SciJudge-Qwen3-4B 是一款针对科学论文评估的微调语言模型。给定两篇学术论文的元数据（标题、摘要、发表日期），该模型能够预测哪篇论文的引用量更高——以此作为评估研究影响力和“科学品味”的代理指标。
该模型源自论文：AI Can Learn Scientific Taste。
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "OpenMOSS-Team/SciJudge-4B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="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)@article{scijudge2025,
title={AI Can Learn Scientific Taste},
year={2025}
}