Alibaba Qwen

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Alibaba Qwen AI

Compiled every national AI strategy in Asia — Vietnam has the most comprehensive standalone law, Japan has no penalties, Korea just eliminated Naver from sovereign LLM competition for using Qwen weights

Compiled a tracker of every national AI strategy in Asia. Headline is that ten major Asian economies now have dedicated AI legislation or comprehensive national strategies, and they're all quite distinct from Western legislation like the EU AI Act or US executive orders. Clear that Asian governments treat AI as infrastructure, not a sector to regulate from a distance. Most national approaches lean promotional (incentives, sandboxes, sovereign LLM funding) rather than punitive (bans, heavy compliance). The exceptions are Vietnam (first standalone AI law in Asia, Dec 2025) and South Korea (Framework AI Act with high-risk-system rules). The major markets that stood out to me: China's open-source-as-industrial-policy framework. ~$98B committed to AI development. Premier Li Qiang declared at WEF 2025 that China's innovation is "open and open-source" and the country is "willing to share indigenous technologies with the world." Derivatives of Alibaba's Qwen are now the largest open-weight model ecosystem on Hugging Face — over 100,000 derivatives (USCC 2026). This is industrial policy through model release, not regulation. Two-tier system: research labs (DeepSeek-style) operate with light governance, consumer-facing apps face stricter rules. Japan's AI Promotion Act (May 2025). No penalties. It's a promotional framework — establishes the AI Strategic Headquarters as a cabinet-level body, mandates a National AI Basic Plan, aligns deployment with "Human-Centred AI Society Principles." Japan's structural problem: only 9% of individuals and 47% of companies were using gen AI as of 2024. The legislation is trying to close adoption gaps via incentives rather than gate behaviour. December 2025 commitment of ¥1 trillion (~$7B) over five years to AI + semiconductors backs it up. Vietnam's AI Law (effective March 2026). Most comprehensive standalone AI law anywhere — 36 articles, three-tier risk classification (low/medium/high), foreign AI providers must appoint a legal representative in Vietnam, max admin fines reach VNĐ 2 billion (~$76K) for orgs with serious violations capped at 2% of preceding year revenue. Plus a National AI Development Fund offering grants/loans/preferential financing, plus regulatory sandboxes for startups. Combined with the Law on Digital Technology Industry covering semiconductors and digital assets, Vietnam now has the most legible AI legal architecture in SEA. What I'm not sure about: how sustainable the "promotional, not punitive" approach is when the next major AI safety incident happens. Japan's framework explicitly has no penalties, and I think that only holds up until something goes wrong. Vietnam's law has teeth but limited enforcement bandwidth. Korea's is the only framework that has both tools and resources to enforce. For people closer to AI policy work — does the Asia approach seem more or less likely to scale globally than EU-style ex-ante rule-making? My read: Asia's bet on incentives + sandboxes + sovereign capability is more aligned with how AI is actually deploying in 2026 than EU rules-based approaches, but the governance gap shows up in the next 24 months. Fuller tracker with country-by-country breakdown: https://digitalinasia.com/2026/04/08/asia-ai-policy-tracker/ submitted by /u/tomsimps0n [link] [comments]

📢 Official Announcement: Qwen Partners with Fireworks AI to Accelerate Access to Qwen Family Models We are pleased to announce a strategic partnership between Qwen and Fireworks AI to deliver optim

📢 Official Announcement: Qwen Partners with Fireworks AI to Accelerate Access to Qwen Family Models We are pleased to announce a strategic partnership between Qwen and Fireworks AI to deliver optimized, production-ready deployment of Qwen's closed weights models via the

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 manipul

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 & https://t.co/DHcuMeRKHg

Forward and backward benchmark results across common configurations. https://t.co/IHMCZRw9AW

Forward and backward benchmark results across common configurations. https://t.co/IHMCZRw9AW

🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. ⚡ 2–3× forward speedup. 2× backward speedup. 💻 Purpose-built for agentic AI on your personal devices. 💡Key ins

🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. ⚡ 2–3× forward speedup. 2× backward speedup. 💻 Purpose-built for agentic AI on your personal devices. 💡Key insights: 1. Gate-driven automatic intra-card CP. 2. Hardware-friendly algebraic https://t.co/4Vhyyw5RuB

🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. ⚡ 2–3× forward speedup. 2× backward speedup. 💻 Purpose-built for agentic AI on your personal devices. 💡Key ins

🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. ⚡ 2–3× forward speedup. 2× backward speedup. 💻 Purpose-built for agentic AI on your personal devices. 💡Key insights: 1. Gate-driven automatic intra-card CP. 2. Hardware-friendly algebraic https://t.co/pA9HCHwFZw

Balanced Performance Across Artistic Styles： More uniform quality across diverse aesthetic domains, effectively reducing style-dependent quality variance.🏖️ https://t.co/3FY3E9VHEp

Balanced Performance Across Artistic Styles： More uniform quality across diverse aesthetic domains, effectively reducing style-dependent quality variance.🏖️ https://t.co/3FY3E9VHEp

Comprehensive Multilingual Text Rendering： Better glyph accuracy, more consistent typography, and cleaner layouts even in complex compositions. It handles mixed-language scenarios more gracefully as w

Comprehensive Multilingual Text Rendering： Better glyph accuracy, more consistent typography, and cleaner layouts even in complex compositions. It handles mixed-language scenarios more gracefully as well. If you’re building posters, UI mockups, ads, or anything text-heavy, this https://t.co/I3Smy3G1Tf

Substantially Elevated Visual Fidelity： We’ve made major improvements in texture detail, lighting coherence, and material realism. This applies across both photorealistic scenes and stylized outputs,

Substantially Elevated Visual Fidelity： We’ve made major improvements in texture detail, lighting coherence, and material realism. This applies across both photorealistic scenes and stylized outputs, so you don’t have to trade realism for style anymore. Yes, this is not a https://t.co/7cT8DC8qRy

Sharper Instruction Following: The model now adheres more closely to prompt semantics, especially for complex compositions. Better handling of multiple objects, spatial relationships and attribute bi

Sharper Instruction Following: The model now adheres more closely to prompt semantics, especially for complex compositions. Better handling of multiple objects, spatial relationships and attribute binding Yes, this is not a screenshot.📲 https://t.co/Bjs1zgT4mF

Qwen-Image-2.0-Pro is now live 🚀🚀 We’ve pushed image quality, multilingual text rendering, and instruction following to a new level, while making performance much more consistent across styles.🌅🌃

Qwen-Image-2.0-Pro is now live 🚀🚀 We’ve pushed image quality, multilingual text rendering, and instruction following to a new level, while making performance much more consistent across styles.🌅🌃 Ranked #9 worldwide for Text-to-Image on @arena 🔗Try it now on ModelScope:

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Available on @ollama ! 🤝🤝

Available on @ollama ! 🤝🤝

⚡️⚡️Run Qwen3.6-27B locally! @UnslothAI

⚡️⚡️Run Qwen3.6-27B locally! @UnslothAI