Sudoku-RWKV is a Sudoku solving tool based on the RWKV model, leveraging deep learning techniques to tackle Sudoku problems. This model has been specifically trained to handle a large number of Sudoku samples, achieving a high rate of accuracy in solving puzzles. Background information indicates that the model was trained using approximately 2M Sudoku samples, covering around 39.2B tokens, with about 12.7M parameters, a vocabulary size of 133, and an architecture consisting of 8 layers, each with 320 dimensions. The main advantages of this model are its efficiency and high accuracy, enabling it to solve any solvable Sudoku puzzle.