README.md

---
library_name: transformers
license: apache-2.0
datasets:
- datumo/CAC-CoT
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
---

# Model Card for Model ID

## Model Details

### Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Sunguk Choi, Yonghoon Kwon, Heondeuk Lee  
- **Shared by:** SelectStar/Datumo
- **Model type:** Decoder-only language model (Causal LM)  
- **Language(s) (NLP):** English  
- **License:** Apache License 2.0  
- **Finetuned from model:** 🔧 Qwen-2.5-7b-it

### Model Sources

- **Repository:** https://github.com/selectstar-ai/CAC-CoT  
- **Paper:** https://arxiv.org/abs/2508.18743

### Direct Use

- Solving reasoning problems requiring chain-of-thought (CoT).
- Educational tutoring, math/logic assistants, explainable QA.
- Applications requiring interpretable reasoning with low latency.

### Downstream Use [optional]

- Fine-tuning for specific reasoning benchmarks such as GSM8K, StrategyQA, or S1-Bench.
- Integration into larger RAG or tutoring systems.

### Out-of-Scope Use

- Non-English tasks.
- Open-ended creative generation (e.g., fiction, poetry).

## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("datumo/CAC-CoT")  # 🔧 Replace with your model path
tokenizer = AutoTokenizer.from_pretrained("datumo/CAC-CoT")

prompt = "Problem: If you have 3 apples and get 2 more, how many do you have?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Citation

**BibTeX:**

```
@misc{choi2025caccotconnectorawarecompactchainofthought,
  title={CAC-CoT: Connector-Aware Compact Chain-of-Thought for Efficient Reasoning Data Synthesis Across Dual-System Cognitive Tasks},
  author={Sunguk Choi and Yonghoon Kwon and Heondeuk Lee},
  year={2025},
  eprint={2508.18743},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2508.18743},
}
```

## More Information
- System-1: Fast, intuitive reasoning
- System-2: Slow, logical reasoning
- Connector phrase: Fixed phrases guiding logical flow (e.g., “Because of this,” “Then,” etc.)
- ART: Average Reasoning Trace length

## Model Card Authors
Sunguk Choi, Yonghoon Kwon, Heondeuk Lee