tictactoe-gpt-1m

🤖 1M-Parameter Lightweight Tic-Tac-Toe Transformer Bot

A compact, ultra-fast GPT-style transformer model (~1.59M parameters) trained to play optimal, unbeatable Tic-Tac-Toe.

📊 Performance & Benchmarks

🏆 10,000,000 Parallel Games Benchmark (vs Minimax)

Tested on an NVIDIA GeForce RTX 4060 Laptop GPU:

• Total Games Played: 10,027,008 games
• Throughput: 17,943 games / second (~64.6 million games/hour)
• Per-Position Service Latency: 0.0124 ms
• GPU Utilization: 99.9% (Inference bound)
• Model as Player X (5,013,504 games):
  • Wins: 0 | Draws: 5,013,504 (100.00%) | Losses: 0 (0.00%)
• Model as Player O (5,013,504 games):
  • Wins: 0 | Draws: 5,013,504 (100.00%) | Losses: 0 (0.00%)

🎯 Accuracy & Rule Adherence

• Legal Move Rate: 100.00% (Never plays an illegal move)
• Validation Dataset Accuracy: >97.00%

📐 Architecture

• Parameters: 1,588,992 (~1.6 Million)
• Layers: 2 Transformer Blocks
• Hidden Dimension (Width): 256
• Attention Heads: 8
• Model Checkpoint Size: ~19.1 MB

🎮 Tokenization & Input Format

The board is represented by 11 tokens:

[turn] [cell 0] [cell 1] ... [cell 8] [dummy prediction token]

• Turn tokens: 0 (X's turn), 1 (O's turn)
• Board cells: 4 (Empty), 5 (X), 6 (O)
• Dummy prediction token: 7
• Output logits: positions 8..16 mapping to cells 0..8

🚀 How to Load and Play in Python

import torch
import numpy as np
from model import ModelConfig, build_model
from tokenization import encode_board, EMPTY

# Download 'best.pt' from this repo
checkpoint = torch.load("best.pt", map_location="cpu", weights_only=False)
config = ModelConfig(**checkpoint["model_config"])
model = build_model(config)
model.load_state_dict(checkpoint["model_state"])
model.eval()

# Board state: 0 for empty, 1 for X, -1 for O
board = (0,) * 9  # Empty board
player = 1        # X to move

# Tokenize and predict move
inputs = torch.from_numpy(encode_board(board, player).astype(np.int64)).unsqueeze(0)
with torch.no_grad():
    logits = model(inputs)[0]
    legal = inputs[0, 1:10] == EMPTY
    move = int(logits.masked_fill(~legal, -torch.inf).argmax().item())

print(f"Model chose cell index: {move}")