Improve model card: add pipeline tag, library name, update license, and paper reference

#1
by nielsr HF Staff - opened
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  1. README.md +18 -7
README.md CHANGED
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  ---
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- datasets:
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- - NeelNanda/pile-10k
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  base_model:
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  - deepseek-ai/DeepSeek-R1
 
 
 
 
 
 
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-
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- ---
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  ## Model Details
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- This model is an int2 model with group_size 64 and symmetric quantization of [deepseek-ai/DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) generated by [intel/auto-round](https://github.com/intel/auto-round) algorithm. Some layers are fallback to 4/16 bits. Refer to Section "Generate the model" for more details of mixed bits setting.
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  Please follow the license of the original model. This model could **NOT** run on other severing frameworks.
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@@ -439,6 +443,13 @@ The license on this model does not constitute legal advice. We are not responsib
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  ## Cite
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- @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }
 
 
 
 
 
 
 
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- [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
 
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  ---
 
 
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  base_model:
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  - deepseek-ai/DeepSeek-R1
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+ datasets:
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+ - NeelNanda/pile-10k
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+ pipeline_tag: text-generation
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+ library_name: transformers
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+ license: apache-2.0
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+ ---
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+ This model is an int2 model with group_size 64 and symmetric quantization of [deepseek-ai/DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1), generated by the **SignRoundV2** algorithm described in the paper [SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs](https://huggingface.co/papers/2512.04746).
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+ For more details on the AutoRound project and its implementation, see the [GitHub repository](https://github.com/intel/auto-round).
 
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  ## Model Details
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+ Some layers are fallback to 4/16 bits. Refer to Section "Generate the model" for more details of mixed bits setting.
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  Please follow the license of the original model. This model could **NOT** run on other severing frameworks.
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  ## Cite
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+ ```bibtex
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+ @article{cheng2025signroundv2,
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+ title={SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs},
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+ author={Cheng, Wenhua and Zhang, Weiwei and Guo, Heng and Shen, Haihao},
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+ journal={arXiv preprint arXiv:2512.04746},
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+ year={2025}
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+ }
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+ ```
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+ [arxiv](https://arxiv.org/abs/2512.04746)