Link model card to technical report and project page
Browse filesHi! I'm Niels from the Hugging Face community team.
This PR improves the model card by:
- Adding the `arxiv` ID to the metadata to link the model with its technical report on Hugging Face Papers.
- Updating the references to the technical report to point to the paper page instead of a local file.
- Adding the official Upstage project page link.
- Updating the BibTeX citation to include the full list of authors for better academic attribution.
README.md
CHANGED
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@@ -2,16 +2,17 @@
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language:
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- en
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- ko
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license: other
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license_name: solar-apache-2.0
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tags:
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- upstage
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- solar
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- moe
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- 100b
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- llm
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-
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pipeline_tag: text-generation
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---
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<p align="center">
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**Solar Open** is Upstage's flagship **102B-parameter** large language model, trained **entirely from scratch** and released under the **Solar-Apache License 2.0** (see [LICENSE](#license) for details). As a **Mixture-of-Experts (MoE)** architecture, it delivers enterprise-grade performance in reasoning, instruction-following, and agentic capabilities—all while prioritizing transparency and customization for the open-source community.
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## Highlights
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* **MoE Architecture (102B / 12B):** Built on a Mixture-of-Experts architecture with **102B total / 12B active parameters**. This design delivers the knowledge depth of a massive model with the inference speed and cost-efficiency of a much smaller model.
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* **Hardware Requirements:**
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* **Minimum:** 4x NVIDIA A100 (80GB)
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For more details, please refer to [Solar Open Technical Report](
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## License
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This repository contains both model weights and code,
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If you use Solar Open in your research, please cite:
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```bibtex
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@
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-
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-
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-
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-
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}
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```
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language:
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- en
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- ko
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library_name: transformers
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license: other
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license_name: solar-apache-2.0
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pipeline_tag: text-generation
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tags:
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- upstage
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- solar
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- moe
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- 100b
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- llm
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arxiv: 2601.07022
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---
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<p align="center">
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**Solar Open** is Upstage's flagship **102B-parameter** large language model, trained **entirely from scratch** and released under the **Solar-Apache License 2.0** (see [LICENSE](#license) for details). As a **Mixture-of-Experts (MoE)** architecture, it delivers enterprise-grade performance in reasoning, instruction-following, and agentic capabilities—all while prioritizing transparency and customization for the open-source community.
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[**Technical Report**](https://huggingface.co/papers/2601.07022) | [**Project Page**](https://upstage.ai)
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## Highlights
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* **MoE Architecture (102B / 12B):** Built on a Mixture-of-Experts architecture with **102B total / 12B active parameters**. This design delivers the knowledge depth of a massive model with the inference speed and cost-efficiency of a much smaller model.
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* **Hardware Requirements:**
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* **Minimum:** 4x NVIDIA A100 (80GB)
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For more details, please refer to the [Solar Open Technical Report](https://huggingface.co/papers/2601.07022).
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## License
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This repository contains both model weights and code,
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If you use Solar Open in your research, please cite:
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```bibtex
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@article{park2025solar,
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title={Solar Open Technical Report},
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author={Sungrae Park and Sanghoon Kim and Jungho Cho and Gyoungjin Gim and Dawoon Jung and Mikyoung Cha and Eunhae Choo and Taekgyu Hong and Minbyul Jeong and SeHwan Joo and Minsoo Khang and Eunwon Kim and Minjeong Kim and Sujeong Kim and Yunsu Kim and Hyeonju Lee and Seunghyun Lee and Sukyung Lee and Siyoung Park and Gyungin Shin and Inseo Song and Wonho Song and Seonghoon Yang and Seungyoun Yi and Sanghoon Yoon and Jeonghyun Ko and Seyoung Song and Keunwoo Choi and Hwalsuk Lee and Sunghun Kim and Du-Seong Chang and Kyunghyun Cho and Junsuk Choe and Hwaran Lee and Jae-Gil Lee and KyungTae Lim and Alice Oh},
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journal={arXiv preprint arXiv:2601.07022},
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year={2025},
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url={https://huggingface.co/papers/2601.07022}
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}
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```
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