Qwen team from Alibaba presents the Qwen 2.5 series: Qwen2.5-Max (large-scale MoE), Qwen2.5-VL (vision-language), and Qwen2.5-1M (long-context). This represents a significant step forward.
I’ve been testing a bunch of large language models, and Qwen 2.5 really holds its own. The ability to process long contexts is a huge win—it handled a 40-page doc with references like a pro. It’s not just smart; it’s contextually aware, which is rare at this level.
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Hey everyone! The Qwen team from Alibaba Cloud recently launched the latest Qwen 2.5 series, featuring some powerful new AI models.
Key Highlights:
🚀 MoE Power (Max): Qwen2.5-Max leverages a Mixture-of-Experts architecture for enhanced intelligence.
🖼️ Advanced Vision-Language (VL): Qwen2.5-VL offers a huge leap in visual understanding and processing.
📖 Long-Context Capability (1M): Qwen2.5-1M tackles extra-long documents and conversations.
🌍 Open-Source Options: Both Qwen2.5-VL and Qwen2.5-1M offer open-source models for the community (See these Models on their Hugging Face).
You can try Qwen 2.5 series model in Qwen Chat: https://chat.qwenlm.ai/
Glad to see more and more Mixture-of-Expert (MoE) architectures leading the LLM leaderboard! DeepSeek and Qwen is all over the place in my social feed these days! Curious how will Alibaba apply Qwen to its current business portfolio?
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I mean come on, who doesn't love AI model after AI model coming out in quick succession, there's so many to try now!!
Going to have to dive into this one ASAP before people use up all the server space!
Thanks for hunting Zac!!
I particularly like the small Qwen models I can run on my own computer. But this is really great: so much work enterprising individuals can distill smaller models from. And jush have the VL and 1M now available is great!
That Alibaba launched Qwen 2.5-Max on the first day of the Lunar New Year signals an urgent response to DeepSeek's recent AI breakthroughs.
This large-scale Mixture-of-Expert (MoE) model has been pre-trained on over 20 trillion tokens (!!) and enhanced through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF).
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