llm-from-scratch

LLM from Scratch

A project to build and train a Large Language Model (LLM) from scratch, implementing core components and training procedures to understand how modern language models work.

🎯 Objective

The final goal is to train a complete LLM from scratch, scaling to whatever size your hardware allows. This project focuses on understanding the fundamentals of transformer architectures, tokenization, training loops, and model optimization.

This project is a learning exercise to understand LLMs at a fundamental level. The implementation will prioritize clarity and educational value over optimization.

Prerequisites

• Python 3.8+
• CUDA-capable GPU (recommended for training)
• Sufficient RAM/VRAM for your target model size

🛠️ Planned Components (not finalized)

• Tokenizer implementation (BPE/WordPiece)
• Transformer architecture (attention, feed-forward, layer norm)
• Positional encoding
• Training loop with gradient accumulation
• Data loading and preprocessing pipeline
• Model checkpointing and resuming
• Inference engine
• Model quantization (for deployment)

📚 Resources

• Attention Is All You Need - Original Transformer paper
• The Illustrated Transformer - Visual guide to transformers
• minGPT - Minimal GPT implementation reference

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