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OpenMOSS-Team
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Model Details
Model Provider
OpenMOSS-Team
Model Tree
Qwen/Qwen3-30B-A3B-Instruct-2507
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_name = "OpenMOSS-Team/SciJudge-30B-2605"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto",) messages = [ {"role": "system", "content": "You are a helpful assistant. You first think about the reasoning process in your mind and then provide the user with the answer."}, {"role": "user", "content": "Today is 2025-12-10. Based on the titles, abstracts, and publication dates of the following two papers A and B, determine which paper has a higher citation count.\nShow your reasoning process in <reason> </reason> tags. And return the final answer in <answer> </answer> tags. The final answer should contain only 'A' or 'B'.\n\nPaper A:\nTitle: ...\nAbstract: ...\nDate: ...\n\nPaper B:\nTitle: ...\nAbstract: ...\nDate: ..."}] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, top_p=0.8, top_k=20)response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)print(response)Accuracy on the SciJudgeBench test split, the 1,000-example MAIN_1000 in-domain evaluation set:
| Model | CS | Math | Physics | Others | Avg. |
|---|---|---|---|---|---|
| Qwen3-30B-A3B-Instruct-2507 | 73.39 | 79.90 | 64.41 | 63.94 | 69.7 |
| SciJudge-30B-2605 | 83.47 | 89.71 | 81.18 | 77.40 | 82.7 |
bibtex
@misc{tong2026ailearnscientifictaste, title={AI Can Learn Scientific Taste}, author={Jingqi Tong and Mingzhe Li and Hangcheng Li and Yongzhuo Yang and Yurong Mou and Weijie Ma and Zhiheng Xi and Hongji Chen and Xiaoran Liu and Qinyuan Cheng and Ming Zhang and Qiguang Chen and Weifeng Ge and Qipeng Guo and Tianlei Ying and Tianxiang Sun and Yining Zheng and Xinchi Chen and Jun Zhao and Ning Ding and Xuanjing Huang and Yugang Jiang and Xipeng Qiu}, year={2026}, eprint={2603.14473}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2603.14473}, }import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_name = "OpenMOSS-Team/SciJudge-30B-2605"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto",) messages = [ {"role": "system", "content": "You are a helpful assistant. You first think about the reasoning process in your mind and then provide the user with the answer."}, {"role": "user", "content": "Today is 2025-12-10. Based on the titles, abstracts, and publication dates of the following two papers A and B, determine which paper has a higher citation count.\nShow your reasoning process in <reason> </reason> tags. And return the final answer in <answer> </answer> tags. The final answer should contain only 'A' or 'B'.\n\nPaper A:\nTitle: ...\nAbstract: ...\nDate: ...\n\nPaper B:\nTitle: ...\nAbstract: ...\nDate: ..."}] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, top_p=0.8, top_k=20)response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)print(response)@misc{tong2026ailearnscientifictaste, title={AI Can Learn Scientific Taste}, author={Jingqi Tong and Mingzhe Li and Hangcheng Li and Yongzhuo Yang and Yurong Mou and Weijie Ma and Zhiheng Xi and Hongji Chen and Xiaoran Liu and Qinyuan Cheng and Ming Zhang and Qiguang Chen and Weifeng Ge and Qipeng Guo and Tianlei Ying and Tianxiang Sun and Yining Zheng and Xinchi Chen and Jun Zhao and Ning Ding and Xuanjing Huang and Yugang Jiang and Xipeng Qiu}, year={2026}, eprint={2603.14473}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2603.14473}, }