FareedKhan-dev / train-llm-from-scratch

2026-05-30 · Source: Github Trending: All languages · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

The FareedKhan-dev GitHub repository presents a PyTorch implementation of a Transformer model, built from scratch based on the "Attention is All You Need" paper. This project enables users to train custom Large Language Models (LLMs) with millions or billions of parameters on a single GPU. It details the use of The Pile dataset, an 825GB collection of 22 diverse datasets, and the `tiktoken` tokenizer (r50k_base). The repository provides a structured codebase, prerequisites including Python 3.8+ and PyTorch, and GPU recommendations, noting that a 13 million-parameter model can train on a Tesla T4, while billion-parameter models require more robust GPUs like an NVIDIA A100 (40 GB) or RTX 4090 (24 GB). The author demonstrates that a 13 million-parameter LLM can generate grammatically correct and somewhat meaningful text, while a 2 billion-parameter model, despite its size, requires a deeper architecture for improved coherence.

Key takeaway

For AI Scientists or Machine Learning Engineers exploring custom LLM development, begin by implementing and training a 13 million-parameter Transformer model using the provided scripts. This approach allows for rapid iteration and validation of core architectural components on accessible GPUs. Subsequently, consider scaling the model incrementally or fine-tuning it on specific datasets to achieve goal-oriented performance under 1 billion parameters, optimizing for secure, private data applications.

Key insights

Building a Transformer LLM from scratch reveals practical challenges and opportunities for model scaling and domain-specific fine-tuning.

Principles

• Causal masking is essential for autoregressive text generation.
• Layer normalization and residual connections stabilize deep Transformer training.
• Model size significantly impacts training complexity and output coherence.

Method

The process involves data preprocessing (The Pile to HDF5 with `tiktoken`), implementing MLP, attention mechanisms, and Transformer blocks, then training with `AdamW` and batch processing.

In practice

• Use HDF5 for efficient storage of tokenized training data.
• Start with 13M-parameter models for faster iteration and GPU compatibility.
• Fine-tune smaller LLMs on domain-specific data for targeted applications.

Topics

• Large Language Models
• Transformer Architecture
• PyTorch
• Deep Learning Training
• Natural Language Processing
• Attention Mechanism
• The Pile Dataset

Code references

• FareedKhan-dev/train-llm-from-scratch

Best for: Machine Learning Engineer, AI Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.