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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
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