rasbt/reasoning-from-scratch

• highreadme#1

  Clarify the repository's primary role as a code-based implementation guide

  Why:

  CURRENT

  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)*.

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  This repository provides a practical, step-by-step code implementation guide for building a reasoning large language model (LLM) in PyTorch, serving as the official code companion for the book *Build a Reasoning Model (From Scratch)*. You will learn to add reasoning capabilities to a pre-trained base LLM from scratch.

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• mediumreadme#2

  Add a dedicated section highlighting the repository's practical code content

  Why:

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  ## What You'll Find in This Repository
  This repository is designed as a hands-on learning resource, providing:
  *   **Step-by-step PyTorch implementations** for adding reasoning capabilities to LLMs.
  *   **Modular code examples** demonstrating core reasoning concepts from first principles.
  *   **Code for loading weights** of existing, pretrained models to build upon.
  *   **Practical exercises** to deepen your understanding of LLM reasoning architectures.

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• lowtopics#3

  Augment topics with terms emphasizing practical implementation and tutorials

  Why:

  CURRENT

  ai, artificial-intelligence, deep-learning, deep-neural-networks, large-language-models, llms, machine-learning, python, pytorch, reasoning, reinforcement-learning

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  ai, artificial-intelligence, deep-learning, deep-neural-networks, large-language-models, llms, machine-learning, python, pytorch, reasoning, reinforcement-learning, llm-implementation, pytorch-tutorial, code-examples, machine-learning-tutorial

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How can I implement a reasoning large language model in PyTorch step-by-step?

  you: not recommended
  AI recommended (in order):

  1. Hugging Face Datasets
  2. GSM8K
  3. CommonsenseQA
  4. ARC (AI2 Reasoning Challenge)
  5. HotpotQA
  6. spaCy
  7. NLTK
  8. Hugging Face Transformers Tokenizers
  9. Hugging Face Transformers
  10. T5ForConditionalGeneration
  11. GPT2LMHeadModel
  12. T5-large
  13. GPT-J
  14. GPT-NeoX
  15. FlanT5ForConditionalGeneration
  16. LlamaForCausalLM
  17. Alpaca
  18. Vicuna
  19. PyTorch
  20. Hugging Face Accelerate
  21. PyTorch Lightning
  22. Hugging Face Evaluate
  23. ROUGE
  24. BLEU
  25. Exact Match
  26. Hugging Face Transformers `pipeline`
  27. ONNX Runtime
  28. TorchScript

  AI recommended 28 alternatives but never named rasbt/reasoning-from-scratch. This is the gap to close.

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• CATEGORY QUERY

  Seeking a practical guide to understand the internal workings of LLM reasoning.

  you: not recommended
  AI recommended (in order):

  1. The Illustrated Transformer
  2. What Does GPT-3 Really Know? A Closer Look at the Knowledge and Reasoning Abilities of Large Language Models
  3. Emergent Abilities of Large Language Models
  4. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  5. Transformers from Scratch
  6. Language Models are Few-Shot Learners
  7. Mechanistic Interpretability, Explained

  AI recommended 7 alternatives but never named rasbt/reasoning-from-scratch. This is the gap to close.

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• Metadata completeness

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• README presence

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• Compared to common alternatives in this category, what is the core differentiator of rasbt/reasoning-from-scratch?

  pass
  ✓AI named rasbt/reasoning-from-scratch explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts rasbt/reasoning-from-scratch in production, what risks or prerequisites should they evaluate first?

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  ✓AI named rasbt/reasoning-from-scratch explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo rasbt/reasoning-from-scratch solve, and who is the primary audience?

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  ✓AI named rasbt/reasoning-from-scratch explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?