[All, Passing along an open source model I trained that you may find useful in your math research. Background: I’ve fine-tuned GPT-OSS-20B on an extensive, personally-curated corpus of analytic number theory research. While number theory was the focus, I also included adjacent mathematical content including random matrix theory, combinatorics, and real and complex analysis. Compared to the base model, the fine-tuned version now (I believe) successfully generates publication-quality mathematical exposition. Training Results: -27% validation loss improvement (0.547 → 0.400) -Zero overfitting—perfect generalization across 22,598 examples -Stable 3-epoch convergence using LoRA fine-tuning Performance on Advanced Mathematical Topics: At optimal configuration (Temperature 1.0, high r…