metadata
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
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