🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

It's great to see that Qwen3 works out-of-the-box with OpenHands! We've heard from community members that Qwen3-30B-A3B also works quite well, and achieves reasonable speed (50-60 tokens/s) even on a Mac M1 processor.

We’re thrilled to announce the launch of Qwen 3 on Together AI. Qwen3 235B A22B, a state of the art hybrid reasoning model, is now available on the Together API. It excels in tool calling, coding, multi-lingual tasks, math, and general tasks. Link & details below!

We’re excited to announce the release of Qwen2.5-Omni-3B, enabling developers with lightweight GPU accessibility! 🔹 Compared to Qwen2.5-Omni-7B model, the 3B version achieves a remarkable 50%+ reduction 🚀 in VRAM consumption during long-context sequence processing (~25k

We will release the quantized models of Qwen3 to you in the following days. Today we release the AWQ and GGUFs of Qwen3-14B and Qwen3-32B, which enables using the models with limited GPU memory. Qwen3-32B-AWQ: huggingface.co/Qwen/Qwen3-32B… Qwen3-32B-GGUF: huggingface.co/Qwen/Qwen3-32B…

We’re officially releasing the quantized models of Qwen3 today! Now you can deploy Qwen3 via Ollama, LM Studio, SGLang, and vLLM — choose from multiple formats including GGUF, AWQ, and GPTQ for easy local deployment. Find all models in the Qwen3 collection on Hugging Face and

Please check out our Qwen3 Technical Report. 👇🏻 github.com/QwenLM/Qwen3/b…

After a few weeks of phased testing, Deep Research on Qwen Chat is now live and available for everyone ! 🎉 Here's how to use it: Just ask something you're curious about — like "Tell me something about robotics." Qwen will then ask you to narrow it down — maybe history, theory,

Parameter and inference-time scaling have already demonstrated that more compute brings more intelligence. 🤔 But is there a new way to scale compute? The answer might be yes! We propose Parallel Scaling—increasing parallel computation during training and inference. As an

We’re releasing the quantized models of Qwen2.5-Omni-7B today! Find all models in the Qwen2.5-Omni collection on Hugging Face and ModelSope. Hugging Face：huggingface.co/collections/Qw… ModelScope：modelscope.cn/collections/Qw… Enjoy!

Title: Modeling World Preference Our research reveals that human preference modeling follows Scaling Laws, suggesting that diverse human preferences might share a unified representation. We propose "Modeling World Preference" to emphasize this potential for scalability. We

🚀 Qwen Web Dev just got even better! ✨ One prompt. One website. One click to deploy. 💡 Let your creativity shine — and share it with the world. 🔥 What will you build today?