# llm-core-from-scratch: Building Core LLM Modules from Scratch

> Dual implementations (NumPy & PyTorch) of core LLM components for interview preparation and understanding of underlying mechanisms—handwritten module by module, shape change analysis, and numerical validation

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-06T09:42:52.000Z
- 最近活动: 2026-06-06T09:55:11.797Z
- 热度: 150.8
- 关键词: 大语言模型, Transformer, NumPy, PyTorch, 手撕代码, 面试准备, 深度学习, 注意力机制
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-core-from-scratch
- Canonical: https://www.zingnex.cn/forum/thread/llm-core-from-scratch
- Markdown 来源: floors_fallback

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## Introduction: llm-core-from-scratch—Master Core LLM Modules from Zero

### Project Basic Information
- Original Author/Maintainer: XUHIT
- Source Platform: GitHub
- Project Positioning: Educational project for interview handwritten coding and understanding of underlying mechanisms
- Core Features: Handwritten key LLM components module by module, dual NumPy & PyTorch implementations, including knowledge explanation, shape change analysis, code parsing, and numerical validation

This project aims to solve the pain point that developers "can use LLM libraries but don't understand internal principles", helping to deeply understand the core mechanisms of large language models.

## Project Background: Pain Points and Needs of Developers

Many current developers can skillfully use the Hugging Face Transformers library to call pre-trained models, but have a superficial understanding of internal operation mechanisms. When asked about Transformer attention mechanism calculation, differences between LayerNorm and BatchNorm in interviews, they often can only recite concepts but can't explain in depth.

The llm-core-from-scratch project was born to solve this pain point.

## Core Approach: NumPy-First and PyTorch Comparative Dual Implementations

#### NumPy First Principle
1. **Transparency**: Every line of code is clearly visible, no black-box operations
2. **Depth of Understanding**: Manually handle details like shape transformation, matrix multiplication, and broadcasting mechanism
3. **Interview Preparation**: Close to algorithm interview handwritten code scenarios
4. **Numerical Sensitivity**: Cultivate sensitivity to numerical stability and precision issues

#### PyTorch Comparative Implementation
- Compare differences between framework and manual implementation
- Understand automatic differentiation mechanism
- Learn to migrate prototype code to production environment
- Verify correctness of manual implementation

## Project Structure and Core Module Analysis

### Project Structure
- **src/llm_core_from_scratch/**: Core implementation code, modules are independent for separate learning and testing
- **docs/**: In-depth principle teaching documents
- **notes/**: Study notes and derivation processes
- **experiments/**: Experiment code and validation scripts
- **tests/**: Unit tests
- **results/**: Experiment results and visualization

### Core Module Technical Points
- **Attention Mechanism**: Scaled Dot-Product, Multi-Head, Self/Cross Attention, Causal Masked Attention
- **Positional Encoding**: Sine-Cosine, Learnable, RoPE
- **Normalization Layers**: LayerNorm, RMSNorm
- **Feed-Forward Network**: FFN/MLP (including GELU, SwiGLU)
- **Other Components**: Embedding layer, residual connection, Dropout

## Suggested Learning Path

#### Stage 1: NumPy Handwritten Implementation
Try to implement modules with NumPy yourself, compare with project code, focus on shape changes, matrix operations, and numerical precision

#### Stage 2: Deep Dive into Principles
Read docs/ and notes/ documents, understand design decisions (e.g., why Attention divides by sqrt(d_k))

#### Stage 3: PyTorch Migration
Learn efficient framework implementation, understand abstraction and optimization

#### Stage 4: End-to-End Assembly
Assemble modules into a complete Transformer model, conduct simple training experiments

## Target Audience and Practical Value

**Target Audience**:
- Algorithm interview candidates: Provides standard reference and in-depth analysis for handwritten questions
- Deep learning researchers: Aids architecture improvement and model debugging
- AI engineers: Enhances technical decision-making ability
- Students: Systematic practical material for learning Transformer

**Practical Value**: Fills the gap between "can use LLM" and "understand LLM", balancing learning depth and engineering practicality

## Summary: Underlying Principles Are the Core of Technical Competitiveness

llm-core-from-scratch is a high-quality educational open-source project, ensuring learning depth and engineering practicality through dual implementations.

In today's fast-evolving LLM technology, core concepts like attention mechanism, normalization, and positional encoding are still fundamental. Mastering underlying principles allows one to maintain competitiveness in the wave of technology.

It is recommended to learn with project code and official Transformer papers for better results.