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--- |
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library_name: transformers |
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license: apache-2.0 |
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datasets: |
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- datumo/CAC-CoT |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-7B-Instruct |
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--- |
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# Model Card for Model ID |
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## Model Details |
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### Model Description |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** Sunguk Choi, Yonghoon Kwon, Heondeuk Lee |
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- **Shared by:** SelectStar/Datumo |
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- **Model type:** Decoder-only language model (Causal LM) |
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- **Language(s) (NLP):** English |
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- **License:** Apache License 2.0 |
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- **Finetuned from model:** 🔧 Qwen-2.5-7b-it |
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### Model Sources |
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- **Repository:** https://github.com/selectstar-ai/CAC-CoT |
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- **Paper:** https://arxiv.org/abs/2508.18743 |
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### Direct Use |
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- Solving reasoning problems requiring chain-of-thought (CoT). |
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- Educational tutoring, math/logic assistants, explainable QA. |
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- Applications requiring interpretable reasoning with low latency. |
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### Downstream Use [optional] |
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- Fine-tuning for specific reasoning benchmarks such as GSM8K, StrategyQA, or S1-Bench. |
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- Integration into larger RAG or tutoring systems. |
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### Out-of-Scope Use |
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- Non-English tasks. |
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- Open-ended creative generation (e.g., fiction, poetry). |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("datumo/CAC-CoT") # 🔧 Replace with your model path |
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tokenizer = AutoTokenizer.from_pretrained("datumo/CAC-CoT") |
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prompt = "Problem: If you have 3 apples and get 2 more, how many do you have?" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=100) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Citation |
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**BibTeX:** |
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``` |
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@misc{choi2025caccotconnectorawarecompactchainofthought, |
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title={CAC-CoT: Connector-Aware Compact Chain-of-Thought for Efficient Reasoning Data Synthesis Across Dual-System Cognitive Tasks}, |
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author={Sunguk Choi and Yonghoon Kwon and Heondeuk Lee}, |
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year={2025}, |
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eprint={2508.18743}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.AI}, |
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url={https://arxiv.org/abs/2508.18743}, |
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} |
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``` |
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## More Information |
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- System-1: Fast, intuitive reasoning |
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- System-2: Slow, logical reasoning |
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- Connector phrase: Fixed phrases guiding logical flow (e.g., “Because of this,” “Then,” etc.) |
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- ART: Average Reasoning Trace length |
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## Model Card Authors |
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Sunguk Choi, Yonghoon Kwon, Heondeuk Lee |
