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llm-chess-agent-v1 – AI Model by SBellilty | AlphaNeural AI

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SBellilty/
llm-chess-agent-v1

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global-chess-challenge-2025

constrained-ranking

Qwen/Qwen2.5-1.5B-Instruct

endpoints_compatible

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LLM Chess Agent - Global Chess Challenge 2025

This model is a fine-tuned chess agent for the 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:

Environment provides: FEN + side + list of legal moves
Agent scores each candidate move via log-probability
Agent selects: best_move = argmax(scores)
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-v1")

# 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