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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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---
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license: mit
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pipeline_tag: text-generation
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language:
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- ja
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tags:
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- japanese
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- qwen2
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inference: false
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
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# DeepSeek-R1-Distill-Qwen-14B-Japanese
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## Model Description
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This is a Japanese finetuned model based on [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B).
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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model = AutoModelForCausalLM.from_pretrained("cyberagent/DeepSeek-R1-Distill-Qwen-14B-Japanese", device_map="auto", torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained("cyberagent/DeepSeek-R1-Distill-Qwen-14B-Japanese")
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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messages = [
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{"role": "user", "content": "AIによって私たちの暮らしはどのように変わりますか?"}
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]
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input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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output_ids = model.generate(input_ids,
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max_new_tokens=4096,
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temperature=0.7,
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streamer=streamer)
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```
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## Prompt Format
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```
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<|begin▁of▁sentence|><|User|>AIによって私たちの暮らしはどのように変わりますか?<|Assistant|>
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```
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## License
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[MIT License](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE)
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## Author
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[Ryosuke Ishigami](https://huggingface.co/rishigami)
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## How to cite
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```tex
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@misc{cyberagent-deepseek-r1-distill-qwen-14b-japanese,
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title={DeepSeek-R1-Distill-Qwen-14B-Japanese},
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url={https://huggingface.co/cyberagent/DeepSeek-R1-Distill-Qwen-14B-Japanese},
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author={Ryosuke Ishigami},
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year={2025},
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}
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```
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## Citation
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```
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@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
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title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning},
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author={DeepSeek-AI and Daya Guo and Dejian Yang and Haowei Zhang and Junxiao Song and Ruoyu Zhang and Runxin Xu and Qihao Zhu and Shirong Ma and Peiyi Wang and Xiao Bi and Xiaokang Zhang and Xingkai Yu and Yu Wu and Z. F. Wu and Zhibin Gou and Zhihong Shao and Zhuoshu Li and Ziyi Gao and Aixin Liu and Bing Xue and Bingxuan Wang and Bochao Wu and Bei Feng and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Qu and Hui Li and Jianzhong Guo and Jiashi Li and Jiawei Wang and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and J. L. Cai and Jiaqi Ni and Jian Liang and Jin Chen and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Liang Zhao and Litong Wang and Liyue Zhang and Lei Xu and Leyi Xia and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Meng Li and Miaojun Wang and Mingming Li and Ning Tian and Panpan Huang and Peng Zhang and Qiancheng Wang and Qinyu Chen and Qiushi Du and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and R. J. Chen and R. L. Jin and Ruyi Chen and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shengfeng Ye and Shiyu Wang and Shuiping Yu and Shunfeng Zhou and Shuting Pan and S. S. Li and Shuang Zhou and Shaoqing Wu and Shengfeng Ye and Tao Yun and Tian Pei and Tianyu Sun and T. Wang and Wangding Zeng and Wanjia Zhao and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and W. L. Xiao and Wei An and Xiaodong Liu and Xiaohan Wang and Xiaokang Chen and Xiaotao Nie and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and X. Q. Li and Xiangyue Jin and Xiaojin Shen and Xiaosha Chen and Xiaowen Sun and Xiaoxiang Wang and Xinnan Song and Xinyi Zhou and Xianzu Wang and Xinxia Shan and Y. K. Li and Y. Q. Wang and Y. X. Wei and Yang Zhang and Yanhong Xu and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Wang and Yi Yu and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yuan Ou and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yunfan Xiong and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Y. X. Zhu and Yanhong Xu and Yanping Huang and Yaohui Li and Yi Zheng and Yuchen Zhu and Yunxian Ma and Ying Tang and Yukun Zha and Yuting Yan and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhicheng Ma and Zhigang Yan and Zhiyu Wu and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Zizheng Pan and Zhen Huang and Zhipeng Xu and Zhongyu Zhang and Zhen Zhang},
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year={2025},
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eprint={2501.12948},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2501.12948},
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}
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```
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