Instructions to use sigmoidneuron123/NeoChess with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sigmoidneuron123/NeoChess with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir NeoChess sigmoidneuron123/NeoChess
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Update selfchess.py
Browse files- selfchess.py +3 -1
selfchess.py
CHANGED
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@@ -5,6 +5,7 @@ import chess
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import os
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import chess.engine as eng
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import torch.multiprocessing as mp
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# CONFIGURATION
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CONFIG = {
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@@ -16,6 +17,7 @@ CONFIG = {
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"num_games": 3000,
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"stockfish_time_limit": 1.0,
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"search_depth": 1,
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}
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device = CONFIG["device"]
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@@ -147,7 +149,7 @@ def game_gen(engine_side):
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evaling = {}
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for move in board.legal_moves:
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board.push(move)
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evaling[move] = -search(board, depth=CONFIG["search_depth"], alpha=float('-inf'), beta=float('inf'))
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board.pop()
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if not evaling:
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import os
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import chess.engine as eng
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import torch.multiprocessing as mp
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import random
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# CONFIGURATION
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CONFIG = {
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"num_games": 3000,
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"stockfish_time_limit": 1.0,
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"search_depth": 1,
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"epsilon": 0.2
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}
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device = CONFIG["device"]
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evaling = {}
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for move in board.legal_moves:
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board.push(move)
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evaling[move] = -search(board, depth=CONFIG["search_depth"], alpha=float('-inf'), beta=float('inf')) * random.uniform(1-CONFIG["epsilon"],1+CONFIG["epsilon"])
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board.pop()
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if not evaling:
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