GuppyLM is a 9M parameter language model trained from scratch on 60,000 synthetic single-turn conversations across 60 topics, simulating a fish named Guppy that responds in short lowercase sentences about tank life, food, and water.

It uses a vanilla 6-layer transformer architecture with 384 hidden dimensions, a 4096 BPE vocabulary, and a 128-token context, trainable in 5 minutes on a T4 GPU via Colab notebook. Pre-trained weights and dataset are on HuggingFace, with code for local inference and custom training available on GitHub under MIT license.