GambitFlow/Nexus-Core

♟️ GambitFlow Nexus-core

Live Engine Demo • Dataset Card

📖 Model Overview

Nexus-core (Core Edition) is the primary engine of the GambitFlow project. It is a deep Residual Neural Network designed to evaluate chess positions with high-level human-like intuition. Unlike traditional engines that use manual "if-then" logic, Nexus-core learned chess strategy by analyzing over 5 million positions played by elite grandmaster-level players.

It serves as the static evaluation function (the "Brain") for the GambitFlow search algorithm, providing a numeric advantage score for any given board state.

🧠 Technical Architecture

The model is built using a Residual Neural Network (ResNet) architecture, inspired by the AlphaZero research but highly optimized for efficient inference in web browsers and low-power devices.

Specification Table

📊 Training Methodology

Nexus-core was trained using a professional-grade pipeline designed for robustness and strategic depth.

1. The Elite Dataset

We utilized the gambitflow-elite-data corpus:

• Filtering: Only games where both players had ELO > 2000.
• Stage: Focused on the first 20 moves to master opening and middlegame transition.
• Volume: ~5,000,000 unique positions.

2. Training Protocol

• Mixed Precision: Trained using torch.amp (FP16) for 2x faster convergence and memory efficiency.
• Optimizer: AdamW with a weight decay of 1e-4 to prevent overfitting.
• Loss: MSE (Mean Squared Error) between the predicted value and the actual game result.

3. ONNX Optimization

• Exported with Opset 17.
• Constant Folding and Graph Optimization enabled to reduce latency during real-time play.

💻 Implementation (JavaScript)

Designed for seamless integration with onnxruntime-web.

import * as ort from 'onnxruntime-web';

// 1. Load the Brain
const session = await ort.InferenceSession.create('./chess_model_v2.onnx');

// 2. Prepare 12x8x8 Tensor (Encoded from FEN)
const inputTensor = new ort.Tensor('float32', boardData, [1, 12, 8, 8]);

// 3. Get Evaluation
const results = await session.run({ board_state: inputTensor });
const score = results.evaluation.data[0];

console.log("Nexus-core Evaluation:", score);

🛡️ License & Ethics

This model is shared under the GPL v3 (GNU General Public License Version 3) license.

Developed by Rafsan @ GambitFlow