Show HN: I built a tiny LLM to demystify how language models work

Demystify complex Large Language Model (LLM) concepts by exploring a practical, small-scale implementation. This Action Pack guides you through setting up and experimenting with a tiny LLM, providing hands-on insight into foundational AI principles beyond abstract theory.

intermediate30 min5 steps

The play

Clone the GuppyLM Repository
Obtain the project's source code by cloning the GitHub repository to your local machine.
Set Up Your Python Environment
Create a virtual environment and install all necessary Python dependencies to run the tiny LLM.
Run a Basic LLM Demo
Execute a pre-built example to see the miniature LLM in action, demonstrating its fundamental text generation capabilities.
Explore Core LLM Components
Examine the project's Python files to understand the implementation of key LLM architectural elements like tokenization, attention mechanisms, and feed-forward layers.
Experiment with Parameters
Modify the model's configuration or training parameters (e.g., number of layers, hidden size, learning rate) and observe how these changes impact its behavior and output.

Starter code

git clone https://github.com/arman-bd/guppylm.git
cd guppylm
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python -m guppylm.example

Source

Repogithub.com