Riemann Computing Inc. has published a technical account detailing the development of its 13.1-billion-parameter language model, My Name is Riemann, covering the model's staged growth process, training infrastructure, evaluation results and areas for further development. Published on October 28 through the OpenPeer AI organization on Hugging Face, the account describes how the model was developed from the published weights of OpenPeerLLM, a 163-million-parameter model, expanding the original model through successive stages to reach approximately 13.1 billion parameters in the current prerelease. The development process used the OpenPeer-AI-NTK-Trainer, which applies a neural tangent kernel-based gate controller to adjust model outputs without modifying the underlying base weights. Initial stages were carried out using a laptop with an external GPU, followed by later stages on a cloud computing node equipped with eight AMD Instinct MI325X GPUs, and the final model-development stage involved approximately 2.16 billion training tokens. Evaluation on a held-out dataset of 1,000 prompt-and-completion pairs reports a reduction in negative log-likelihood from 1.323 to 1.310 after applying the stage-six controller, alongside a change in token accuracy from 67.73% to 67.84%. The company notes these results should not be interpreted as evidence of general-purpose or frontier-level AI capabilities, and identifies limitations including the need for broader evaluation, additional training data and improvements to the training process.