Spaces:
Sleeping
Sleeping
File size: 4,782 Bytes
eafc489 d6ea406 eafc489 d6ea406 71b7323 d6ea406 71b7323 d6ea406 71b7323 d6ea406 71b7323 d6ea406 71b7323 d6ea406 71b7323 d6ea406 71b7323 d6ea406 71b7323 d6ea406 71b7323 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
---
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** 🚀♟️ |