Huginn-0125 is a latent variable recurrent deep model developed by the Tom Goldstein Lab at the University of Maryland, College Park. This model, trained on 800 billion tokens, showcases exceptional performance in inference and code generation with its 3.5 billion parameters. Its core feature is the dynamic adjustment of computation at test time through a recurrent deep structure, allowing for flexible adaptation of computation steps based on task requirements, thereby optimizing resource utilization while maintaining performance. The model is available on the open-source Hugging Face platform, supporting community sharing and collaboration, allowing users to download, use, and further develop it freely. Its open-source nature and flexible architecture make it a vital tool in research and development, particularly in resource-constrained situations or where high-performance inference is necessary.