chesshacks_model

NNUE (Efficiently Updatable Neural Network) chess evaluation model

Model Details

• Model type: NNUE Chess Evaluation
• Architecture: HalfKP feature representation
• Uploaded: 2025-11-15 22:49:16

Architecture

Input: HalfKP features (40,960 dimensions per perspective)
  ↓
Feature Transformer: 40,960 → 256 (separate for white/black)
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ClippedReLU activation
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Concatenate: 256 + 256 → 512
  ↓
Hidden Layer 1: 512 → 32 + ClippedReLU
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Hidden Layer 2: 32 → 32 + ClippedReLU
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Output Layer: 32 → 1 (centipawn evaluation)

Training Information

• Epoch: 45
• Training Loss: 2581668.3669
• Validation Loss: 2661316.6873

Usage

import torch
from huggingface_hub import hf_hub_download
import chess

# Download and load model
checkpoint_path = hf_hub_download(repo_id="chesshacks_model", filename="pytorch_model.bin")
checkpoint = torch.load(checkpoint_path, map_location='cpu')

# Load model config
model_config = checkpoint['model_config']

# Create model instance (you'll need the NNUEModel class)
# from model import NNUEModel
# model = NNUEModel(**model_config)
# model.load_state_dict(checkpoint['model_state_dict'])
# model.eval()

# Evaluate a position
# board = chess.Board()
# score = model.evaluate_board(board)
# print(f"Evaluation: {score:.2f} centipawns")

Training Configuration

• batch_size: 256
• learning_rate: 0.003
• num_epochs: 50
• optimizer: adam
• loss_function: mse
• hidden_size: 256
• hidden2_size: 32
• hidden3_size: 32

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Model generated with NNUE training pipeline