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

5 Steps

1. 1

   Clone the GuppyLM Repository: Obtain the project's source code by cloning the GitHub repository to your local machine.

2. 2

   Set Up Your Python Environment: Create a virtual environment and install all necessary Python dependencies to run the tiny LLM.

3. 3

   Run a Basic LLM Demo: Execute a pre-built example to see the miniature LLM in action, demonstrating its fundamental text generation capabilities.

4. 4

   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.

5. 5

   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.