初始化项目，由ModelHub XC社区提供模型

Model: OpenMOSS-Team/SciJudge-4B
Source: Original Platform

README.md

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+---
+language:
+  - en
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+tags:
+  - scientific-evaluation
+  - citation-prediction
+  - preference-learning
+  - GRPO
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# SciJudge-Qwen3-4B
+
+SciJudge-Qwen3-4B is a fine-tuned language model for **scientific paper evaluation**. Given two academic papers' metadata (title, abstract, publication date), it predicts which paper has a higher citation count — serving as a proxy for assessing research impact and "scientific taste."
+
+This model is part of the paper: **[AI Can Learn Scientific Taste](https://arxiv.org/abs/2603.14473)**.
+
+## Usage
+
+```python
+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)
+```
+
+## Training Details
+
+- **Base model:** Qwen3-4B-Instruct-2507
+- **Training method:** GRPO (Generative Reward Policy Optimization) with DAPO loss
+- **Training data:** 720,341 preference pairs from arXiv papers
+- **Learning rate:** 8e-7 (cosine schedule, 5% warmup)
+- **Batch size:** 8 per device × 64 GPUs × 2 gradient accumulation = 1024 effective
+- **Optimizer:** AdamW (β1=0.9, β2=0.95, weight decay=0.1)
+- **Precision:** bfloat16
+- **KL coefficient (β):** 0.03
+
+## Citation
+
+```bibtex
+@article{scijudge2025,
+  title={AI Can Learn Scientific Taste},
+  year={2025}
+}
+```