Show HN: I have trained a model to predict my blood sugar

I have trained a custom 31,253 parameter encoder-only transformer on a pregenerated synthetic CGM dataset and fitted it to my own personal data using LoRA. It is competitive among other published models. I used 115 days of CGM data, of which 80 was used for training the adapter and 35 for testing. The model began with a pretraining stage where it consumed ~19,200 CGM-years worth of synthetic data, consisting of 1 million simulated type 1 diabetics at 7 days each. The simulator is designed to aid in counterfactual reasoning, but I don't yet have a benchmark to test these capabilities downstream. Simply using these models on my phone gives me the impression that they do indeed react appropriately when forced to consider future insulin & carbs. In addition to time series input, two additional inputs are provided: insulin (U) and carbs (G, GI) as action curves where U = units of insulin, G = carbs in grams, GI = glycemic index. U is exact, while (G, GI) is my best estimate. The model is trained to output BG in Kovatchev risk-space using {DILATE,MSE} and pinball loss combined with learned Kendall-Gal weights. The figures above show models trained using DILATE. It can predict 2 hours int