reasoning-from-scratch
Implement a reasoning LLM in PyTorch from scratch, step by step
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Decision brief
A step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization.
Good fit when
- When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS.
- If you prefer material that can run primarily on consumer-grade hardware, allowing flexibility in your development environment.
Avoid when
- Avoid this tool if you are looking for rapid prototyping or quick model deployment; it focuses more on understanding and building the LLM from scratch rather than providing prebuilt components.
- If specialized server hardware is available and preferred for the entire project, as chapters 5 and 6 recommend GPU use but earlier sections can be completed with just a CPU.
- Requirements:
- Automatic GPU utilization where available, though not strictly necessary for the early chapters.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Active (12d since push)
- As of 3d
- Provenance
- Not a fork · Personal account
- As of 3d
- Security (OSV)
- 15 low (15 low)
- As of 1mo
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Install
git clone https://github.com/rasbt/reasoning-from-scratchHow it fits your stack(7)
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Evidence and technical details
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Overview
A step-by-step guide to implement a reasoning large language model using PyTorch. The repository provides detailed instructions, suitable for running on consumer hardware including automatic GPU utilization where available.
Capability facts
- Languages
- jupyter notebook, python
Source: github.language+pyproject.toml · Aug 17, 2026
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 17, 2026)
(Please see the [setup_tips](ch02/02_setup-tips/python-instructions.md) doc for additional recommendations.)Source link
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README
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 doc for additional recommendations.)
For agents
This page has a .md twin and JSON over the API.