Sudoku RWKV : A specialized RWKV model for solving Sudoku puzzles.

Sudoku RWKV

#Sudoku #RWKV Model #Deep Learning #Python #Sudoku Solving Standard Picks Open Source

Overview :

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.

Target Users :

The target audience includes Sudoku enthusiasts, AI researchers, and developers who need Sudoku solving algorithms. Sudoku enthusiasts can quickly solve puzzles using this tool and enjoy the challenge; AI researchers can study and improve the model, exploring the applications of deep learning in games and logical problem-solving; developers can integrate this model into their applications to offer Sudoku solving features.

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Use Cases

Sudoku enthusiasts use Sudoku-RWKV to solve high-difficulty puzzles online.

AI researchers utilize the Sudoku-RWKV model for academic research, exploring optimizations and improvements.

Mobile app developers integrate Sudoku-RWKV into their Sudoku games to provide automatic solving features.

Features

- Use the RWKV model to solve Sudoku puzzles: Users can experience the model's solving capabilities directly by running the demo.py or minimum_inference.py files.

- Generate training data: By running generate_sudoku_data.py, users can create data to train the model.

- Optimize model parameters: The model includes code for simple performance improvements, enhancing solving efficiency.

- Support for various difficulty levels in Sudoku solving: The model can handle Sudoku puzzles from easy to complex.

- Provide model training details: Users can view hyperparameters and loss curves used during model training.

- Model files and vocabulary: Offers the trained model file sudoku_rwkv_20241120.pth and vocabulary file sudoku_vocab.txt.

- Comprehensive usage instructions and experimental results: The README file contains a quick start guide and displays experimental results.

How to Use

1. Visit the GitHub page for Sudoku-RWKV and clone or download the project files.

2. Ensure that Python and the required dependencies, such as rwkv and tkinter, are installed on your system.

3. Run the demo.py or minimum_inference.py file, input the initial layout of the Sudoku puzzle, and the model will output the solving process and results.

4. If you need to generate training data, run the generate_sudoku_data.py script.

5. Refer to the README file for detailed instructions and experimental results to understand the model's performance and usage specifics.

6. Modify model parameters or code as needed to suit different use cases.