This repository is dedicated to building a Large Language Model (LLM) from scratch, inspired by Sebastian Raschka's book. The goal is to gain a deep understanding of the fundamental concepts behind modern LLMs by implementing key components from the ground up.
With the rise of powerful LLMs like GPT, understanding their inner workings is crucial for researchers and developers. Instead of relying solely on pre-built models, this project aims to break down the architecture, training methodologies, and optimizations that power these models.
- Tokenization – Implementing custom tokenizers and handling different vocabularies.
- Embeddings – Word embeddings and positional encodings.
- Transformer Architecture – Building attention mechanisms and feed-forward layers.
- Training Pipeline – Implementing a scalable training loop with optimizations.
- Evaluation & Fine-tuning – Assessing performance and fine-tuning the model.
- Deployment – Exploring ways to serve the trained LLM efficiently.
- Sebastian Raschka's book (Build a Large Language Model)
- Research papers on Transformers and Language Models
- Open-source implementations like GPT-2, BERT, and LLaMA
Contributions and discussions are welcome! If you want to collaborate, feel free to fork the repository and submit pull requests.
I'll be documenting my progress through commits and updates. If you're interested in LLMs, star ⭐ this repo and follow along!
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