61 lines
2.5 KiB
Markdown
61 lines
2.5 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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base_model: Qwen/Qwen3-4B-Instruct-2507
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tags:
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- scientific-evaluation
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- citation-prediction
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- preference-learning
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- GRPO
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pipeline_tag: text-generation
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library_name: transformers
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---
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# SciJudge-Qwen3-4B
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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."
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This model is part of the paper: **[AI Can Learn Scientific Taste](https://arxiv.org/abs/2603.14473)**.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "OpenMOSS-Team/SciJudge-4B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="bfloat16", device_map="auto")
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messages = [
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{"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."},
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{"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: ..."}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, top_p=0.8, top_k=20)
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response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
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print(response)
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```
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## Training Details
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- **Base model:** Qwen3-4B-Instruct-2507
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- **Training method:** GRPO (Generative Reward Policy Optimization) with DAPO loss
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- **Training data:** 720,341 preference pairs from arXiv papers
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- **Learning rate:** 8e-7 (cosine schedule, 5% warmup)
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- **Batch size:** 8 per device × 64 GPUs × 2 gradient accumulation = 1024 effective
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- **Optimizer:** AdamW (β1=0.9, β2=0.95, weight decay=0.1)
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- **Precision:** bfloat16
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- **KL coefficient (β):** 0.03
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## Citation
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```bibtex
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@article{scijudge2025,
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title={AI Can Learn Scientific Taste},
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year={2025}
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}
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```
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