Building Large Language Models from Scratch: A Complete Learning Guide

This post introduces the GitHub project Large-Language-Models-From-Scratch maintained by ahmed-m-sharaf, which provides complete code and detailed explanations for implementing large language models (LLMs) from scratch. It covers core concepts such as Transformer architecture, attention mechanisms, and training workflows, and is suitable for learners who wish to deeply understand the principles of LLMs. Original project link: https://github.com/ahmed-m-sharaf/Large-Language-Models-From-Scratch, published on 2026-05-24.

Building LLMs from scratch has three key values:

1. Deep understanding of principles: By implementing components like multi-head attention yourself, you can grasp the meaning of Query/Key/Value and the logic of capturing dependencies;
2. Engineering skill development: Solve challenges such as large-scale data processing, memory management, and distributed training;
3. Customization needs: Modify the Tokenizer or try novel attention variants to meet specific scenario requirements.

The project's core modules include:

• Data preprocessing and Tokenization: Text cleaning, Tokenizer implementation (character-level/BPE), vocabulary construction, sequence processing (Padding/Truncation/Batching);
• Transformer architecture: Self-attention (Scaled Dot-Product/multi-head/causal masking), positional encoding (sinusoidal/RoPE/learnable), feed-forward networks and normalization (residual connections);
• Training optimization: Data loading/loss function (cross-entropy)/optimizer (Adam/AdamW)/learning rate scheduling, plus techniques like gradient accumulation, mixed-precision training, and gradient clipping;
• Inference generation: Autoregressive generation, decoding strategies (greedy/random sampling/Top-k/Top-p/Temperature adjustment).

Suggested learning paths for readers with different backgrounds:

• Deep learning beginners: First master data preprocessing and PyTorch/TensorFlow basics, then dive into attention mechanisms, and train small models on small datasets (e.g., Shakespeare's texts);
• Experienced NLP engineers: Focus on comparing positional encoding differences, training optimization techniques, reproducing classic architecture variants, and exploring quantization acceleration;
• AI researchers: Validate new architectures based on the project, implement attention variants, and study sparse/linear attention and model compression/distillation.

Common challenges in practice and their solutions:

• Computational resource limitations: Use small datasets (WikiText-2/TinyStories), reduce model size, or fine-tune using pre-trained weights;
• Training instability: Adopt Xavier/Kaiming initialization, learning rate warm-up, and gradient clipping;
• Long text processing: Sliding window attention, sparse attention (Longformer/BigBird), and chunk processing.

Further learning resources:

• Must-read papers: Attention Is All You Need, GPT Series, BERT, Scaling Laws;
• Recommended books: Natural Language Processing with Transformers, Speech and Language Processing, The Little Book of Deep Learning;
• Online courses: Stanford CS224N, CS25 (Transformers United), Fast.ai NLP courses.

This project provides a valuable starting point for deeply understanding LLMs. By implementing components with your own hands, you not only master technical details but also develop the ability to solve practical problems. In an era of rapid AI development, understanding underlying principles is more valuable than calling APIs—you gain the ability to design new architectures and optimize models. Whether you are a student, engineer, or researcher, it is worth investing time in learning. Hands-on practice is the best way—open your IDE and start writing your first Transformer!