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---
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">
[](https://creativecommons.org/licenses/by-nc/4.0/)
-green)
[**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.
---
## 📊 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) |
---
## 🚀 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]}")
```
---
## 🛣️ 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**.
---
## 🛡️ 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.
---
**Model Authors:** [Rafsan / GambitFlow](https://huggingface.co/Rafs-an09002)
**Project Mission:** Democratizing Superhuman Chess AI through Neural Innovation. 🚀♟️ |