| Name | reasoning-from-scratch JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | Reasoning Models From Scratch |
| upload_time | 2025-09-10 01:28:10 |
| maintainer | None |
| docs_url | None |
| author | Sebastian Raschka |
| requires_python | >=3.10 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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# Build A Reasoning Model (From Scratch)
This repository contains the code for developing an LLM reasoning model and is the official code repository for the book [*Build a Reasoning Model (From Scratch)*](https://mng.bz/lZ5B).
<br>
<br>
<a href="https://mng.bz/lZ5B"><img src="https://sebastianraschka.com/images/reasoning-from-scratch-images/cover.webp?123" width="250px"></a>
(Printed in color.)
<br>
In [*Build a Reasoning Model (From Scratch)*](https://mng.bz/lZ5B), you will learn and understand how a reasoning large language model (LLM) works.
Reasoning is one of the most exciting and important recent advances in improving LLMs, but it’s also one of the easiest to misunderstand if you only hear the term reasoning and read about it in theory. This is why this book takes a hands-on approach. We will start with a pre-trained base LLM and then add reasoning capabilities ourselves, step by step in code, so you can see exactly how it works.
The methods described in this book walk you through the process of developing your own small-but-functional reasoning model for educational purposes. It mirrors the approaches used in creating large-scale reasoning models such as DeepSeek R1, GPT-5 Thinking, and others. In addition, this book includes code for loading the weights of existing, pretrained models.
- Link to the official [source code repository](https://github.com/rasbt/reasoning-from-scratch)
- Link to the [book at Manning](https://mng.bz/lZ5B) (the publisher's website)
- Link to the book page on Amazon.com (TBD)
- ISBN 9781633434677
<br>
<br>
To download a copy of this repository, click on the [Download ZIP](https://github.com/rasbt/reasoning-from-scratch/archive/refs/heads/main.zip) button or execute the following command in your terminal:
```bash
git clone --depth 1 https://github.com/rasbt/reasoning-from-scratch.git
```
<br>
> **Tip:**
> Chapter 2 provides additional tips on installing Python, managing Python packages, and setting up your coding environment.
<br>
<br>
## Table of Contents (In Progress)
[](https://github.com/rasbt/reasoning-from-scratch/actions/workflows/tests-linux.yml)
[](https://github.com/rasbt/reasoning-from-scratch/actions/workflows/tests-macos.yml)
[](https://github.com/rasbt/reasoning-from-scratch/actions/workflows/tests-windows.yml)
| Chapter Title | Main Code |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| Ch 1: Understanding reasoning models | No code |
| Ch 2: Generating text with a pre-trained LLM | - [ch02_main.ipynb](ch02/01_main-chapter-code/ch02_main.ipynb)<br/>- [ch02_exercise-solutions.ipynb](ch02/01_main-chapter-code/ch02_exercise-solutions.ipynb) |
| Ch 3: Evaluating reasoning models | TBA |
| Ch 4: Improving reasoning with inference-time scaling | TBA |
| Ch 5: Training reasoning models with reinforcement learning | TBA |
| Ch 6: Distilling reasoning models for efficient reasoning | TBA |
| Ch 7: Improving the reasoning pipeline and future directions | TBA |
| Appendix A: References and further reading | No code |
| Appendix B: Exercise solutions | Code and solutions are in each chapter's subfolder |
| Appendix C: Qwen3 LLM source code | - [chC_main.ipynb](chC/01_main-chapter-code/chC_main.ipynb) |
<br>
The mental model below summarizes the main techniques covered in this book.
<img src="https://sebastianraschka.com/images/reasoning-from-scratch-images/mental-model.webp" width="650px">
<br>
## Companion Book
Please note that *Build A Reasoning Model (From Scratch)* is a standalone book focused on methods to improve LLM reasoning.
In this book, we work with a pre-trained open-source base LLM (Qwen3) on top of which we code apply reasoning methods from scratch. This includes inference-time scaling, reinforcement learning, and distillation.
However, if you are interested in understanding how a conventional base LLM is implemented, you may like my previous book, [*Build a Large Language Model (From Scratch)*](https://amzn.to/4fqvn0D).
<a href="https://amzn.to/4fqvn0D"><img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/cover.jpg?123" width="120px"></a>
- [Amazon link](https://amzn.to/4fqvn0D)
- [Manning link](http://mng.bz/orYv)
- [GitHub repository](https://github.com/rasbt/LLMs-from-scratch)
<br>
## Hardware Requirements
The code in the main chapters of this book is designed to mostly run on consumer hardware within a reasonable timeframe and does not require specialized server hardware. This approach ensures that a wide audience can engage with the material. Additionally, the code automatically utilizes GPUs if they are available. That being said, chapters 2-4 will work well on CPUs and GPUs. For chapters 5 and 6, it is recommended to use a GPU if you want to replicate the results in the chapter.
(Please see the [setup_tips](ch02/https://github.com/rasbt/reasoning-from-scratch/blob/main/ch02/01_main-chapter-code/python-instructions.md) doc for additional recommendations.)
## Exercises
Each chapter of the book includes several exercises. The solutions are summarized in Appendix B, and the corresponding code notebooks are available in the main chapter folders of this repository (for example, [`ch02/01_main-chapter-code/ch02_exercise-solutions.ipynb`](ch02/01_main-chapter-code/ch02_exercise-solutions.ipynb)).
## Questions, Feedback, and Contributing to This Repository
I welcome all sorts of feedback, best shared via the [Manning Discussion Forum](https://livebook.manning.com/forum?product=raschka2&page=1) or [GitHub Discussions](https://github.com/rasbt/reasoning-from-scratch/discussions). Likewise, if you have any questions or just want to bounce ideas off others, please don't hesitate to post these in the forum as well.
Please note that since this repository contains the code corresponding to a print book, I currently cannot accept contributions that would extend the contents of the main chapter code, as it would introduce deviations from the physical book. Keeping it consistent helps ensure a smooth experience for everyone.
## Citation
If you find this book or code useful for your research, please consider citing it.
Chicago-style citation:
> Raschka, Sebastian. *Build A Reasoning Model (From Scratch)*. Manning, 2025. ISBN: 9781633434677.
BibTeX entry:
```
@book{build-llms-from-scratch-book,
author = {Sebastian Raschka},
title = {Build A Reasoning Model (From Scratch)},
publisher = {Manning},
year = {2025},
isbn = {9781633434677},
url = {https://mng.bz/lZ5B},
github = {https://github.com/rasbt/reasoning-from-scratch}
}
```
Raw data
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"author": "Sebastian Raschka",
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"description": "# Build A Reasoning Model (From Scratch)\n\nThis repository contains the code for developing an LLM reasoning model and is the official code repository for the book [*Build a Reasoning Model (From Scratch)*](https://mng.bz/lZ5B).\n\n\n<br>\n<br>\n\n<a href=\"https://mng.bz/lZ5B\"><img src=\"https://sebastianraschka.com/images/reasoning-from-scratch-images/cover.webp?123\" width=\"250px\"></a>\n\n(Printed in color.)\n\n<br>\n\nIn [*Build a Reasoning Model (From Scratch)*](https://mng.bz/lZ5B), you will learn and understand how a reasoning large language model (LLM) works.\n\nReasoning is one of the most exciting and important recent advances in improving LLMs, but it\u2019s also one of the easiest to misunderstand if you only hear the term reasoning and read about it in theory. This is why this book takes a hands-on approach. We will start with a pre-trained base LLM and then add reasoning capabilities ourselves, step by step in code, so you can see exactly how it works.\n\nThe methods described in this book walk you through the process of developing your own small-but-functional reasoning model for educational purposes. It mirrors the approaches used in creating large-scale reasoning models such as DeepSeek R1, GPT-5 Thinking, and others. In addition, this book includes code for loading the weights of existing, pretrained models.\n\n- Link to the official [source code repository](https://github.com/rasbt/reasoning-from-scratch)\n- Link to the [book at Manning](https://mng.bz/lZ5B) (the publisher's website)\n- Link to the book page on Amazon.com (TBD)\n- ISBN 9781633434677\n\n\n\n<br>\n<br>\n\nTo download a copy of this repository, click on the [Download ZIP](https://github.com/rasbt/reasoning-from-scratch/archive/refs/heads/main.zip) button or execute the following command in your terminal:\n\n```bash\ngit clone --depth 1 https://github.com/rasbt/reasoning-from-scratch.git\n```\n\n<br>\n\n\n> **Tip:**\n> Chapter 2 provides additional tips on installing Python, managing Python packages, and setting up your coding environment.\n\n<br>\n<br>\n\n## Table of Contents (In Progress)\n\n\n[](https://github.com/rasbt/reasoning-from-scratch/actions/workflows/tests-linux.yml)\n[](https://github.com/rasbt/reasoning-from-scratch/actions/workflows/tests-macos.yml)\n[](https://github.com/rasbt/reasoning-from-scratch/actions/workflows/tests-windows.yml)\n\n| Chapter Title | Main Code |\n| ------------------------------------------------------------ | ------------------------------------------------------------ |\n| Ch 1: Understanding reasoning models | No code |\n| Ch 2: Generating text with a pre-trained LLM | - [ch02_main.ipynb](ch02/01_main-chapter-code/ch02_main.ipynb)<br/>- [ch02_exercise-solutions.ipynb](ch02/01_main-chapter-code/ch02_exercise-solutions.ipynb) |\n| Ch 3: Evaluating reasoning models | TBA |\n| Ch 4: Improving reasoning with inference-time scaling | TBA |\n| Ch 5: Training reasoning models with reinforcement learning | TBA |\n| Ch 6: Distilling reasoning models for efficient reasoning | TBA |\n| Ch 7: Improving the reasoning pipeline and future directions | TBA |\n| Appendix A: References and further reading | No code |\n| Appendix B: Exercise solutions | Code and solutions are in each chapter's subfolder |\n| Appendix C: Qwen3 LLM source code | - [chC_main.ipynb](chC/01_main-chapter-code/chC_main.ipynb) |\n\n<br>\n \n\nThe mental model below summarizes the main techniques covered in this book.\n\n<img src=\"https://sebastianraschka.com/images/reasoning-from-scratch-images/mental-model.webp\" width=\"650px\">\n\n\n\n<br>\n\n\n\n \n## Companion Book\n\nPlease note that *Build A Reasoning Model (From Scratch)* is a standalone book focused on methods to improve LLM reasoning.\n\nIn this book, we work with a pre-trained open-source base LLM (Qwen3) on top of which we code apply reasoning methods from scratch. This includes inference-time scaling, reinforcement learning, and distillation.\n\nHowever, if you are interested in understanding how a conventional base LLM is implemented, you may like my previous book, [*Build a Large Language Model (From Scratch)*](https://amzn.to/4fqvn0D).\n\n<a href=\"https://amzn.to/4fqvn0D\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover.jpg?123\" width=\"120px\"></a>\n\n- [Amazon link](https://amzn.to/4fqvn0D)\n- [Manning link](http://mng.bz/orYv)\n- [GitHub repository](https://github.com/rasbt/LLMs-from-scratch)\n\n\n<br>\n \n\n## Hardware Requirements\n\nThe code in the main chapters of this book is designed to mostly run on consumer hardware within a reasonable timeframe and does not require specialized server hardware. This approach ensures that a wide audience can engage with the material. Additionally, the code automatically utilizes GPUs if they are available. That being said, chapters 2-4 will work well on CPUs and GPUs. For chapters 5 and 6, it is recommended to use a GPU if you want to replicate the results in the chapter.\n\n\n(Please see the [setup_tips](ch02/https://github.com/rasbt/reasoning-from-scratch/blob/main/ch02/01_main-chapter-code/python-instructions.md) doc for additional recommendations.)\n\n \n## Exercises\n\nEach chapter of the book includes several exercises. The solutions are summarized in Appendix B, and the corresponding code notebooks are available in the main chapter folders of this repository (for example, [`ch02/01_main-chapter-code/ch02_exercise-solutions.ipynb`](ch02/01_main-chapter-code/ch02_exercise-solutions.ipynb)).\n\n \n## Questions, Feedback, and Contributing to This Repository\n\n\nI welcome all sorts of feedback, best shared via the [Manning Discussion Forum](https://livebook.manning.com/forum?product=raschka2&page=1) or [GitHub Discussions](https://github.com/rasbt/reasoning-from-scratch/discussions). Likewise, if you have any questions or just want to bounce ideas off others, please don't hesitate to post these in the forum as well.\n\nPlease note that since this repository contains the code corresponding to a print book, I currently cannot accept contributions that would extend the contents of the main chapter code, as it would introduce deviations from the physical book. Keeping it consistent helps ensure a smooth experience for everyone.\n\n \n## Citation\n\nIf you find this book or code useful for your research, please consider citing it.\n\nChicago-style citation:\n\n> Raschka, Sebastian. *Build A Reasoning Model (From Scratch)*. Manning, 2025. ISBN: 9781633434677.\n\nBibTeX entry:\n\n```\n@book{build-llms-from-scratch-book,\n author = {Sebastian Raschka},\n title = {Build A Reasoning Model (From Scratch)},\n publisher = {Manning},\n year = {2025},\n isbn = {9781633434677},\n url = {https://mng.bz/lZ5B},\n github = {https://github.com/rasbt/reasoning-from-scratch}\n}\n```\n",
"bugtrack_url": null,
"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n \n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n \n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n \n 2. Grant of Copyright License. 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