A developer has created a compact language model with roughly 9 million parameters to explore the inner workings of language models. Utilizing a straightforward transformer architecture, the model was trained on an impressive dataset of 60,000 synthetic conversations, all implemented in about 130 lines of PyTorch code. Remarkably, it can be trained in just 5 minutes on a free Colab T4 instance. The quirky takeaway? A fish in this model believes that the meaning of life revolves around food. Users can also customize the personality of the model by forking the project and infusing their own character into it.