Implementing Large Language Models from Scratch: The Learning and Practice Journey of the LLMPractice Project

This article introduces the LLMPractice project, an open-source project on GitHub by kelan5111. It aims to help learners gain an in-depth understanding of the working principles and implementation details of LLMs by implementing each component of a large language model through hands-on coding. The project adopts a 'learn-by-doing' approach, allowing learners to shift from calling APIs to mastering underlying mechanisms, laying the foundation for innovation. Original project link: https://github.com/kelan5111/LLMPractice, published on May 29, 2026.

Large language models like GPT and Claude have become popular technologies in the AI field, but they remain a 'black box' for most learners. The LLMPractice project helps learners through a combination of reading textbooks and hands-on code implementation:

1. Gain an in-depth understanding of core concepts such as attention mechanisms and Transformer architecture
2. Master model training techniques and engineering practices
3. Develop an intuitive understanding of model behavior
4. Lay the foundation for future innovation This 'learn-by-doing' method is a classic path to understanding complex technologies.

A complete LLM consists of multiple key components. The project covers the following implementation content:

1. Word Embedding

Convert text symbols into continuous vectors, including One-hot encoding, dense embedding, positional encoding, and subword tokenization (BPE, etc.)

2. Attention Mechanism

The core of Transformer, including self-attention, multi-head attention, scaled dot-product attention, and masked attention

3. Transformer Architecture

Composed of encoder/decoder, including feed-forward networks, layer normalization, residual connections, and Dropout

4. Training Process

Data preparation (corpus cleaning, tokenization), training loop (forward/backward propagation, optimizer), training techniques (gradient clipping, mixed precision)

5. Inference Generation

Greedy decoding, random sampling, Temperature adjustment, Top-k/Top-p sampling

Recommended learning path to follow the project:

Stage 1: Basic Preparation

Review deep learning fundamentals (PyTorch/TensorFlow), understand neural network forward/backward propagation, and familiarize yourself with NLP basics

Stage 2: Core Implementation

Start with n-gram models → word embedding layer → attention mechanism → assemble Transformer layers

Stage 3: Training and Optimization

Prepare small-scale datasets → implement training loops and evaluation → debug and optimize performance → experiment with hyperparameters

Stage 4: Expansion and Deepening

Read classic papers (GPT, BERT) → compare with official implementations → add new features (LoRA, quantization) → participate in community discussions

Resources referenced by the project: Textbooks:

• Deep Learning (Goodfellow et al.)
• Dive into Deep Learning (Li Mu)
• Natural Language Processing with Transformers (Hugging Face) Papers:
• Attention Is All You Need
• GPT-1/2 papers
• Llama papers Online Resources:
• Andrej Karpathy's 'Let’s build GPT from scratch' video
• Hugging Face Transformers source code
• PyTorch official tutorials

Common challenges and solutions encountered during LLM implementation:

Numerical Stability

Problem: Gradient vanishing/explosion → Solutions: Layer normalization, residual connections, gradient clipping, weight initialization

Memory Limitations

Problem: Insufficient GPU memory → Solutions: Gradient accumulation, mixed precision training, checkpointing activations, parallel training

Training Efficiency

Problem: Long training time → Solutions: GPU/TPU, optimized data loading, distributed training, PyTorch 2.0 compilation

Value of the LLMPractice project:

1. Lower learning barriers by providing runnable code
2. Promote knowledge dissemination; open-source sharing benefits more people
3. Cultivate engineering capabilities through complete training from theory to practice
4. Stimulate innovation; understanding the underlying layers makes it easier to propose improvements Summary suggestions: Follow the project to reproduce step by step, read relevant textbooks and papers, be brave to experiment and debug, and participate in community discussions. Remember: The insights gained from implementing an LLM by hand (even a simple one) far exceed those from using ready-made models.