AstroPT trains astronomical large observation models using imagery data. The code follows a similar saturating log-log scaling law to textual models and the models' performances on downstream tasks as measured by linear probing improves with model size up to the model parameter saturation point. Other modalities can be folded into the AstroPT model, and use of a causally trained autoregressive transformer model enables integration with the wider deep learning FOSS community.