TinyLLM offers a hands-on exploration of Large Language Models (LLMs), focusing on building and training a model from scratch using transformer architecture principles. Inspired by the GPT-2 model, TinyLLM utilizes transformer decoder blocks to create a simplified yet functional LLM. The source code for this project is available on GitHub.
The model is trained on the tiny-stories dataset for approximately one and a half hours on an NVIDIA A40 GPU. You can view the training run on wandb.
TinyLLM provides an approachable way to understand LLM mechanics, including tokenizer setup, model training, and evaluation. While the model is simplified and not optimized compared to state-of-the-art LLMs, it serves as an educational tool, making the complexities of transformers and neural networks more accessible.
Key features include user-friendly scripts for model training, tokenizer configuration, and performance evaluation. It also supports cloud-based training on GPUs via Runpod.io, enabling scalable experimentation and learning.
This project is ideal for those eager to delve into LLMs and transformer models, offering a practical foundation for understanding and experimenting with advanced neural network architectures.