| --- |
| license: gpl-3.0 |
| library_name: onnx |
| tags: |
| - chess |
| - deep-learning |
| - pytorch |
| - onnx |
| - resnet |
| - strategy |
| - game-ai |
| - gambitflow |
| - Chess engine |
| datasets: |
| - GambitFlow/Elite-Data |
| language: |
| - en |
| pipeline_tag: reinforcement-learning |
| metrics: |
| - mse |
| model-index: |
| - name: GambitFlow Nexus-core |
| results: [] |
| --- |
| |
| # ♟️ GambitFlow Nexus-core |
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| <div align="center"> |
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| [](https://www.gnu.org/licenses/gpl-3.0) |
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| -green) |
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| [**Live Engine Demo**](https://gambitflow.onrender.com) • [**Dataset Card**](https://huggingface.co/datasets/Gambitflow/elite-data) |
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| </div> |
|
|
| ## 📖 Model Overview |
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| **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. |
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| It serves as the static evaluation function (the "Brain") for the GambitFlow search algorithm, providing a numeric advantage score for any given board state. |
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|
| ## 🧠 Technical Architecture |
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| 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. |
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| ### 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. | |
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| ## 📊 Training Methodology |
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| Nexus-core was trained using a professional-grade pipeline designed for robustness and strategic depth. |
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| ### 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. |
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| ### 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. |
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| ### 3. ONNX Optimization |
| - Exported with **Opset 17**. |
| - **Constant Folding** and Graph Optimization enabled to reduce latency during real-time play. |
|
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| ## 💻 Implementation (JavaScript) |
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| Designed for seamless integration with `onnxruntime-web`. |
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|
| ```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 |
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| This model is shared under the **GPL v3 (GNU General Public License Version 3)** license. |
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| --- |
| <div align="center"> |
| <p>Developed by <a href="https://huggingface.co/Rafs-an09002">Rafsan</a> @ GambitFlow</p> |
| </div> |
