Building GPT-2 from Scratch: A Complete LLM Implementation Project

This article introduces SharvChopra's open-source project LLM_Code on GitHub, which aims to implement the GPT-2 architecture from scratch, covering custom BPE tokenizer, data pipeline optimization, and complete implementation of Transformer core components, helping developers deeply understand the underlying principles of LLMs. Project link: https://github.com/SharvChopra/LLM_Code, published on May 26, 2026.

Most developers rely on high-level frameworks like PyTorch and Hugging Face, which are convenient but hide underlying details. This project strips away abstraction layers and builds GPT-2 from scratch, allowing learners to understand the mathematical principles and engineering implementations of LLMs by implementing tokenizers, data pipelines, and Transformer components.

The project implements a production-grade BPE tokenizer via Tokenizer_script.ipynb:

• Byte-level encoding handles any Unicode character, avoiding OOV (Out-of-Vocabulary) issues;
• Regex pre-tokenization splits text;
• Smart injection of special tokens (start/end markers) prevents incorrect splitting. This helps understand how GPT processes text and why BPE has become a standard.

Data_pipeline_from_scratch.ipynb designs a high-throughput data pipeline:

• Text normalization ensures data consistency;
• Fixed-length sequence packing facilitates batch computation;
• Random batch sampling prevents the model from memorizing patterns. These optimizations avoid GPU starvation (data supply can't keep up with computation speed).

Building_GPT_from_Basics.ipynb implements core components:

• Multi-head self-attention: Scaled dot-product attention, multi-head parallelism, causal masking to prevent forward leakage;
• Positional encoding and layer normalization: Sinusoidal/learnable positional embeddings, Pre-Norm strategy, residual connections to mitigate gradient vanishing;
• Weight tying: Tying weights of input embedding and output projection layers, reducing parameter count and improving efficiency.

The project discusses details of the inference phase:

• Computation/memory bottlenecks: Pre-filling phase (computation-intensive) vs decoding phase (memory bandwidth-limited);
• KV caching: Stores key-value pairs of previous tokens, avoiding redundant computations and significantly improving generation speed.

The project's value includes:

1. Principle understanding: Hands-on implementation of components to master concepts like attention and layer normalization;
2. Engineering practice: Complete workflow (data preprocessing → model building → training → inference);
3. Performance awareness: Cultivate sensitivity to optimizations like GPU starvation and memory constraints;
4. Research foundation: Provide a solid base for LLM research.

This project proves that LLMs are composed of interpretable mathematics and engineering techniques. It is recommended to learn in the order of the notebooks: Tokenizer → Data Pipeline → Transformer Core. As LLMs evolve, underlying implementation capabilities are crucial for model fine-tuning, architecture improvement, and application development.