Spaces:
Sleeping
Sleeping
| 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. | |
| [](https://huggingface.co/GambitFlow/Nexus-Core) | |
| [](https://huggingface.co/GambitFlow/Nexus-Core) | |
| [](https://huggingface.co/GambitFlow/Nexus-Core) | |
| ## 🎯 Model Details | |
| **Nexus-Core** is a lightweight ResNet-based chess engine optimized for speed: | |
| - **Model:** [GambitFlow/Nexus-Core](https://huggingface.co/GambitFlow/Nexus-Core) | |
| - **Parameters:** 13 Million | |
| - **Architecture:** Pure CNN (ResNet with 10 blocks) | |
| - **Input:** 12-channel board representation | |
| - **Training Data:** [GambitFlow/Elite-Data](https://huggingface.co/datasets/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:** | |
| ```json | |
| { | |
| "fen": "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1", | |
| "depth": 4, | |
| "time_limit": 3000 | |
| } | |
| ``` | |
| **Response:** | |
| ```json | |
| { | |
| "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 | |
| ```python | |
| 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](https://huggingface.co/GambitFlow) / Rafsan1711 | |
| **License:** CC BY-NC 4.0 | |
| **Citation:** | |
| ```bibtex | |
| @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** 🚀♟️ |