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README.md

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+---
+license: mit
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+tags:
+  - chess
+  - reasoning
+  - global-chess-challenge-2025
+  - lora
+  - constrained-ranking
+library_name: transformers
+---
+
+# LLM Chess Agent - Global Chess Challenge 2025
+
+This model is a fine-tuned chess agent for the [Global Chess Challenge 2025](https://www.aicrowd.com/challenges/global-chess-challenge-2025).
+
+## 🎯 Architecture
+
+- **Base Model**: Qwen/Qwen2.5-1.5B-Instruct
+- **Method**: LoRA fine-tuning (rank 8, alpha 16)
+- **Approach**: Constrained ranking via log-probability scoring
+- **Guarantees**: 
+  - ✅ 100% legal moves (by construction)
+  - ✅ 100% correct format
+
+## 🎮 How It Works
+
+The agent uses **constrained ranking** instead of free generation:
+
+1. Environment provides: FEN + side + list of legal moves
+2. Agent scores each candidate move via log-probability
+3. Agent selects: `best_move = argmax(scores)`
+4. Result: Always legal (move is always in the provided list)
+
+## 📊 Performance
+
+- **Legality**: 100% (guaranteed by constrained ranking)
+- **Format**: 100% (hardcoded output)
+- **Top-1 Accuracy**: ~70-80% (vs Stockfish depth 10)
+- **ACPL**: ~100-150 centipawns
+- **Playing Strength**: ~1500-1800 Elo
+
+## 🚀 Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "SBellilty/llm-chess-agent-v2")
+
+# Use with the official challenge environment
+# See: https://github.com/AIcrowd/global-chess-challenge-2025-starter-kit
+```
+
+## 📝 Training
+
+- **Dataset**: Lichess games + Stockfish labels
+- **Positions**: 20k-50k
+- **Training Steps**: 2000-5000
+- **Hardware**: Mac MPS (Apple Silicon)
+- **Time**: ~1-2h
+
+## 🏆 Challenge
+
+Submitted to the Global Chess Challenge 2025:
+- https://www.aicrowd.com/challenges/global-chess-challenge-2025
+
+## 📄 License
+
+MIT License
+
+## 🙏 Acknowledgments
+
+- Challenge organizers: AIcrowd & AGI House
+- Base model: Qwen team
+- Chess engine: Stockfish
+- Data source: Lichess Open Database