Building Modern LLM from Scratch: A Tutorial-level Implementation of Llama-style Language Model

This article introduces an open-source project for building modern large language models from scratch. The project evolves from a GPT-2-style basic architecture to a Llama 2/3-style production-level implementation, covering complete tutorial implementations of key technologies such as RMSNorm, RoPE, SwiGLU, GQA, and MoE.

This project was developed by RangeshPandianPT and implements a modern large language model architecture from scratch using PyTorch. Its core goal is to enable learners to deeply understand the internal mechanisms of modern LLMs by building code themselves, rather than just staying at the theoretical level or calling high-level APIs.

Unlike traditional tutorials, this project provides a complete evolution path: starting from a simple GPT-2-style baseline model, gradually introducing key components of modern LLMs, and finally reaching an architecture close to Llama 2/3 style. This progressive learning method allows developers to clearly see the practical effects of each technical improvement.

The project uses RMSNorm (Root Mean Square Normalization) to replace the traditional LayerNorm. RMSNorm performs normalization by calculating the root mean square of the input vector, omitting the mean calculation step in LayerNorm, thus reducing computational overhead while maintaining training stability. This improvement has become a standard in modern LLMs, especially performing better when handling long sequences.

RoPE (Rotary Positional Embeddings) is another key technology introduced in this project. Unlike absolute positional encoding, RoPE injects positional information into attention calculations via rotation matrices, allowing the model to better generalize to sequences longer than those used in training. This relative positional encoding method has become the standard configuration for Llama series models.

In the FeedForward network, the project uses the SwiGLU (SiLU Gated Linear Unit) activation function. SwiGLU combines the characteristics of a gating mechanism and the Swish activation function, providing stronger expressive power compared to traditional ReLU or GELU. Although this improvement seems simple, it has a significant impact on the final performance of the model.

Grouped Query Attention (GQA) is an important optimization technology introduced in Llama 2/3. Traditional multi-head attention (MHA) maintains independent Key and Value heads for each Query Head, leading to large memory overhead during inference. GQA reduces this by allowing multiple query heads to share the same set of key-value heads, significantly lowering inference memory usage while ensuring model quality.

The project also implements the Mixture of Experts (MoE) sparse routing mechanism, a technology adopted by advanced models like Mixtral 8x7B. MoE allows the model to drastically increase parameter count while keeping computational costs unchanged. By dynamically selecting activated expert sub-networks via a routing network, it decouples computational efficiency from model capacity.

In addition to core architecture improvements, the project showcases several engineering highlights:

Modular code structure: Configuration, model definition, training logic, and tokenizer are separated into independent files (config.py, model.py, train.py, tokenizer.py), making the code easy to understand and extend.

Mixed precision training: Automatic mixed precision training is implemented via torch.amp, enabling significant training acceleration on modern GPUs.

KV cache optimization: Efficient O(N) complexity generation inference is implemented, avoiding redundant self-attention calculations during the generation phase.

Large dataset handling: Uses numpy.memmap technology to process datasets exceeding memory capacity, allowing the project to handle large-scale training corpora.

Custom BPE tokenizer: The project includes a Byte Pair Encoding tokenizer trained from scratch, helping learners understand the full tokenization process.