kaya

Kaya - KataGo ONNX Models

This repository contains ONNX-converted versions of KataGo neural network models for the game of Go (Baduk/Weiqi).

These models power the Kaya app, a web-based Go application with AI-powered game analysis and move suggestions.

Model Description

These models are converted from the official KataGo PyTorch checkpoints to ONNX format for use in web-based and cross-platform applications.

Available Models

Each model is available in three versions:

• .fp32.onnx - Full precision (FP32) - Recommended for browser/WASM
• .fp16.onnx - Half precision (FP16) - For native apps (CoreML, CUDA, WebGPU)
• .uint8.onnx - Quantized (UINT8) - ~4x smaller, for memory-constrained devices

Usage

With ONNX Runtime (Python)

import onnxruntime as ort
import numpy as np

# Load the model (use .fp32.onnx for browser/WASM, .fp16.onnx for native apps)
session = ort.InferenceSession("kata1-b28c512nbt-adam-s11165M-d5387M.fp32.onnx")

# Prepare inputs (batch_size, channels, height, width)
bin_input = np.random.randn(1, 22, 19, 19).astype(np.float32)
global_input = np.random.randn(1, 19).astype(np.float32)

# Run inference
outputs = session.run(None, {
    "bin_input": bin_input,
    "global_input": global_input
})

policy, value, miscvalue, moremiscvalue, ownership, scoring, futurepos, seki, scorebelief = outputs

With ONNX Runtime Web (JavaScript)

import * as ort from "onnxruntime-web";

// Use .fp32.onnx for WASM backend, or .uint8.onnx for smaller download size
const session = await ort.InferenceSession.create(
  "kata1-b28c512nbt-adam-s11165M-d5387M.fp32.onnx"
);

const binInput = new ort.Tensor(
  "float32",
  new Float32Array(1 * 22 * 19 * 19),
  [1, 22, 19, 19]
);
const globalInput = new ort.Tensor(
  "float32",
  new Float32Array(1 * 19),
  [1, 19]
);

const results = await session.run({
  bin_input: binInput,
  global_input: globalInput,
});

Model Inputs

Model Outputs

Original Source

These models are derived from the KataGo project by David J. Wu (lightvector).

• Original Repository: https://github.com/lightvector/KataGo
• Training Data: https://katagotraining.org/
• Original Checkpoints: https://media.katagotraining.org/

License

The original KataGo neural network weights are released under the MIT License.

This ONNX conversion and the associated tooling are also released under the MIT License.

Citation

If you use these models, please cite the original KataGo paper:

@article{wu2019accelerating,
  title={Accelerating Self-Play Learning in Go},
  author={Wu, David J.},
  journal={arXiv preprint arXiv:1902.10565},
  year={2019}
}

Conversion Details

• Conversion Tool: katago-onnx
• ONNX Opset: 17
• FP16 Conversion: Internal computations in FP16, I/O remains FP32 for compatibility
• UINT8 Quantization: Dynamic quantization with QUInt8 weights
• Dynamic Axes: Batch size, board height/width are dynamic

Acknowledgments

Special thanks to:

• David J. Wu (lightvector) for creating KataGo
• The KataGo training community for providing trained networks