What does it take to run @huggingface Spaces on Arm64? With Arm64 powering modern dev machines and 1M+ Hugging Face Spaces out there, compatibility matters. This session shows how to spot hidden x86 dependencies and fix them quickly with MCP tools. Watch →

Last week, we had a very playful but yet efficient off-site between @huggingface and @ggml_org . We brainstormed many UI/UX related subjects, many more to tackle in near future! It's a pleasure to meet everyone IRL and visit the beautiful capital of Bulgaria 🌹 @julien_c @victormustar @ggerganov and Alek

This is where we are right now. And i’m not gonna lie it feels pretty magical 🧚‍♀️ Qwen3.6 27B running inside of Pi coding agent via Llama.cpp on the MacBook Pro For non-trivial tasks on the @huggingface codebases, this feels very, very close to hitting the latest Opus in Claude Code, or whatever shiny monopolistic closed source API of the day is. In full airplane mode. Most people haven’t realized this yet. If you have, it means you have a huge headstart to what I call the second revolution of AI. Powerful local models for efficiency, security, privacy, sovereignty 🔥

Today we’re releasing Qwen-Scope 🔭, an open suite of sparse autoencoders for the Qwen model family. It turns SAE features into practical tools： 🎯 Inference — Steer model outputs by directly manipulating internal features, no prompt engineering needed 📂 Data — Classify & synthesize targeted data with minimal seed examples, boosting long-tail capabilities 🏋️ Training — Trace code-switching & repetitive generation back to their source, fix them at the root 📊 Evaluation — Analyze feature activation patterns to select smarter benchmarks and cut redundancy We hope the community uses Qwen-Scope to uncover new mechanisms inside Qwen models and build applications beyond what we explored.Excited to see what you build! 🚀 🔗🔗 Blog: HuggingFace: ModelScope: Technical Report:

Day 2 is done, and that’s a wrap on #PyTorchConferenceEU# 🇫🇷🔥 A great close to two excellent days at Station F: deep technical content, practical engineering, open collaboration, and an ecosystem with real momentum. This morning I had the chance to deliver the @PyTorch Foundation CTO keynote, focused on ecosystem growth, new working groups, certification, and what it takes to make open source AI easier to build, govern, and scale (OpenMVG). Big Day 2 announcement 🚨 @HuggingFace Safetensors is joining the PyTorch Foundation as a hosted project. That is a meaningful step forward for secure model distribution and trusted AI deployment. (PyTorch) Also great to see keynotes and sessions featuring: • Léonard Hussenot from @GoogleDeepMind's compelling talk on Gemma 4 Edward Yang (@Meta) on PyTorch updates • @LysandreJik (@huggingface) on the Hub as infrastructure and safe, performant model distribution • an outstanding mix of speakers and contributors from @AMD, @RedHat, @NVIDIA, @Google, @IBM, and @LightningAI (PyTorch Conference 2026) Thank you to everyone who made the first PyTorch Conference Europe such a strong debut. The ecosystem is growing. The Foundation is growing. And the community is building what’s next. 🚀 #PyTorch# #OpenSourceAI# #Safetensors# #huggingface#

If you love fine-tuning open-source models (like me), then listen. > Start with 1B, 2B, 4B, and 8B models. (Don't start with a 27B model or bigger at first.) > Use WebGPU providers. I use Google Colab Pro for any model smaller than 9B. A single A100 80GB costs around $0.60/hr, which is cheap. Enough for small models. > Don’t buy GPUs unless you fine-tune 7 to 10 models. You'll understand the nitty-gritty in the process. > Use Codex 5.5 × DeepSeek v4 Pro to create datasets. Codex to plan, DeepSeek v4 Pro to generate rows. > Use Unsloth's instruct models as a base from Hugging Face. Yes, there are others too, but Unsloth also provides fast fine-tuning notebooks. > Use Unsloth's fine-tuning notebooks as a reference. Paste them into Codex, and Codex will write a custom notebook with the configs you need. > Spend 1 day learning about: - SFT (supervised fine-tuning) - RL training (GRPO, DPO, PPO, etc.) - LoRA / QLoRA training - Quantization and types - Local inference engines (llama.cpp) - KV cache and prompt cache > Just get started. Claude, Codex, and ChatGPT can design a step-by-step plan for how you can fine-tune your first AI model. Future tech is moving toward small 5B to 15B ELMs (Expert Language Models) rather than general 1T LLMs. So fine-tuning is an important skill that anyone can acquire today. Tune models, test them, use them. Then fine-tune for companies and make a career out of it. (Companies pay $50k+ to fine-tune models on their data so they can get personalized AI models.) Shoot your questions below. I'll be sharing in-depth raw findings about this topic in the coming days.

Smart Studio: Self-host the latest AI 🚀 Stop jumping between platforms. Everything you need to test and serve models is now in one place: ✅ Instant SOTA Access: Run Qwen3.6-Max, DeepSeek-v4, and the latest models the moment they drop. ✅ Full Multimodal Support: Access multimodal and Image & Video generation models. ✅ Visual Model Lab: Compare open vs. closed-source outputs side-by-side. ✅ HF-to-API in Minutes: Turn Hugging Face model into live API in minutes. 🔗: #AlibabaCloud# #SmartStudio# #ModelExploration# #GenAI# #AInnovation# #LLM#

👋Introducing the #Meissonic# & #Menotico# model series, a collaborative pre-trained image generation model by Collov Labs! This marks another step forward in our research journey, following our work on #3D# Prior Image Synthesis and D-edit. In collaboration with University of California, Berkeley AI Research and Stanford University , this series features highly efficient image generation models based on the MIM (Masked Image Modeling) architecture. Designed to surpass traditional diffusion models (such as SDXL), these models enable efficient generation of 1024x1024 and 512x512 images even at the edge. The #Meissonic# & #Menotico# series represents a milestone for Collov Labs as we pursue advancements in hashtag#spatialdesignintelligence#. Our goal is to create stunning, efficient text-to-image models trained with minimal data and parameters, paving the way for cost-effective pre-training that delivers enterprise-grade solutions. By collaborating with clients in real estate and home decor, Collov offers customizable, compliant, and cost-controlled pre-training solutions. Through our post-training pipeline, MIM models excel in downstream tasks, including: 🖼 Precision: Perfectly scaled image generation 💡 Visual Memory: Retaining 3D and 2D concepts with fidelity, like furniture, cabinetry, and flooring textures 🚀 Spatial Reasoning: Intelligent spatial arrangement and drag-and-drop editing capabilities We’re thrilled to contribute to the open-source community, sparking discussions on YouTube and Reddit from Japan, Korea, the U.S., India, the Middle East, and the U.K. Check out this YouTube tutorial on deploying these models efficiently at the edge: We invite you to explore and discuss our work! Our code and full paper are now available: 💻 Hugging Face: 📄 Full Paper: