Moka

A cute Go-playing model.

Moka v1 is a 105,353-parameter policy and value network for 9×9 Go. Its 114 KB INT8 weights run entirely in the browser. With 64-visit full-symmetry search, this artifact won 95 of 200 fresh games against KataGo b6c96, compared with 78 for the previous model on the same openings.

Moka was distilled from KataGo b6c96 using teacher games and positions reached by Moka’s own rollouts.

Moka’s browser path is about 165× smaller than the teacher path. The point is not to replace KataGo. It is to put a learned Go player inside an ordinary webpage.

Moka runs inference in a Web Worker, which keeps model execution off the main thread.

These are the medians of five Chromium runs on Apple Silicon. Each run measured 100 positions after 10 warmups. Latency includes Worker messaging; inference stays off the main thread.

Embed Moka by serving its browser client, worker, manifest, and weights with your site. The example below runs without a framework or bundler.

Build Moka from this repository, then copy its four runtime files into your site’s public directory.

ni
nr build
mkdir -p ../your_site/public/moka
cp dist/index.js ../your_site/public/moka/index.js
cp dist/worker.js ../your_site/public/moka/worker.js
cp model/go-model.json ../your_site/public/moka/go-model.json
cp model/go-model.bin ../your_site/public/moka/go-model.bin

The deployed site should serve these files from /moka. Use different URLs in the initialization options if you choose another directory.

Run this code from a JavaScript module, such as an app.js file loaded with type="module".

import {
  GoModelWorkerClient,
  createGameState,
  encodeStudentFeatures,
  getAutomaticallyDeadMoves,
  getAreaScore,
  playMove,
  removeDeadStones,
  selectHighestLegalMove,
} from "/moka/index.js";

const mokaWorker = new Worker("/moka/worker.js", {
  type: "module",
});
const moka = new GoModelWorkerClient(mokaWorker);

await moka.initialize({
  manifestUrl: "/moka/go-model.json",
  weightsUrl: "/moka/go-model.bin",
});

let gameState = createGameState();

initialize downloads the manifest and weights, verifies the model digest, and loads the model inside the worker.

Encode the current game state, run inference, and select the highest-rated legal move.

const inference = await moka.infer(encodeStudentFeatures(gameState));
const mokaMove = selectHighestLegalMove(gameState, inference.policyLogits);
const nextGameState = playMove(gameState, mokaMove);

if (nextGameState) {
  gameState = nextGameState;
}

const currentPlayerWinProbability = (inference.value + 1) / 2;

The value result ranges from -1 to 1 for the player who was next to move. The final line converts it to a probability from 0 to 1.

Map board intersections to move indexes with row * 9 + column. Indexes 0 through 80 represent board points in row-major order, starting at the top-left. Index 81 represents a pass. playMove returns null when a move is illegal.

Call moka.dispose() when your page or component no longer needs the model. This terminates the worker and rejects pending requests.

Two passes stop play, but dead stones must be removed before area scoring. Moka combines conservative pass-alive analysis with a capture aftermath to identify them automatically:

const deadMoves = getAutomaticallyDeadMoves(gameState);
const adjudicatedState = removeDeadStones(gameState, deadMoves);
const blackLead = getAreaScore(adjudicatedState);

Pass-alive groups and unresolved seki remain on the board. removeDeadStones returns a copy and leaves the played position unchanged. Positive scores lead for Black; negative scores lead for White, including 7 points of komi.

Serve the assets over HTTP or HTTPS because browsers do not start module workers from file:// pages. A Content Security Policy (CSP) must allow the worker URL through worker-src. Cross-origin asset URLs also need Cross-Origin Resource Sharing (CORS) headers.

Install the JavaScript and Python dependencies before training or building Moka.

uv sync
ni

Place KataGo b6c96 at teachers/katago-b6c96.onnx.

Generate positions, train the network, and export its browser weights.

uv run go-generate --positions 50000
uv run go-train --data data/katago-distillation.npz --epochs 30
uv run go-export

Build the browser client and worker into dist.

nr build

Run the TypeScript checks and Python test suite before publishing changes.

nr check
nr test

MIT