README.md

metadata

title: Nexus-Core Inference API
emoji: ⚡
colorFrom: green
colorTo: blue
sdk: docker
pinned: false
license: cc-by-nc-4.0

⚡ Nexus-Core Inference API

Fast and efficient chess move prediction powered by Nexus-Core v2.0.

🎯 Model Details

Nexus-Core is a lightweight ResNet-based chess engine optimized for speed:

• Model: GambitFlow/Nexus-Core
• Parameters: 13 Million
• Architecture: Pure CNN (ResNet with 10 blocks)
• Input: 12-channel board representation
• Training Data: GambitFlow/Elite-Data (5M+ positions)
• Strength: 2000-2200 ELO estimated

🔬 Search Algorithm

Efficient alpha-beta implementation with essential optimizations:

Core Features

• Alpha-Beta Pruning [^1] - Classic minimax with cutoffs
• Iterative Deepening [^2] - Progressive depth increase
• Quiescence Search [^3] - Tactical sequence resolution
• Simple Transposition Table - 100K position cache

Move Ordering

• MVV-LVA [^4] - Most Valuable Victim - Least Valuable Attacker
• Promotion prioritization
• Check detection

📊 Performance

Metric	Value	Environment
Depth 4 Search	~0.5-1 second	HF Spaces CPU
Average Nodes	5K-15K per move	Typical positions
Memory Usage	~2GB RAM	Peak inference
Response Time	500-1000ms	95th percentile

📡 API Endpoints

POST /get-move

Request:

{
  "fen": "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1",
  "depth": 4,
  "time_limit": 3000
}

Response:

{
  "best_move": "e2e4",
  "evaluation": 0.21,
  "depth_searched": 4,
  "nodes_evaluated": 8432,
  "time_taken": 856
}

GET /health

Health check endpoint.

🔧 Parameters

• fen (required): Board position in FEN notation
• depth (optional): Search depth (1-6, default: 4)
• time_limit (optional): Max time in milliseconds (1000-10000, default: 3000)

🚀 Quick Start

import requests

response = requests.post(
    "https://YOUR-SPACE.hf.space/get-move",
    json={
        "fen": "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1",
        "depth": 4
    }
)

data = response.json()
print(f"Best move: {data['best_move']}")
print(f"Evaluation: {data['evaluation']}")

💻 Use Cases

Perfect for:

• Online chess platforms - Fast response times
• Training applications - Consistent ~2000 ELO play
• Mobile apps - Lightweight inference
• Rapid/Blitz games - Quick move generation

📚 Research References

[^1]: Alpha-Beta Pruning: Knuth, D. E., & Moore, R. W. (1975). "An analysis of alpha-beta pruning". Artificial Intelligence, 6(4), 293-326.

[^2]: Iterative Deepening: Korf, R. E. (1985). "Depth-first iterative-deepening: An optimal admissible tree search". Artificial Intelligence, 27(1), 97-109.

[^3]: Quiescence Search: Shannon, C. E. (1950). "Programming a computer for playing chess". Philosophical Magazine, 41(314), 256-275.

[^4]: MVV-LVA: Hyatt, R. M., Gower, A. E., & Nelson, H. L. (1990). "Cray Blitz". Computers, Chess, and Cognition, 111-130.

📖 Related Work

• Stockfish: https://stockfishchess.org/ - Open-source chess engine
• Fruit: Letouzey, F. (2005). "Fruit 2.1 source code". http://www.fruitchess.com/
• Crafty: Hyatt, R. M. (1996-2023). "Crafty Chess Program". https://www.craftychess.com/

🏆 Model Lineage

GambitFlow AI Engine Series:

1. Nexus-Nano (2.8M) - Ultra-fast baseline
2. Nexus-Core (13M) - Balanced performance ✨
3. Synapse-Base (38.1M) - State-of-the-art

⚖️ Comparison

Feature	Nexus-Core	Synapse-Base
Speed	⚡⚡⚡ Ultra-fast	⚡⚡ Fast
Strength	2000-2200 ELO	2400-2600 ELO
Tactics	Basic	Advanced
Search Depth	4-5	5-7
Use Case	Online/Mobile	Tournament

---

Developed by: GambitFlow / Rafsan1711
License: CC BY-NC 4.0
Citation:

@software{gambitflow_nexus_core_2025,
  author = {Rafsan1711},
  title = {Nexus-Core: Lightweight ResNet Chess Engine},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/GambitFlow/Nexus-Core}
}

---

Part of the GambitFlow Project 🚀♟️