with audios inside with Colab A100 🔥 Just dropped the notebook where you can learn how to fine-tune Gemma3n on images+audio+text at the same time!
keep in mind, it's made for educational purposes 🫡 we do LoRA, audio resampling & video downsampling to be able to train <40GB VRAM stretch modalities and unfreeze layers as you wish! 🙏🏻 merve/smol-vision
We've moved over 20PB from Git LFS to Xet on the Hub without downtime or data loss. Having things "just work" on a migration of this scale is about as good as it gets.
In the early days of joining Hugging Face, we made a few key design decisions: * There would be no "hard cut-over" from Git LFS to Xet * A Xet-enabled repository should be able to contain both Xet and LFS files * Repository migrations from LFS to Xet can run in the background without disrupting downloads or uploads
These were largely driven by our desire to ensure the community could keep working without interruption.
We cover the infrastructure making this all go in this post, specifically: * An integral piece of infrastructure known internally as the Git LFS Bridge * Background content migrations that run around the clock
