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Qwen 2.5 - Alibaba's Large Language Model Series
webqwenlm.github.io·qwenlm.github.io/
Qwen 2.5 is a frontier model series from Alibaba, relevant to AI safety discussions around capability proliferation, open-weight model risks, and the geopolitical landscape of advanced AI development outside Western labs.
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Summary
Qwen 2.5 is Alibaba's latest series of large language models, representing significant capability advances across language understanding, coding, mathematics, and multimodal tasks. The series includes models of various sizes designed for both research and commercial deployment. It represents a major frontier model release from a leading Chinese AI lab.
Key Points
- •Qwen 2.5 is a family of open-weight LLMs from Alibaba Cloud with models ranging from small to very large parameter counts
- •Models demonstrate strong performance on coding, math reasoning, and instruction-following benchmarks
- •Includes specialized variants such as Qwen2.5-Coder and Qwen2.5-Math for domain-specific tasks
- •Released with open weights, enabling broad research access and deployment flexibility
- •Represents growing frontier AI capabilities from non-Western labs, relevant to AI governance and competition dynamics
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Proliferation Risk Model | Analysis | 65.0 |
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Go Now Qwickly forging AGI, enhancing intelligence. Tech Report GitHub Hugging Face ModelScope DISCORD
Introduction We are excited to introduce Qwen3Guard, the first safety guardrail model in the Qwen family. Built upon the powerful Qwen3 foundation models and fine-tuned specifically for safety classificatoin, Qwen3Guard ensures responsible AI interactions by delivering precise safety detection for both prompts and responses, complete with risk levels and categorized classifications for accurate moderation.
Qwen3Guard achieves state-of-the-art performance on major safety benchmarks, demonstrating strong capabilities in both prompt and response classification tasks across English, Chinese, and multilingual environments....
QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD
We are excited to introduce Qwen-Image-Edit, the image editing version of Qwen-Image. Built upon our 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image’s unique text rendering capabilities to image editing tasks, enabling precise text editing. Furthermore, Qwen-Image-Edit simultaneously feeds the input image into Qwen2.5-VL (for visual semantic control) and the VAE Encoder (for visual appearance control), achieving capabilities in both semantic and appearance editing....
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD
We are thrilled to release Qwen-Image, a 20B MMDiT image foundation model that achieves significant advances in complex text rendering and precise image editing. To try the latest model, feel free to visit Qwen Chat and choose “Image Generation”.
The key features include:
Superior Text Rendering: Qwen-Image excels at complex text rendering, including multi-line layouts, paragraph-level semantics, and fine-grained details. It supports both alphabetic languages (e....
PAPER DISCORD
Introduction Reinforcement Learning (RL) has emerged as a pivotal paradigm for scaling language models and enhancing their deep reasoning and problem-solving capabilities. To scale RL, the foremost prerequisite is maintaining stable and robust training dynamics. However, we observe that existing RL algorithms (such as GRPO) exhibit severe instability issues during long training and lead to irreversible model collapse, hindering further performance improvements with increased compute.
To enable successful RL scaling, we propose the Group Sequence Policy Optimization (GSPO) algorithm....
DEMO API DISCORD
Introduction Here we introduce the latest update of Qwen-MT (qwen-mt-turbo) via Qwen API. This update builds upon the powerful Qwen3, leveraging trillions multilingual and translation tokens to comprehensively enhance the model’s multilingual understanding and translation capabilities. By integrating reinforcement learning techniques, the model achieves significant improvements in translation accuracy and l
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