Remek Kinas announced the release of basal 2.2, which—he wrote—introduces the basal-calibrate tool for adjusting model calibration to the user's local data. According to him, the tool uses labeled examples from a given process, and the data is not shared externally or used for training the model. Kinas stated that he tested the solution in a new field where the model had not been trained. He wrote that using the "1%" setting from the provided file would result in automatically accepting every eighth decision, with 8% of those being incorrect. After calibration on the user's data, the calibration error for choice questions decreased from 0.268 to 0.083. In the case of the verify statements, the model automatically accepted 54.8% of the cases and made no errors in doing so. The release also included FP8 and GGUF ports for basal-omni-1.0-mini and max, as well as MLX ports for Apple Silicon Macs: mini, max, and 1.1-max. Kinas thanked Piotr Durlej for GGUF export, Kuba Sołtys for implementation on Google TPU, Michał Kardyś for work related to AMD Strix Halo, user lukaspoloki for basal-viz, and Paweł Kiszczak for exports to Apple Silicon MLX. The author also expressed gratitude for the posts and comments from the past weekend. He mentioned that he had responded to some of them and then worked on further improvements; he also announced that he would publish comments in the evening. #sztucznainteligencja #uczenieMaszynowe #technologia (translated)
