Add model card for DeepSeekMath-7B-Caco with metadata and links
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by
nielsr
HF Staff
- opened
README.md
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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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---
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# DeepSeekMath-7B-Caco
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This repository hosts the **DeepSeekMath-7B-Caco** model, which is fine-tuned using the Caco framework. Caco (Code-Assisted Chain-of-ThOught) is a novel framework that automates the synthesis of high-quality, verifiable, and diverse instruction-Chain-of-Thought (CoT) reasoning data through code-driven augmentation. It leverages executable code steps to generate comprehensive and logically correct reasoning paths, enhancing the reasoning capabilities of Large Language Models (LLMs).
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The **DeepSeekMath-7B-Caco** model is built upon the `deepseek-math-7b-base` and has been trained on the Caco-1.3M dataset, achieving strong competitive performance on mathematical reasoning benchmarks, as detailed in the research paper.
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For more details on the Caco framework, its methodology, and experimental results, please refer to the official paper:
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[**Scaling Code-Assisted Chain-of-Thoughts and Instructions for Model Reasoning**](https://huggingface.co/papers/2510.04081) (accepted by NeurIPS 2025).
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## Code and Further Information
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The official implementation, detailed instructions for data generation, training, and evaluation, can be found on the GitHub repository:
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[https://github.com/LHL3341/Caco](https://github.com/LHL3341/Caco)
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You can also find the Caco dataset and other Caco-trained models within the Hugging Face collection:
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[https://huggingface.co/collections/LHL3341/caco-68e0cb7b8a5f0071fac1f611](https://huggingface.co/collections/LHL3341/caco-68e0cb7b8a5f0071fac1f611)
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## Citation
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If you find this work useful, please cite the paper:
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```bibtex
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@article{caco,
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title={Scaling Code-Assisted Chain-of-Thoughts and Instructions for Model Reasoning},
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author={Honglin Lin and Qizhi Pei and Xin Gao and Zhuoshi Pan and Yu Li and Juntao Li and Conghui He and Lijun Wu},
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journal={arXiv preprint arXiv:2510.04081},
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year={2025}
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}
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```
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