Package Exports

@openfluke/welvet
@openfluke/welvet/dist/index.js

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Readme

@openfluke/welvet

M-POLY-VTD AI Engine (Loom v0.75.0) — Isomorphic TypeScript/WASM library for building, training, and evolving neural networks with 21 numerical types, WebGPU acceleration, and DNA-based evolution.

Features

Isomorphic Architecture: Runs seamlessly in Node.js, Bun, and the Browser (React/Frontend).
Multi-Precision Support: Native support for 21 numerical types (FP64, FP32, FP16, BF16, FP8, INT8... all the way down to Binary/Ternary).
Hybrid Training: standard Backprop, Target Propagation, and NEAT-style structural evolution in a single unified engine.
Hardware Acceleration: WebGPU support for high-performance inference and training in the browser.
DNA Evolution: Network "DNA" extraction and comparison for architectural analysis and genetic recombination.

Installation

npm install @openfluke/welvet
# or
bun add @openfluke/welvet

Quick Start

1. Initialization

The engine initializes automatically after a single call to init(). It detects whether it's running in Node.js, Bun, or the Browser and loads the appropriate WASM environment.

import welvet from "@openfluke/welvet";

await welvet.init(); // Auto-detects Node.js/Browser and loads WASM

[!TIP] In browser environments, if your assets are served from a non-standard path, you can optionally pass a custom URL: await init("/custom/path/to/main.wasm").

2. Create a Network

import welvet from "@openfluke/welvet";

// 1D sequential stack on the Volumetric Grid (cell 0,0,0)
const net = welvet.createNetwork({
  depth: 1, rows: 1, cols: 1, layers_per_cell: 2,
  layers: [
    { z: 0, y: 0, x: 0, l: 0, type: "Dense", input_height: 784, output_height: 256, activation: "ReLU" },
    { z: 0, y: 0, x: 0, l: 1, type: "Dense", input_height: 256, output_height: 10, activation: "Linear" }
  ]
});

const input = new Float32Array(784).fill(0.5);
const output = net.sequentialForward(input);
console.log("Prediction:", output[0]); // Returns Float32Array

3. Training

import { trainNetwork } from "@openfluke/welvet";

const batches = [
  { input: [0.1, 0.2, ...], target: [1, 0, ...] },
];

const result = trainNetwork(net, batches, 10, 0.001);
console.log("Final Loss:", result.final_loss);

4. Transformer (LLM Context)

// Create an MHA layer (Multi-Head Attention)
const tnet = welvet.createNetwork({
  layers: [{ z: 0, y: 0, x: 0, l: 0, type: "MHA", d_model: 64, num_heads: 4 }]
});

// Wrap in Transformer context with pre-loaded weights
const model = welvet.createTransformer(tnet, embeds, lmHead, finalNorm);
const logits = model.forwardFull([1, 2, 3, 4]); // Direct token prefill

5. NEAT Evolution

const population = welvet.createNEATPopulation(net, 100);
const fitnesses = new Array(100).fill(1.0);

population.evolveWithFitnesses(fitnesses);
console.log("Top Fitness:", population.bestFitness());

Testing & Benchmarks

The package includes a built-in TypeScript testing suite suitable for both CI and environment verification.

Run tests in Node.js:

npm test

Or individual suites:

npm run test:cabi    # Check WASM exports and functional smoke tests
npm run test:bench   # Run layer-by-layer performance benchmarks

Integrate tests into Frontend/React:

import { runVerify } from "@openfluke/welvet/tests/cabi_verify";

// Run benchmarks or verification logic directly in your app's lifecycle
await runVerify();

License

Apache-2.0