OpenOrca-Step-by-step-reasoning

This work was performed to help models with reasoning. I developed it working on my Cinder model, a STEM q and a model.
Modified OpenORCA Step-by-Step Reasoning Dataset Overview

The Modified OpenORCA Step-by-Step Reasoning Dataset represents a groundbreaking resource in the field of artificial intelligence, specifically designed to enhance the reasoning capabilities of AI models. This unique dataset is the result of a meticulous process of sorting, selecting, and altering dialogues from the original OpenORCA collection, with a focus on promoting an intrinsic approach to step-by-step logical reasoning across a wide array of topics.

Dataset Composition

Derived from the comprehensive OpenORCA dataset, this manually modified version strategically removes sections of prompts for step-by-step reasoning. Instead, it presents AI models with real-world scenarios requiring the deduction of logical steps to reach conclusions without explicit prompting to do so. Thereby encouraging models to develop a natural inclination towards systematic problem-solving. The dataset spans various domains, including but not limited to, everyday logical puzzles, basic mathematical problems, and complex scenario-based queries.
Features

Size: 92.4 MB, 64963 rows of dialogues that demonstrate step-by-step reasoning.
Format: Available in JSON facilitating easy integration with common machine learning frameworks and environments.
Content: Each entry includes a user query followed by a system-generated response that embodies step-by-step reasoning, without explicitly stating the requirement for such a process. This setup aims to train AI models to autonomously employ logical progression in their responses.
Use Cases: Ideal for developing AI models geared towards natural language understanding, conversation AI, educational bots, and any application requiring a deep grasp of logical progression and problem-solving skills.

Potential Applications

AI Model Training: Serves as an invaluable tool for training and refining AI models, especially those focused on natural language processing, conversational intelligence, and automated reasoning.
Educational Technology: Offers a rich resource for creating educational bots and tools designed to assist in teaching logical reasoning, critical thinking, and problem-solving strategies.
Research and Development: Provides a robust foundation for academic and commercial research into improving step-by-step reasoning capabilities of AI systems, enhancing their ability to understand and interact with the world in a more human-like manner.

Licensing and Accessibility

This dataset is distributed under the MIT License, allowing for broad use, modification, and distribution, provided that the original license and copyright notices are included. This liberal licensing ensures that the Modified OpenORCA Step-by-Step Reasoning Dataset can be freely utilized by researchers, developers, and educators to advance the field of AI and develop applications that benefit from enhanced reasoning capabilities.

For request, questions, support, or chat about current research, message me on Cinder's discord https://discord.gg/5ebjDrnZ
Or email [e-mail]

Original Open Orca dataset: https://huggingface.co/datasets/Open-Orca/OpenOrca
Inspired by the Microsoft unreleased datasets for Phi.
Special thanks to the contributors of the original dataset

Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan

http://AlignmentLab.ai:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus

Also special thanks to TheBloke for supporting the community.

Original Open Orca Citation:

@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca}},
}

@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}

@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}

@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}

本研究旨在助力AI模型提升推理能力，其开发基于笔者的Cinder模型——一款理工科问答模型。

# 改进版OpenORCA逐步推理数据集概述
本数据集是AI领域极具开创性的资源，专为增强AI模型的推理能力而设计。该独特数据集源自对原始OpenORCA数据集的对话进行精心筛选、整理与修改的全过程，核心目标是在多元主题范围内，推动AI模型采用内在逻辑逐步开展推理的能力。

## 数据集构成
本手动改进版数据集源自体量完整的OpenORCA数据集，其策略性地移除了原有数据集中针对逐步推理的提示语部分。取而代之的是，数据集向AI模型提供真实世界场景，要求其在未被明确提示的情况下，通过推导逻辑步骤得出结论，以此激励模型形成系统性解决问题的自然倾向。本数据集覆盖多元领域，包括但不限于日常逻辑谜题、基础数学问题以及复杂场景类查询。

## 数据集特性
- 体量：92.4 MB，包含64963条展示逐步推理过程的对话数据
- 格式：采用JSON格式，可轻松集成至主流机器学习框架与开发环境中
- 内容：每条数据均包含用户查询与系统生成的回复，回复中蕴含逐步推理过程，但未明确提及该推理要求。此设计旨在训练AI模型在回复中自主运用逻辑递进思维
- 应用场景：适配面向自然语言理解、对话式AI、教育机器人的AI模型开发，以及任何需要深度掌握逻辑递进与问题解决能力的应用场景

## 潜在应用方向
1. AI模型训练：作为训练与优化AI模型的宝贵工具，尤其适用于聚焦自然语言处理、对话智能与自动推理的模型开发
2. 教育科技：可为开发教育机器人与教学工具提供丰富资源，助力逻辑推理、批判性思维与问题解决策略的教学工作
3. 研发工作：可为学术与商业研究提供坚实基础，用于提升AI系统的逐步推理能力，使其能够以更类人的方式理解并与外界互动

## 许可与可访问性
本数据集采用MIT许可协议进行分发，允许广泛使用、修改与分发，但需保留原始许可与版权声明。此宽松的许可协议确保研究人员、开发者与教育者可自由使用本改进版OpenORCA逐步推理数据集，以推动AI领域发展，并开发得益于更强推理能力的应用程序。

如需咨询、提问、获取支持或讨论当前研究，请前往Cinder的Discord频道https://discord.gg/5ebjDrnZ发送消息，或发送邮件至[e-mail]。

### 原始OpenORCA数据集：https://huggingface.co/datasets/Open-Orca/OpenOrca
本数据集灵感源自微软尚未公开的Phi系列数据集。

特别感谢原始数据集的所有贡献者：
Teknium、WingLian/Caseus、Eric Hartford、NanoBit、Pankaj、Winddude、Rohan
AlignmentLab.ai团队：Autometa、Entropi、AtlasUnified、NeverendingToast、NanoBit、WingLian/Caseus

同时特别感谢TheBloke对社区的支持。

### 原始OpenORCA引用文献：
@misc{OpenOrca,
title = {OpenOrca：基于GPT增强FLAN推理痕迹的开源数据集},
author = {Wing Lian, Bleys Goodson, Eugene Pentland, Austin Cook, Chanvichet Vong, "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace 仓库},
howpublished = {url{https://huggingface.co/Open-Orca/OpenOrca}},
}

@misc{mukherjee2023orca,
title = {Orca：从GPT-4的复杂解释痕迹中渐进式学习},
author = {Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, Ahmed Awadallah},
year = {2023},
eprint = {2306.02707},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}

@misc{longpre2023flan,
title = {Flan集合：设计有效指令微调的数据与方法},
author = {Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, Adam Roberts},
year = {2023},
eprint = {2301.13688},
archivePrefix = {arXiv},
primaryClass = {cs.AI}
}

@misc{touvron2023llama,
title = {Llama 2：开源基础模型与微调聊天模型},
author = {Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom},
year = {2023},
eprint = {arXiv:2307.09288}
}
@software{touvron2023llama,
title = {LLaMA：开源高效基础语言模型},
author = {Touvron, Hugo, Lavril, Thibaut, Izacard, Gautier, Martinet, Xavier, Lachaux, Marie-Anne, Lacroix, Timothée, Rozière, Baptiste, Goyal, Naman, Hambro, Eric, Azhar, Faisal, Rodriguez, Aurelien, Joulin, Armand, Grave, Edouard, Lample, Guillaume},
journal = {arXiv preprint arXiv:2302.13971},
year = {2023}
}