williamliao/Qwen3.6-27B-NVFP4-GGUF
Text Generation • 27B • Updated • 4.31k • 12
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IsiiTheFuture
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reacted to Jiaqi-hkust's post with 🚀 22 days ago
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🚀 Introducing Robust-U1: Teaching MLLMs to Self-Recover Corrupted Visual Content
Multimodal Large Language Models (MLLMs) have achieved impressive visual understanding, yet they remain highly brittle under real-world corruptions—noise, blur, compression artifacts, adverse weather.
Standard MLLMs suffer dramatic performance drops, and existing robustness solutions come with fundamental limits: black‑box feature alignment lacks interpretability, while white‑box text reasoning cannot restore the lost pixel‑level visual details. This raises a crucial question:
🧐 Can MLLMs recover corrupted visual content by themselves?
If the answer is yes, we can move beyond merely “compensating” for corruption and instead build a more intrinsic, generalizable form of resilience. Robust-U1 is our answer to that question.
💡 Paper: https://arxiv.org/abs/2606.08063
🔗 Code: github.com/jqtangust/Robust-U1
🌍 Demo: Jiaqi-hkust/Robust-U1
Multimodal Large Language Models (MLLMs) have achieved impressive visual understanding, yet they remain highly brittle under real-world corruptions—noise, blur, compression artifacts, adverse weather.
Standard MLLMs suffer dramatic performance drops, and existing robustness solutions come with fundamental limits: black‑box feature alignment lacks interpretability, while white‑box text reasoning cannot restore the lost pixel‑level visual details. This raises a crucial question:
🧐 Can MLLMs recover corrupted visual content by themselves?
If the answer is yes, we can move beyond merely “compensating” for corruption and instead build a more intrinsic, generalizable form of resilience. Robust-U1 is our answer to that question.
💡 Paper: https://arxiv.org/abs/2606.08063
🔗 Code: github.com/jqtangust/Robust-U1
🌍 Demo: Jiaqi-hkust/Robust-U1
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reacted to sergiopaniego's post with 🔥 26 days ago
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3914
OpenEnv has a new home: github.com/huggingface/OpenEnv
Starting today, it's coordinated by a committee that includes Meta-PyTorch, Reflection, Unsloth, Modal, Prime Intellect, Nvidia, Mercor, Fleet AI, and Hugging Face
frontier labs train their models and their harnesses together. Claude knows Claude Code. GPT-5.5 knows Codex. that's not an accident, it's training. open-source models deserve the same magic, but pulling that off requires infrastructure that belongs to everyone, not one lab
OpenEnv is that layer. one api, any harness, any trainer, any environment
Rewards and training loops stay in TRL, Unsloth, wherever you already work. OpenEnv is the socket they all plug into
Get involved!
Full announcement: https://huggingface.co/blog/openenv-agentic-rl
Starting today, it's coordinated by a committee that includes Meta-PyTorch, Reflection, Unsloth, Modal, Prime Intellect, Nvidia, Mercor, Fleet AI, and Hugging Face
frontier labs train their models and their harnesses together. Claude knows Claude Code. GPT-5.5 knows Codex. that's not an accident, it's training. open-source models deserve the same magic, but pulling that off requires infrastructure that belongs to everyone, not one lab
OpenEnv is that layer. one api, any harness, any trainer, any environment
Rewards and training loops stay in TRL, Unsloth, wherever you already work. OpenEnv is the socket they all plug into
Get involved!
Full announcement: https://huggingface.co/blog/openenv-agentic-rl
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#2 opened about 1 month ago
by IsiiTheFuture
liked 5
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michaelw9999/Qwen3.6-27B-NVFP4-SMALL-MTP-GGUF
27B • Updated • 1.21k • 4
michaelw9999/Qwen3.6-27B-NVFP4-MTP-GGUF
27B • Updated • 67.7k • 38
Jianqiao1/Qwen3.6-27B-NVFP4-MTP-GGUF
27B • Updated • 3.33k • 4
nvidia/LocateAnything-3B
Image-Text-to-Text • 4B • Updated • 1.25M • 2.62k
michaelw9999/Qwen3.5-9B-NVFP4-MTP-GGUF
9B • Updated • 1.09k • 4
reacted to RakshitAralimatti's post with ❤️ about 1 month ago
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501
Reading engineering and research blogs from OpenAI, Anthropic, DeepMind, Meta and others has genuinely leveled up my understanding of AI systems and helped me in my day-to-day work. But keeping track of 20+ sites manually is a pain.
So I built AI Blogs Tracker — a Streamlit app that scrapes the actual blog listing pages (not search) of 20+ top AI companies and surfaces titles, dates, and links in one clean feed. Filter by source, by date, star posts to a reading list, or add your own custom sources.
One click. ~30 seconds. Everything in one place.
🔗 GitHub link - https://github.com/rakshit2020/Tech-Blogs-Tracker-of-Top-AI-Companies-Agent
So I built AI Blogs Tracker — a Streamlit app that scrapes the actual blog listing pages (not search) of 20+ top AI companies and surfaces titles, dates, and links in one clean feed. Filter by source, by date, star posts to a reading list, or add your own custom sources.
One click. ~30 seconds. Everything in one place.
🔗 GitHub link - https://github.com/rakshit2020/Tech-Blogs-Tracker-of-Top-AI-Companies-Agent
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