HRM-Text1 is an experimental instruction-following text generation model based on the Hierarchical Recurrent Memory (HRM) architecture. It is trained on the databricks/databricks-dolly-15k dataset, which consists of instruction–response pairs across multiple task types.

The model utilizes the HRM structure, consisting of a "Specialist" module for low-level processing and a "Manager" module for high-level abstraction and planning. This architecture aims to handle long-range dependencies more effectively by summarizing information at different temporal scales.