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Add arXiv metadata and update citation with paper links

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Hi, I'm Niels from the community science team at Hugging Face.

This PR improves the model card for GLM-5 by:
- Adding the `arxiv` tag to the metadata to link the repository with its technical report.
- Adding direct links to the paper and the official GitHub repository at the top of the description.
- Updating the citation section with the correct BibTeX entry.

These changes help researchers and developers easily access the associated technical documentation and codebase.

Files changed (1) hide show
  1. README.md +17 -3
README.md CHANGED
@@ -1,10 +1,11 @@
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  ---
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  language:
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- - en
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- - zh
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  library_name: transformers
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  license: mit
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  pipeline_tag: text-generation
 
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  ---
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  # GLM-5
@@ -22,6 +23,11 @@ pipeline_tag: text-generation
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  👉 One click to <a href="https://chat.z.ai">GLM-5</a>.
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  </p>
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  ## Introduction
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  We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity.
@@ -149,4 +155,12 @@ vLLM, SGLang, KTransformers, and xLLM all support local deployment of GLM-5. A s
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  ## Citation
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- Our technical report is coming soon.
 
 
 
 
 
 
 
 
 
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  ---
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  language:
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+ - en
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+ - zh
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  library_name: transformers
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  license: mit
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  pipeline_tag: text-generation
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+ arxiv: 2602.15763
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  ---
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  # GLM-5
 
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  👉 One click to <a href="https://chat.z.ai">GLM-5</a>.
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  </p>
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+ <p align="center">
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+ [<a href="https://huggingface.co/papers/2602.15763" target="_blank">Paper</a>]
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+ [<a href="https://github.com/zai-org/GLM-5" target="_blank">GitHub</a>]
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+ </p>
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+
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  ## Introduction
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  We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity.
 
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  ## Citation
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+ ```bibtex
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+ @article{glm5team2026glm5,
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+ title={GLM-5: from Vibe Coding to Agentic Engineering},
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+ author={GLM-5 Team and Aohan Zeng and Xin Lv and Zhenyu Hou and Zhengxiao Du and Qinkai Zheng and Bin Chen and Da Yin and Chendi Ge and Chengxing Xie and others},
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+ journal={arXiv preprint arXiv:2602.15763},
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+ year={2026},
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+ url={https://huggingface.co/papers/2602.15763}
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+ }
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+ ```