---
license: cc-by-nc-4.0
library_name: onnx
tags:
- chess
- deep-learning
- pytorch
- onnx
- resnet
- strategy
- game-ai
- gambitflow
datasets:
- Rafs-an09002/gambitflow-elite-data
language:
- en
pipeline_tag: reinforcement-learning
metrics:
- mse
model-index:
- name: GambitFlow Nexus-core (CE)
results: []
---
# ♟️ GambitFlow Nexus-core (CE)
[](https://creativecommons.org/licenses/by-nc/4.0/)
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[**Live Engine Demo**](https://chessmate-engine.onrender.com/) • [**Dataset Card**](https://huggingface.co/datasets/Rafs-an09002/gambitflow-elite-data)
## 📖 Model Overview
**Nexus-core CE** (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
| Component | Specification | Description |
| :--- | :--- | :--- |
| **Input Layer** | `(1, 12, 8, 8)` | 12-channel bitboard representation (6 for White, 6 for Black pieces). |
| **Convolutional Stem** | 128 Filters, 3x3 | Initial spatial feature extraction from the 8x8 grid. |
| **Residual Tower** | **10 Blocks** | A stack of 10 ResBlocks using Skip-Connections to prevent gradient vanishing and capture deep strategy. |
| **Value Head** | Dense / Tanh | Compresses 8192 high-level features into a single scalar evaluation. |
| **Output Range** | `[-1.0, 1.0]` | Positive favors White, negative favors Black. |
| **Params** | **~3.5 Million** | Balanced for high depth-to-speed ratio. |
## 📊 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`.
```javascript
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 **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license.
- **Attribution:** You must give credit to the GambitFlow project.
- **Non-Commercial:** This model cannot be used for commercial purposes without an explicit agreement.
- **Innovation:** We encourage researchers to fork and improve this model for non-commercial game-AI research.
---
Developed by Rafsan @ GambitFlow Labs