♟️ GambitFlow Synapse-Edge v1 (Flagship)
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🌟 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
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:
- v1 (Current): Master-level baseline trained on master games and puzzles.
- v1.1 - v1.5: Iterative fine-tuning on refined elite datasets.
- v2 (Self-Play): The model will play against itself for weeks, generating "Alien Strategies" to surpass human theory.
- 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 Project Mission: Democratizing Superhuman Chess AI through Neural Innovation. 🚀♟️