README.md

---
language: 
- en
tags:
- chess
- reinforcement-learning
- resnet
- transformer
- gambitflow
- synapse-edge
license: cc-by-nc-4.0
library_name: onnx
metrics:
- accuracy
- mse
pipeline_tag: zero-shot-classification
---

# ♟️ GambitFlow Synapse-Edge v1 (Flagship)

<div align="center">

![GambitFlow Banner](https://capsule-render.vercel.app/api?type=waving&color=0:2c3e50,100:000000&height=200&section=header&text=Synapse-Edge%20v1&fontSize=50&animation=fadeIn&fontAlignY=35&desc=Next-Gen%20Hybrid%20Chess%20Intelligence&descAlignY=60)

[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
![Architecture](https://img.shields.io/badge/Architecture-Hybrid%20ResNet--Transformer-orange)
![Parameters](https://img.shields.io/badge/Params-~16.5M-blue)
![Format](https://img.shields.io/badge/Format-ONNX%20(Opset%2017)-green)
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[**Dataset Hub**](https://huggingface.co/datasets/GambitFlow/Synapse-Edge-Data) •  [**Community Support**](https://huggingface.co/Rafs-an09002)

</div>

## 🌟 Model Overview

**Synapse-Edge v1** is the definitive flagship chess AI from **GambitFlow**. Representing the pinnacle of our Gen-3 research, it shatters the limitations of pure convolutional models by introducing a massive **Hybrid ResNet-Transformer architecture**. 

While previous models like *Nexus-Core* excelled at recognizing spatial patterns, Synapse-Edge v1 masters **long-range tactical dependencies** and **strategic sequencing**, making it our most "human-like" yet superhumanly sharp engine to date.

---

## 🏗️ Technical Architecture

The model utilizes a sophisticated multi-stage processing pipeline:

### 1. The Input: 119-Channel Rich Feature Map
Instead of a simple 12-channel board state, Synapse-Edge v1 processes **119 discrete information layers** per position:
- **Piece Occupancy (12):** Fundamental bitboards for all pieces.
- **Attack Influence Maps (12):** Explicit spatial "heatmaps" of which squares are under fire.
- **Auxiliary Metadata (95):** Castling rights, side to move, check status, en passant targets, and board history.

### 2. The Backbone: SE-ResNet-20
- **20 Residual Blocks** ensure deep feature extraction.
- **Squeeze-and-Excitation (SE) Attention** modules in every block allow the network to dynamically recalibrate piece importance based on the position.

### 3. The Neck: Transformer Sequence Fusion
- **4 Transformer Layers** process the board as a 64-square sequence.
- This allows the model to understand **tactical causality** (e.g., *"If I move here, the pinned knight will be attacked 3 moves later"*).

### 4. Multi-Head Prediction System
The model doesn't just evaluate; it understands the game through four specialized heads:
- **Policy Head:** Predicts the most likely master-level move from **4,672 possible UCI combinations**.
- **Value Head:** Provides a rock-solid evaluation in the range **[-1, +1]**.
- **Tactical Head:** A binary classifier that flags **"Sharpness"** (detects Forks, Pins, and Skewers instantly).
- **Phase Head:** Dynamically identifies game phases (**Opening, Middlegame, Endgame**) to adjust playing style.

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## 📊 Training Details

### Distributed 4-Worker Sharding
Synapse-Edge v1 was trained using a **High-Efficiency Distributed Pipeline**:
- **Dataset:** Over **5.5 million elite positional samples** (Elo 2000+) + **3 million tactical puzzles**.
- **Execution:** The database was split into 4 shards and trained simultaneously across 4 independent Google Colab instances.
- **Synthesis:** The final model is a **Synchronized Ensemble** where weights from all four shards were merged and averaged to create a "Master Brain" with collective knowledge.

| Specification | Value |
| :--- | :--- |
| **Total Parameters** | 16,494,757 |
| **Total Samples** | 8.5 Million |
| **Training Device** | 4x Tesla T4 GPUs (Distributed) |
| **Optimizer** | AdamW (1e-4) |
| **Precision** | Mixed (FP16/FP32) |

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## 🚀 Usage & Implementation

The model is exported in **ONNX (Opset 17)** for maximum cross-platform compatibility.

### Quick Start with Python
```python
import onnxruntime as ort
import numpy as np

# Initialize the flagship engine
session = ort.InferenceSession("synapse_edge_v1.onnx")

# Prepare your input (119 channels)
# dummy_input = np.random.randn(1, 119, 8, 8).astype(np.float32)

# Run Multi-Head Inference
policy, value, tactical, phase = session.run(None, {"input": dummy_input})

print(f"Value Score: {value[0][0]}")
print(f"Tactical Sharpness: {tactical[0][0]}")
```

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## 🛣️ Roadmap: The Path to Superhuman Strength

Synapse-Edge v1 is not a finished product, but the beginning of a **Continuous Development Pipeline**:

1.  **v1 (Current):** Master-level baseline trained on master games and puzzles.
2.  **v1.1 - v1.5:** Iterative fine-tuning on refined elite datasets.
3.  **v2 (Self-Play):** The model will play against itself for weeks, generating "Alien Strategies" to surpass human theory.
4.  **v3 (Final Flagship):** Full Reinforcement Learning (RL) integration aiming for **3500+ Elo**.

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## 🛡️ Limitations & Bias
- **Inference Latency:** Due to the Transformer layers, inference on CPU is slower than Nexus-Core (~100-300ms per position). For optimal performance, use GPU-based ONNX Runtime.
- **Endgame Accuracy:** Without tablebases, very complex endgames (e.g., KBNK) may require more search depth.

## 📜 License
This model is released under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. Commercial use without prior permission is prohibited.

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**Model Authors:** [Rafsan / GambitFlow](https://huggingface.co/Rafs-an09002)
**Project Mission:** Democratizing Superhuman Chess AI through Neural Innovation. 🚀♟️