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README.md

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 ---
-title: Nexus Nano Inference Api
-emoji: 🏃
-colorFrom: gray
-colorTo: indigo
+title: Nexus-Nano Inference API
+emoji: 🚀
+colorFrom: yellow
+colorTo: red
 sdk: docker
 pinned: false
+license: cc-by-nc-4.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🚀 Nexus-Nano Inference API
+
+Ultra-lightweight chess engine for instant responses.
+
+[![Model](https://img.shields.io/badge/Model-Nexus--Nano-yellow)](https://huggingface.co/GambitFlow/Nexus-Nano)
+[![Parameters](https://img.shields.io/badge/Params-2.8M-orange)](https://huggingface.co/GambitFlow/Nexus-Nano)
+[![Speed](https://img.shields.io/badge/Speed-Lightning-red)](https://huggingface.co/GambitFlow/Nexus-Nano)
+
+## 🎯 Model Details
+
+**Nexus-Nano** is the fastest model in the GambitFlow series:
+
+- **Model:** [GambitFlow/Nexus-Nano](https://huggingface.co/GambitFlow/Nexus-Nano)
+- **Parameters:** 2.8 Million
+- **Architecture:** Compact ResNet (6 blocks)
+- **Input:** 12-channel board representation
+- **Training Data:** [GambitFlow/Elite-Data](https://huggingface.co/datasets/GambitFlow/Elite-Data) (5M+ positions)
+- **Strength:** 1800-2000 ELO estimated
+
+## 🔬 Search Algorithm
+
+Ultra-minimal implementation for maximum speed:
+
+### Core Features
+- **Pure Alpha-Beta Pruning** [^1] - Classic minimax
+- **Simple MVV-LVA Ordering** [^2] - Capture prioritization
+- **No Transposition Table** - Zero memory overhead
+- **Iterative Deepening** - Anytime algorithm
+
+### Design Philosophy
+- **Single-file engine** - No modular complexity
+- **Minimal overhead** - Direct evaluation calls
+- **Speed over strength** - Optimized for response time
+
+## 📊 Performance
+
+| Metric | Value | Environment |
+|--------|-------|-------------|
+| **Depth 3 Search** | ~0.2-0.5 seconds | HF Spaces CPU |
+| **Average Nodes** | 2K-5K per move | Typical positions |
+| **Memory Usage** | ~1GB RAM | Peak inference |
+| **Response Time** | 200-500ms | 95th percentile |
+
+## 📡 API Endpoints
+
+### `POST /get-move`
+
+**Request:**
+```json
+{
+  "fen": "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1",
+  "depth": 3
+}
+```
+
+**Response:**
+```json
+{
+  "best_move": "e2e4",
+  "evaluation": 0.18,
+  "depth_searched": 3,
+  "nodes_evaluated": 2847,
+  "time_taken": 234
+}
+```
+
+### `GET /health`
+
+Health check endpoint.
+
+## 🔧 Parameters
+
+- **fen** (required): Board position in FEN notation
+- **depth** (optional): Search depth (1-5, default: 3)
+
+## 🚀 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": 3
+    }
+)
+
+data = response.json()
+print(f"Best move: {data['best_move']} (took {data['time_taken']}ms)")
+```
+
+## 💻 Use Cases
+
+Perfect for:
+- **Bullet chess (1+0, 2+1)** - Lightning-fast moves
+- **Chess tutorials** - Instant move suggestions
+- **Mobile applications** - Minimal resource usage
+- **Live analysis** - Real-time position evaluation
+- **Casual play** - Good enough for beginners/intermediate
+
+## 📚 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]: **MVV-LVA**: Hyatt, R. M., Gower, A. E., & Nelson, H. L. (1990). "Cray Blitz". *Computers, Chess, and Cognition*, 111-130.
+
+## 📖 Minimalist Design Inspiration
+
+- **MicroMax** - Mulder, H. G. (2007). "1433-byte chess program". https://home.hccnet.nl/h.g.muller/max-src2.html
+- **Sunfish** - Fiekas, N. (2013). "Simple chess engine in Python". https://github.com/thomasahle/sunfish
+- **Stockfish Lite** - Simplified versions for embedded systems
+
+## 🏆 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 Table
+
+| Feature | Nexus-Nano | Nexus-Core | Synapse-Base |
+|---------|------------|------------|--------------|
+| **Speed** | ⚡⚡⚡⚡ Lightning | ⚡⚡⚡ Ultra-fast | ⚡⚡ Fast |
+| **Strength** | 1800-2000 ELO | 2000-2200 ELO | 2400-2600 ELO |
+| **Memory** | 1GB | 2GB | 5GB |
+| **Depth** | 3-4 | 4-5 | 5-7 |
+| **Response** | 200-500ms | 500-1000ms | 1000-2000ms |
+| **Best for** | Bullet/Mobile | Online/Rapid | Tournament/Analysis |
+
+## 🎯 When to Use
+
+Choose **Nexus-Nano** if:
+- ✅ Speed is critical (bullet games, live demos)
+- ✅ Resource-constrained environment (mobile, embedded)
+- ✅ Playing against beginners/intermediate (1800-2000 ELO)
+- ✅ You need instant move suggestions
+
+Choose **Nexus-Core** if:
+- ⚡ You want balanced speed and strength
+- ⚡ Playing online rapid/blitz games
+
+Choose **Synapse-Base** if:
+- 🏆 Maximum strength is priority
+- 🏆 Tournament-level play
+- 🏆 Deep position analysis needed
+
+---
+
+**Developed by:** [GambitFlow](https://huggingface.co/GambitFlow) / Rafsan1711  
+**License:** CC BY-NC 4.0  
+**Citation:**
+
+```bibtex
+@software{gambitflow_nexus_nano_2025,
+  author = {Rafsan1711},
+  title = {Nexus-Nano: Ultra-Lightweight Chess Engine},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/GambitFlow/Nexus-Nano}
+}
+```
+
+---
+
+Part of the **GambitFlow Project** ⚡♟️