How to Use the Advanced Magnus Chess Model from Hugging Face

Quick Start Guide

Once your model is uploaded to Hugging Face, here's how others can use it:

1. Installation

pip install huggingface_hub torch chess numpy pyyaml scikit-learn

2. Download and Use the Model

from huggingface_hub import hf_hub_download
import chess
import sys
import os

# Download model files (replace YOUR_USERNAME with your actual username)
repo_id = "YOUR_USERNAME/advanced-magnus-chess-model"

# Download required files
model_path = hf_hub_download(repo_id=repo_id, filename="model.pth")
predictor_path = hf_hub_download(repo_id=repo_id, filename="advanced_magnus_predictor.py")
config_path = hf_hub_download(repo_id=repo_id, filename="config.yaml")

# Add the download directory to Python path
download_dir = os.path.dirname(model_path)
sys.path.append(download_dir)

# Import and use the predictor
from advanced_magnus_predictor import AdvancedMagnusPredictor

# Initialize the predictor
predictor = AdvancedMagnusPredictor()

# Analyze a chess position
board = chess.Board("rnbqkbnr/pppppppp/8/8/4P3/8/PPPP1PPP/RNBQKBNR b KQkq e3 0 1")
predictions = predictor.predict_moves(board, top_k=5)

print("Magnus-style move predictions:")
for i, pred in enumerate(predictions, 1):
    move = pred['move']
    confidence = pred['confidence']
    san = board.san(chess.Move.from_uci(move))
    print(f"{i}. {san} ({move}) - {confidence:.3f} confidence")

3. Example Output

Magnus-style move predictions:
1. c5 (c7c5) - 0.145 confidence
2. e5 (e7e5) - 0.123 confidence
3. Nf6 (g8f6) - 0.098 confidence
4. d6 (d7d6) - 0.087 confidence
5. e6 (e7e6) - 0.075 confidence

Advanced Usage

Batch Analysis

positions = [
    "rnbqkbnr/pppppppp/8/8/4P3/8/PPPP1PPP/RNBQKBNR b KQkq e3 0 1",
    "rnbqkbnr/pp1ppppp/8/2p5/4P3/8/PPPP1PPP/RNBQKBNR w KQkq c6 0 2",
    "rnbqkbnr/ppp1pppp/8/3p4/2PP4/8/PP2PPPP/RNBQKBNR b KQkq c3 0 2"
]

for i, fen in enumerate(positions):
    print(f"\nPosition {i+1}: {fen}")
    board = chess.Board(fen)
    predictions = predictor.predict_moves(board, top_k=3)

    for pred in predictions:
        san = board.san(chess.Move.from_uci(pred['move']))
        print(f"  {san}: {pred['confidence']:.3f}")

Integration with Chess Engines

import chess.engine

# Combine Magnus predictions with Stockfish analysis
stockfish = chess.engine.SimpleEngine.popen_uci("/path/to/stockfish")

board = chess.Board("your_position_fen")

# Get Magnus-style predictions
magnus_predictions = predictor.predict_moves(board, top_k=5)

# Get engine analysis
engine_result = stockfish.play(board, chess.engine.Limit(time=1.0))
engine_move = engine_result.move.uci()

print("Magnus predictions vs Engine:")
for pred in magnus_predictions:
    move = pred['move']
    san = board.san(chess.Move.from_uci(move))
    marker = " ⭐" if move == engine_move else ""
    print(f"  {san}: {pred['confidence']:.3f}{marker}")

stockfish.quit()

Model Features

Style Emulation: Predicts moves in Magnus Carlsen's characteristic style
High Accuracy: 6.65% exact match, 14.17% top-5 accuracy
Fast Inference: ~50ms per position
Comprehensive: Handles all chess positions and game phases
Educational: Perfect for learning Magnus's strategic concepts

Use Cases

Chess Training: Learn Magnus's move preferences
Game Analysis: Understand Magnus-style thinking
AI Development: Building chess applications
Research: Studying player-specific chess styles
Educational Tools: Teaching advanced chess concepts

Technical Notes

Model requires position feature extraction
Works best with properly formatted FEN strings
Optimized for modern hardware (GPU/MPS supported)
Compatible with standard chess libraries

Support

For issues or questions about using the model, please check the model repository on Hugging Face or create an issue in the original project repository.

Citation

@misc{advanced_magnus_chess_model_2025,
  title={Advanced Magnus Carlsen Chess Model},
  author={Chess AI Research Team},
  year={2025},
  url={https://huggingface.co/YOUR_USERNAME/advanced-magnus-chess-model}
}