It produces text that seems to me at least equivalent to small LLMs, such as those produced by nanoGPT. Here is an example:
jplr@mypass:~/Documenti/2025/SimpleModels/v3_very_good$
./SLM10b_train UriAlon.txt 3
Training model with order 3...
Skip-gram detection: DISABLED (order < 5)
Pruning is disabled
Calculating model size for JSON export...
Will export 29832 model entries
Exporting vocabulary (1727 entries)...
Vocabulary export complete.
Exporting model entries...
Processed 12000 contexts, written 28765 entries (96.4%)...
JSON export complete: 29832 entries written to model.json
Model trained and saved to model.json
Vocabulary size: 1727
jplr@mypass:~/Documenti/2025/SimpleModels/v3_very_good$ ./SLM9_gen model.json
Aging cell models require comprehensive event data. Getting such a large medical database of joints has risk factors. Therefore, the theory can be expanded to describe the development of atherosclerosis and metabolic syndrome. For example, end-stage type 2 diabetes is associated with the decline of beta-cell function. There are two parameters for this decline: The fraction of senescent cells is predicted to influence the extent of the disease. For each individual, senescent-cell abundance is simulated using the SR model, which has a nearly exponential incidence curve with a decline in aging. In this section, we have simulated a wide range of age-related incidence curves. The next sections provide examples of classes of diseases that show improvement upon senolytic treatment and qualitatively support such predictions. Various disease thresholds are modeled as disease values when the physiological parameter ϕ increases due to disease. The increasing sensitivity parameter, which varies approximately 3-fold between BMIs below 25 (men) and 54 (women), is least related to age and 25 (men) and 28 (women) are strongly age-related, as defined above. Of these, we found that 66 are well described by the model as having a wide range of feedback mechanisms that can provide homeostasis in days of half-life in young mice, but that their removal rates slow down in older mice. For a given type of cancer, there should be stronger risk factors, leading to increased removal rates in joints that endure the most common biological process of aging that controls the onset of pathology in at least 104 records of people, A total of 877 disease category codes (see SI section 9), increasing the range by 6–8% per year. The two-parameter model describes strongly age-related ICD9 codes well: 90% of codes show R 2 > 0.9) (Figure 4c). This agreement is similar to the IMII model previously proposed for cancer, major fibrotic diseases, and hundreds of other age-related disease conditions, yielding a 10-4 reduction in cancer incidence. A better fit is obtained when allowing for disease classes to exceed their boundary mechanisms, providing a predicted etiology for diseases with unknown origin, such as bone marrow and skin. Thus, the immune response of the cancer leads to sudden collapse of the alveoli to the outer parts. For example, NK cells also remove senescent cells that contribute to other types of age-related damage and degradation (de Bourcy et al., 2017). This can be described as a first-passage-time problem that, when mutated, hinders the removal of particles by the bronchi and causes damage to alveolar cells (Yang et al., 2019; Xu et al., 2018), and immunotherapy that causes T cells to target senescent cells (Amor et al., 2020). Because these treatments are predicted to have an exponential incidence curve that slows at very high ages. Interestingly, the main effects are opposite to those for cancer growth rate as is the case for removal rate. We next consider the case of frontline tissues discussed above.