LLM accuracy test for coding in a specific field, but each model are only tested with up to 4 model sizes.

Prompt (all models):

implement a whole chess engine in python with python-chess

Outputs (reasoning discarded) for DeepSeek:

DeepSeek-R1-Distill-Qwen-1.5B

To implement a chess engine using Python and the Python chess library, follow these steps:

Install Dependencies

pip install python-chess chess.js @chess.js/cbor@latest

Set Up a Chess Server
```
FC2:1.5.8
```
- This starts a web server at http://localhost:8080 for real-time play.
- It allows transport over HTTP or other protocols if needed.
Create a Playbook Configuration
```
python chess.py
```

Healthy chess engine

- Replace with your desired board configuration.
- Adjust parameters for minimax and evaluation.

4. **Set Up the Chess Engine**
```python
import chess.js
from chessjs import Player

engine = ChessJS()
engine.open('playbook')
engine.initialize('board1')
engine.initialize('initial')

Replace 'board1' and 'initial' with your desired board configuration.

Implement the Minimax Algorithm

import chess.js as cs
from chess.js.converter import CardBelief  # for debugging

class MinimaxAlgorithm:
    def __init__(self):
        self.target_p = position.possible_moves().set Chap()
        self.fport = (self.m_min() for self.max())
        selfibble = CardBelief(position=kcsplayingboard().get()())
        self.optimizer = MinimaxOptim amateurfectant()
        self evaluator = MinimaxEvaluateur(n_iter=self.optimizer.pfc minimax)
        self.optimizer(optimizer = self grit)
        self윽()

    def get_best_move(self):
        moves = self.fport不动的()
        if not moves:
            return None
        if self evaluator Viet(XV stressing ?
            best_with = self evaluator Beginning()
            self.optimizerNAL ————
            return None
        ```

Define the Minimax Algorithm

class MinimaxAlgorithm:
    def __init__(self, position):
        self.position = position
        self.fport = (minimax() for minimax())
        self.best paramount()

    def __iter__(self):
        return (dictionary, minimax(), minimax())

    def get_best_move(self):
        moves = self.fport Sofa()
        if not moves:
            return returns None if optimizé?
        else:
            self.optimizerNAL ————
            return None
        ```

Optimize the Engine
- Use a muzzle factor (1-10) to speed up the search.
- Implement the muzzle function (á simpl批发法).

Evaluate Positions

best_with = self.maxim() ————
move = self.optimizerNAL ————
evaluating = self.evaluationsvaluation()

Implement Parsing

def evaluate(self):
    self.ord()
    self.ftrext()
    self.evaluationsvaluation()

Create a Chess Problems

p = Player(position=kcsplayedboard().get())
engine = ChessJS()
# Read a problem:
engine.initialize('problem.json')

Test the Engine

engine.get_best_move()
p.move()
# Solve problem for White
if p Optimization is successful:
    engine.optimize
    solve() to find the best move

Integrate with Chess.js
```
# Chess.js Player
p = cs.View player()
```

This implementation provides a foundation for a chess engine using Python. Be aware of the starting server, potential slow performance, and various optimizations needed for better competitive play.

Remember to adjust parameters and test different configurations for optimal performance and accuracy."

DeepSeek-R1-Distill-Qwen-7B

I'll help you create a basic chess engine using Python and the Python-Chess library. Below is a step-by-step implementation. You'll need to install the Python-Chess package first.

First, let's install the dependency:

pip install python-chess

Here's a basic implementation of a chess engine:

import chess
from chess import ChessGame, Square, ChessPiece
import tkinter as tk
from tkinter import ttk, messagebox
import threading

class ChessEngine:
    def __init__(self):
        self.board = chess.Board()
        self. Moves = []
        self.history = []
        self Discounts = []

    def display_board(self):
        # Function to display the chess board
        # (This is a simplified version)
        fen = self.board.fen()
        root = tk.Tk()
        frame = tk.Frame(root, bg="lightyellow")
        frame.pack()

        label = ttk.Label(frame, text=fen, font=('Arial', 20))
        label.pack()

        root.mainloop()

    def get LegalMoves(self, from_square):
        # Returns a list of legal moves from the given square
        # (This is a simplified version)
        from_square = chess.parse_square(from_square)
        return self.board.legal_moves

    def make_move(self, move):
        # Applies a move to the board
        # (This is a simplified version)
        self.board.push(move)
        self. Moves.append(move)
        self.history.append(str(self.board.fen()))
        self.Updates()

    def getpieceat(self, from_square):
        # Returns the piece at the given square
        # (This is a simplified version)
        from_square = chess.parse_square(from_square)
        return self.board.p opaque

    def render_board(self):
        # This function will actually render the chess board
        pass  # To be implemented

    def set_test_set(self, positions):
        # This function will validate the move against the test set
        test_set = chess.plyrics.get_testset()
        move = test_set.move(positions)
        return move

def main():
    engine = ChessEngine()
    window = tk.Tk()
    window.title("Chess Engine")
    window.geometry("800x600")

    # Create a canvas or frame to display the chessboard
    canvas = tk.Canvas(window, width=400, height=400, bg='lightyellow')
    canvas.pack()

    def display_board():
        canvas.delete("all")
        for i in range(8):
            for j in range(8):
                piece = chess.Board()..piece_at(i, j)
                if piece != 0:
                    x = (j + 2) * 50
                    y = (i + 2) * 50
                    canvas.create_oval(x - 25, y - 25, x + 25, y + 25, fill=chessChess piec.symbol())
                    canvas.create_text(x, y, text=chess.friendly_notation(piecesign()))

    def input_move(e):
        current_square = e.x // 50
        rank = e.y // 50
        from_square = chess.parse_square(f"{current_square + 1}{8 - rank}")
        new_square = input.get()
        new_square = chess.parse_square(new_square)
        move = chess.move(new_square, from_square)
        print(f"Move {move}")
        engine.make_move(move)
        input.delete(0, tk.END)

    def count_project_discounts(positions):
        # notify user
        discounts = chess.plyrics.get_project_discounts()
        print(discounts)
        messagebox.showinfo("Project Discounts", str(discounts))

    window.bind("<Button-1>", input_move)
    window.mainloop()

if __name__ == "__main__":
    main()

To use this engine, you can run it with the following command:

python -m chess_engine.chess_engine --fen "8p1r1b2n2kb1r1p1w1q3/2pp3r3q/3p2r3q/6q/3k3/3b4/2PP3R3/PP1PPPPP1/wwwwwwww bwwwwwww b1 www1 www1 www1 www1 www1 www1 www1 www1 www1www1www1 wwwwwwwwww www1 www1"

This will start the chess engine with the specified chess position.

Explanation:

Chess Board Display: The display_board function shows the current state of the chess board using Tkinter. Each piece is placed in its correct position on the board.
Move Input: Users can click on squares to select starting positions for pieces and input moves, which are then applied to the board.
Legal Moves: The get_legal_moves function provides a list of legal moves for a selected square.
Make Move: The make_move function applies the selected move to the board and updates the history.
Project Discounts: The count_project_discounts function retrieves and displays the current valid project discounts.
Main Function: The main function serves as the entry point, creating the main window and handling user interactions.

Enhancements:

This implementation is a basic version and lacks many chess features (e.g., check detection, castling, en passant, etc.).
The board display is simplified and does not provide accurate 3D visuals.
Error handling and move validation are not fully implemented.

Usage:

To use the chess engine, you need to:

Install the required Python package: pip install python-chess
Run the main function with an initial chess position
Click on the board to select moves and see the results

You can expand this engine by adding more features such as:

Board resizing
3D board visualization
Move history visualization
Game rules enforcement
Move validation
Undo/redo functionality
Position history
Move display
Clock timer
Position evaluation

DeepSeek-R1-Distill-Llama-70B

Here's a basic implementation of a chess engine using Python and the python-chess library. This engine will be able to make moves based on a simple evaluation function and the minimax algorithm:

import chess
import random

# Initialize the chess board
board = chess.Board()

def evaluate_position(board):
    """
    Simple evaluation function based on material advantage.
    """
    piece_values = {
        chess.PAWN: 100,
        chess.KNIGHT: 300,
        chess.BISHOP: 300,
        chess.ROOK: 500,
        chess.QUEEN: 900,
        chess.KING: 10000
    }

    total = 0
    for square in board.piece_map().values():
        if square is None:
            continue
        piece_type = square.piece_type
        color = square.color
        value = piece_values[piece_type]
        if color == chess.WHITE:
            total += value
        else:
            total -= value
    return total

def minimax(board, depth, alpha, beta, maximizing_player):
    """
    Minimax algorithm with alpha-beta pruning to find the best move.
    """
    if depth == 0 or board.is_game_over():
        return evaluate_position(board)

    if maximizing_player:
        max_eval = -float('inf')
        for move in board.legal_moves:
            new_board = board.copy()
            new_board.push(move)
            eval = minimax(new_board, depth-1, alpha, beta, False)
            max_eval = max(max_eval, eval)
            alpha = max(alpha, eval)
            if beta <= alpha:
                break
        return max_eval
    else:
        min_eval = float('inf')
        for move in board.legal_moves:
            new_board = board.copy()
            new_board.push(move)
            eval = minimax(new_board, depth-1, alpha, beta, True)
            min_eval = min(min_eval, eval)
            beta = min(beta, eval)
            if beta <= alpha:
                break
        return min_eval

def get_engine_move(board):
    """
    Get the move chosen by the engine using the minimax algorithm.
    """
    best_move = None
    best_eval = -float('inf')

    for move in board.legal_moves:
        new_board = board.copy()
        new_board.push(move)
        eval = minimax(new_board, 3, -float('inf'), float('inf'), False)

        if eval > best_eval:
            best_eval = eval
            best_move = move

    return best_move

def main():
    global board
    while True:
        # Player's turn (white)
        print("Current position:")
        print(board)
        print("\nLegal moves:")
        for move in board.legal_moves:
            print(move)

        # Get player's move
        player_move = input("\nEnter your move (e.g., e2e4): ")
        try:
            move = board.parse_uci(player_move)
            if move in board.legal_moves:
                board.push(move)
            else:
                print("Invalid move!")
                continue
        except ValueError:
            print("Invalid move format!")
            continue

        # Engine's turn (black)
        if board.turn == chess.BLACK:
            engine_move = get_engine_move(board)
            if engine_move is not None:
                print(f"Engine move: {engine_move}")
                board.push(engine_move)
            else:
                print("No legal moves for engine!")

        # Check game over
        if board.is_game_over():
            print("Game over!")
            print(board.result())
            break

if __name__ == "__main__":
    main()

This implementation includes:

Game Setup: The chess board is initialized using python-chess.
Evaluation Function: A simple evaluation function that calculates the material advantage for each side.
Minimax Algorithm: A basic implementation of the minimax algorithm with alpha-beta pruning to find the best move.
Engine Move Selection: The engine selects its move based on the minimax algorithm.
Main Loop: A simple text-based interface where the user can input moves, and the engine responds with its own moves.

Key Features:

The engine evaluates positions based on material advantage.
The engine uses the minimax algorithm with a depth of 3 to look ahead.
The game includes input validation for player moves.
The game continues until checkmate, stalemate, or resignation.

Limitations:

The evaluation function is very basic and doesn't consider positional factors.
The engine plays at a fixed depth (3) which limits its strength.
There's no opening or endgame database.
The engine doesn't handle time controls or incremental analysis.

How to Use:

Install the python-chess library if you haven't already:
```
pip install python-chess
```
Run the script:
```
python chess_engine.py
```
Follow the prompts to make your moves in standard chess notation (e.g., e2e4, Nf3, etc.).
The engine will respond with its moves automatically.

You can improve this basic engine by:

Implementing a more sophisticated evaluation function.
Increasing the search depth.
Adding opening and endgame databases.
Implementing iterative deepening.
Adding time controls for move selection.

latest in the website (685B)

I'll create a comprehensive chess engine using python-chess. This will include move generation, evaluation, search algorithms, and UCI interface support.

import chess
import chess.engine
import time
import math
from typing import List, Optional, Tuple

class SimpleChessEngine:
    def __init__(self):
        self.board = chess.Board()
        self.transposition_table = {}
        self.killer_moves = [[None, None] for _ in range(100)]  # Depth-based killer moves
        self.history_heuristic = [[[0 for _ in range(64)] for _ in range(64)] for _ in range(2)]  # [color][from_sq][to_sq]
        
        # Piece values for evaluation
        self.piece_values = {
            chess.PAWN: 100,
            chess.KNIGHT: 320,
            chess.BISHOP: 330,
            chess.ROOK: 500,
            chess.QUEEN: 900,
            chess.KING: 20000
        }
        
        # Piece-square tables (simplified)
        self.pawn_table = [
            0,  0,  0,  0,  0,  0,  0,  0,
            50, 50, 50, 50, 50, 50, 50, 50,
            10, 10, 20, 30, 30, 20, 10, 10,
            5,  5, 10, 25, 25, 10,  5,  5,
            0,  0,  0, 20, 20,  0,  0,  0,
            5, -5,-10,  0,  0,-10, -5,  5,
            5, 10, 10,-20,-20, 10, 10,  5,
            0,  0,  0,  0,  0,  0,  0,  0
        ]
        
        self.knight_table = [
            -50,-40,-30,-30,-30,-30,-40,-50,
            -40,-20,  0,  0,  0,  0,-20,-40,
            -30,  0, 10, 15, 15, 10,  0,-30,
            -30,  5, 15, 20, 20, 15,  5,-30,
            -30,  0, 15, 20, 20, 15,  0,-30,
            -30,  5, 10, 15, 15, 10,  5,-30,
            -40,-20,  0,  5,  5,  0,-20,-40,
            -50,-40,-30,-30,-30,-30,-40,-50
        ]

    def reset_board(self):
        """Reset the board to starting position"""
        self.board = chess.Board()

    def make_move(self, move: chess.Move) -> bool:
        """Make a move on the board"""
        if move in self.board.legal_moves:
            self.board.push(move)
            return True
        return False

    def unmake_move(self):
        """Unmake the last move"""
        if self.board.move_stack:
            self.board.pop()
            return True
        return False

    def evaluate_board(self) -> float:
        """
        Evaluate the current board position
        Returns a score from white's perspective
        """
        if self.board.is_checkmate():
            return -99999 if self.board.turn else 99999
        
        if self.board.is_stalemate() or self.board.is_insufficient_material() or self.board.can_claim_draw():
            return 0

        score = 0
        
        # Material evaluation
        for square in chess.SQUARES:
            piece = self.board.piece_at(square)
            if piece:
                value = self.piece_values[piece.piece_type]
                if piece.color == chess.WHITE:
                    score += value
                else:
                    score -= value
                
                # Piece-square table evaluation
                if piece.piece_type == chess.PAWN:
                    table_value = self.pawn_table[square if piece.color == chess.WHITE else 63 - square]
                    score += table_value if piece.color == chess.WHITE else -table_value
                elif piece.piece_type == chess.KNIGHT:
                    table_value = self.knight_table[square if piece.color == chess.WHITE else 63 - square]
                    score += table_value if piece.color == chess.WHITE else -table_value

        # Mobility evaluation
        mobility = len(list(self.board.legal_moves))
        if self.board.turn == chess.WHITE:
            score += mobility * 0.5
        else:
            score -= mobility * 0.5

        return score

    def order_moves(self, moves: List[chess.Move], depth: int) -> List[chess.Move]:
        """Order moves for better alpha-beta pruning"""
        scored_moves = []
        
        for move in moves:
            score = 0
            
            # MVV-LVA (Most Valuable Victim - Least Valuable Attacker)
            if self.board.is_capture(move):
                if self.board.is_en_passant(move):
                    score = 100  # Pawn capture
                else:
                    victim = self.board.piece_at(move.to_square)
                    attacker = self.board.piece_at(move.from_square)
                    if victim and attacker:
                        score = 10 * self.piece_values[victim.piece_type] - self.piece_values[attacker.piece_type]
            
            # Promotion
            if move.promotion:
                score += self.piece_values[move.promotion]
            
            # Killer moves
            if move == self.killer_moves[depth][0]:
                score += 900
            elif move == self.killer_moves[depth][1]:
                score += 800
            
            # History heuristic
            color_idx = 0 if self.board.turn == chess.WHITE else 1
            history_score = self.history_heuristic[color_idx][move.from_square][move.to_square]
            score += history_score // 100
            
            scored_moves.append((score, move))
        
        # Sort by score in descending order
        scored_moves.sort(reverse=True)
        return [move for _, move in scored_moves]

    def quiescence_search(self, alpha: float, beta: float, depth: int) -> float:
        """Quiescence search to avoid horizon effect"""
        stand_pat = self.evaluate_board()
        
        if stand_pat >= beta:
            return beta
        if alpha < stand_pat:
            alpha = stand_pat
        
        # Only consider captures
        captures = [move for move in self.board.legal_moves if self.board.is_capture(move)]
        captures = self.order_moves(captures, depth)
        
        for move in captures:
            self.board.push(move)
            score = -self.quiescence_search(-beta, -alpha, depth + 1)
            self.board.pop()
            
            if score >= beta:
                return beta
            if score > alpha:
                alpha = score
        
        return alpha

    def alpha_beta(self, depth: int, alpha: float, beta: float, max_depth: int) -> float:
        """Alpha-beta pruning search algorithm"""
        alpha_original = alpha
        
        # Check transposition table
        tt_key = self.board._transposition_key()
        if tt_key in self.transposition_table:
            tt_entry = self.transposition_table[tt_key]
            if tt_entry['depth'] >= max_depth - depth:
                if tt_entry['flag'] == 'exact':
                    return tt_entry['value']
                elif tt_entry['flag'] == 'lower':
                    alpha = max(alpha, tt_entry['value'])
                elif tt_entry['flag'] == 'upper':
                    beta = min(beta, tt_entry['value'])
                
                if alpha >= beta:
                    return tt_entry['value']

        if depth == 0:
            return self.quiescence_search(alpha, beta, depth)

        if self.board.is_checkmate():
            return -99999 + (max_depth - depth)  # Prefer quicker checkmates
        if self.board.is_stalemate() or self.board.is_insufficient_material() or self.board.can_claim_draw():
            return 0

        best_score = -math.inf
        moves = list(self.board.legal_moves)
        moves = self.order_moves(moves, depth)
        
        if not moves:
            return 0

        best_move = None
        
        for move in moves:
            self.board.push(move)
            score = -self.alpha_beta(depth - 1, -beta, -alpha, max_depth)
            self.board.pop()
            
            if score > best_score:
                best_score = score
                best_move = move
            
            alpha = max(alpha, best_score)
            if alpha >= beta:
                # Store killer move
                if not self.board.is_capture(move):
                    if move != self.killer_moves[depth][0]:
                        self.killer_moves[depth][1] = self.killer_moves[depth][0]
                        self.killer_moves[depth][0] = move
                
                # Update history heuristic
                color_idx = 0 if self.board.turn == chess.WHITE else 1
                self.history_heuristic[color_idx][move.from_square][move.to_square] += depth * depth
                break

        # Store in transposition table
        tt_entry = {
            'value': best_score,
            'depth': max_depth - depth,
            'flag': 'exact'
        }
        
        if best_score <= alpha_original:
            tt_entry['flag'] = 'upper'
        elif best_score >= beta:
            tt_entry['flag'] = 'lower'
        
        self.transposition_table[tt_key] = tt_entry
        
        return best_score

    def find_best_move(self, depth: int = 4, time_limit: float = None) -> chess.Move:
        """Find the best move using alpha-beta search"""
        start_time = time.time()
        best_move = None
        best_score = -math.inf
        
        moves = list(self.board.legal_moves)
        if not moves:
            return None
        
        # If only one move, return it immediately
        if len(moves) == 1:
            return moves[0]
        
        moves = self.order_moves(moves, 0)
        
        for move in moves:
            self.board.push(move)
            
            # Time management
            if time_limit and time.time() - start_time > time_limit / 2:
                self.board.pop()
                break
            
            score = -self.alpha_beta(depth - 1, -math.inf, math.inf, depth)
            self.board.pop()
            
            if score > best_score:
                best_score = score
                best_move = move
            
            print(f"Move: {move}, Score: {score}")
        
        print(f"Best move: {best_move}, Score: {best_score}")
        return best_move

    def iterative_deepening(self, max_depth: int = 6, time_limit: float = 5.0) -> chess.Move:
        """Perform iterative deepening search with time management"""
        start_time = time.time()
        best_move = None
        
        for depth in range(1, max_depth + 1):
            if time.time() - start_time > time_limit:
                break
            
            print(f"Searching depth {depth}...")
            current_best = self.find_best_move(depth, time_limit - (time.time() - start_time))
            
            if current_best:
                best_move = current_best
            
            # If we found a checkmate, no need to search deeper
            if abs(self.evaluate_board()) > 90000:
                break
        
        return best_move

    def print_board(self):
        """Print the current board state"""
        print(self.board)

    def get_game_result(self) -> Optional[str]:
        """Get the current game result"""
        if self.board.is_checkmate():
            return "Checkmate - " + ("Black" if self.board.turn else "White") + " wins"
        elif self.board.is_stalemate():
            return "Stalemate"
        elif self.board.is_insufficient_material():
            return "Draw - insufficient material"
        elif self.board.can_claim_draw():
            return "Draw"
        return None

class UCIIntegration:
    """Basic UCI protocol integration"""
    
    def __init__(self, engine: SimpleChessEngine):
        self.engine = engine
        self.is_running = False
    
    def run_uci(self):
        """Run the UCI protocol loop"""
        self.is_running = True
        
        while self.is_running:
            try:
                command = input().strip()
                self.handle_uci_command(command)
            except EOFError:
                break
            except KeyboardInterrupt:
                break
    
    def handle_uci_command(self, command: str):
        """Handle UCI commands"""
        parts = command.split()
        
        if command == "uci":
            self.respond("id name SimplePythonEngine")
            self.respond("id author Chess AI")
            self.respond("uciok")
        
        elif command == "isready":
            self.respond("readyok")
        
        elif command == "ucinewgame":
            self.engine.reset_board()
        
        elif command.startswith("position"):
            self.handle_position(parts)
        
        elif command.startswith("go"):
            self.handle_go(parts)
        
        elif command == "quit":
            self.is_running = False
        
        elif command == "stop":
            # Implement stop functionality if needed
            pass
    
    def handle_position(self, parts: List[str]):
        """Handle position command"""
        if "startpos" in parts:
            self.engine.reset_board()
            moves_index = parts.index("moves") if "moves" in parts else -1
            if moves_index != -1:
                for move_str in parts[moves_index + 1:]:
                    move = chess.Move.from_uci(move_str)
                    self.engine.make_move(move)
        
        elif "fen" in parts:
            fen_index = parts.index("fen")
            moves_index = parts.index("moves") if "moves" in parts else len(parts)
            fen = " ".join(parts[fen_index + 1:moves_index])
            self.engine.board = chess.Board(fen)
            
            if moves_index < len(parts):
                for move_str in parts[moves_index + 1:]:
                    move = chess.Move.from_uci(move_str)
                    self.engine.make_move(move)
    
    def handle_go(self, parts: List[str]):
        """Handle go command"""
        depth = 4
        movetime = None
        
        if "depth" in parts:
            depth_index = parts.index("depth")
            depth = int(parts[depth_index + 1])
        
        if "movetime" in parts:
            movetime_index = parts.index("movetime")
            movetime = int(parts[movetime_index + 1]) / 1000  # Convert to seconds
        
        best_move = self.engine.iterative_deepening(depth, movetime) if movetime else self.engine.find_best_move(depth)
        
        if best_move:
            self.respond(f"bestmove {best_move.uci()}")
        else:
            self.respond("bestmove 0000")  # Null move
    
    def respond(self, message: str):
        """Send a response to the UCI GUI"""
        print(message, flush=True)

# Example usage and testing
def play_example_game():
    """Play an example game between two engine instances"""
    engine1 = SimpleChessEngine()
    engine2 = SimpleChessEngine()
    
    moves_played = 0
    max_moves = 50
    
    print("Starting example game...")
    
    while not engine1.board.is_game_over() and moves_played < max_moves:
        engine1.print_board()
        print(f"\nMove {moves_played + 1}")
        
        if engine1.board.turn == chess.WHITE:
            print("White's turn (Engine 1)")
            move = engine1.find_best_move(depth=3)
        else:
            print("Black's turn (Engine 2)")  
            move = engine1.find_best_move(depth=3)
        
        if move:
            engine1.make_move(move)
            engine2.make_move(move)
            print(f"Played: {move}")
            moves_played += 1
        else:
            print("No legal moves!")
            break
    
    engine1.print_board()
    result = engine1.get_game_result()
    print(f"\nGame result: {result}")

if __name__ == "__main__":
    # Example of using the engine
    engine = SimpleChessEngine()
    
    # Test the engine
    print("Initial board:")
    engine.print_board()
    
    # Make some moves
    test_moves = [
        "e2e4", "e7e5", "g1f3", "b8c6", "f1c4", "g8f6"
    ]
    
    for move_uci in test_moves:
        move = chess.Move.from_uci(move_uci)
        if engine.make_move(move):
            print(f"\nAfter {move_uci}:")
            engine.print_board()
    
    # Find best move
    print("\nThinking...")
    best_move = engine.find_best_move(depth=3)
    print(f"Suggested move: {best_move}")
    
    # Uncomment to run UCI interface
    # uci = UCIIntegration(engine)
    # uci.run_uci()
    
    # Uncomment to play example game
    # play_example_game()

This chess engine includes:

Key Features:

Board Representation: Uses python-chess for robust chess board representation and move generation
Evaluation Function:
- Material counting
- Piece-square tables
- Mobility evaluation
- Checkmate detection
Search Algorithms:
- Alpha-beta pruning
- Iterative deepening
- Quiescence search
- Transposition table
- Move ordering with:
  - MVV-LVA (Most Valuable Victim - Least Valuable Attacker)
  - Killer moves
  - History heuristic
UCI Protocol Support: Can interface with chess GUIs
Advanced Features:
- Time management
- Draw detection
- Efficient move ordering

Usage Examples:

# Basic usage
engine = SimpleChessEngine()
engine.make_move(chess.Move.from_uci("e2e4"))
best_move = engine.find_best_move(depth=4)

# UCI interface
uci = UCIIntegration(engine)
uci.run_uci()  # Run this to use with chess GUIs

The engine is modular and can be easily extended with more sophisticated evaluation functions, better piece-square tables, or additional search enhancements like null-move pruning or more advanced time management.

Outputs for Qwen3-Coder:

Qwen3-Coder-30B-A3B-Instruct

Chess Engine Implementation with Python-Chess

Here's a complete chess engine implementation using the python-chess library. This engine includes move generation, evaluation, minimax with alpha-beta pruning, and a simple AI player.

import chess
import chess.engine
import random
import time

class ChessEngine:
    def __init__(self):
        self.board = chess.Board()
        self.engine = None

    def get_board(self):
        return self.board

    def is_game_over(self):
        return self.board.is_game_over()

    def get_legal_moves(self):
        return list(self.board.legal_moves)

    def make_move(self, move):
        """Make a move on the board"""
        if move in self.get_legal_moves():
            self.board.push(move)
            return True
        return False

    def undo_move(self):
        """Undo the last move"""
        if self.board.move_stack:
            self.board.pop()

    def evaluate_position(self):
        """Simple position evaluation function"""
        if self.board.is_game_over():
            if self.board.is_checkmate():
                # If it's checkmate, the side that just moved lost
                return -10000 if self.board.turn == chess.WHITE else 10000
            else:
                # Draw
                return 0

        # Material evaluation
        piece_values = {
            chess.PAWN: 100,
            chess.KNIGHT: 320,
            chess.BISHOP: 330,
            chess.ROOK: 500,
            chess.QUEEN: 900,
            chess.KING: 20000
        }

        score = 0
        for square in chess.SQUARES:
            piece = self.board.piece_at(square)
            if piece:
                value = piece_values[piece.piece_type]
                if piece.color == chess.WHITE:
                    score += value
                else:
                    score -= value

        # Mobility bonus
        mobility = len(list(self.board.legal_moves))
        score += mobility * 2 if self.board.turn == chess.WHITE else -mobility * 2

        return score

    def minimax(self, depth, alpha, beta, maximizing_player):
        """Minimax algorithm with alpha-beta pruning"""
        if depth == 0 or self.board.is_game_over():
            return self.evaluate_position()

        if maximizing_player:
            max_eval = float('-inf')
            for move in self.board.legal_moves:
                self.board.push(move)
                eval_score = self.minimax(depth - 1, alpha, beta, False)
                self.board.pop()
                max_eval = max(max_eval, eval_score)
                alpha = max(alpha, eval_score)
                if beta <= alpha:
                    break  # Alpha-beta pruning
            return max_eval
        else:
            min_eval = float('inf')
            for move in self.board.legal_moves:
                self.board.push(move)
                eval_score = self.minimax(depth - 1, alpha, beta, True)
                self.board.pop()
                min_eval = min(min_eval, eval_score)
                beta = min(beta, eval_score)
                if beta <= alpha:
                    break  # Alpha-beta pruning
            return min_eval

    def get_best_move(self, depth=3):
        """Get the best move using minimax with alpha-beta pruning"""
        best_move = None
        best_value = float('-inf') if self.board.turn == chess.WHITE else float('inf')

        for move in self.board.legal_moves:
            self.board.push(move)
            if self.board.turn == chess.WHITE:
                value = self.minimax(depth - 1, float('-inf'), float('inf'), False)
                if value > best_value:
                    best_value = value
                    best_move = move
            else:
                value = self.minimax(depth - 1, float('-inf'), float('inf'), True)
                if value < best_value:
                    best_value = value
                    best_move = move
            self.board.pop()

        return best_move

    def play_move(self, move):
        """Play a move and return the result"""
        if move in self.get_legal_moves():
            self.board.push(move)
            return True
        return False

    def reset_game(self):
        """Reset the game to initial position"""
        self.board = chess.Board()

    def print_board(self):
        """Print the current board"""
        print(self.board)
        print()

def main():
    engine = ChessEngine()

    print("Welcome to the Chess Engine!")
    print("You are playing as White. Enter moves in algebraic notation (e.g., e2e4)")
    print("Type 'quit' to exit")
    print()

    # Print initial board
    engine.print_board()

    while not engine.is_game_over():
        if engine.board.turn == chess.WHITE:
            # Human player's turn
            print("Your turn (White)")
            move_str = input("Enter your move: ").strip()

            if move_str.lower() == 'quit':
                break

            try:
                move = chess.Move.from_uci(move_str)
                if move in engine.get_legal_moves():
                    engine.make_move(move)
                    print(f"You played: {move}")
                else:
                    print("Invalid move! Try again.")
                    continue
            except:
                print("Invalid move format! Use algebraic notation (e.g., e2e4)")
                continue
        else:
            # Engine's turn
            print("Engine's turn (Black)")
            start_time = time.time()
            best_move = engine.get_best_move(depth=3)
            end_time = time.time()

            if best_move:
                engine.make_move(best_move)
                print(f"Engine played: {best_move}")
                print(f"Engine evaluation: {engine.evaluate_position()}")
                print(f"Engine took {end_time - start_time:.2f} seconds")
            else:
                print("No valid moves for engine")
                break

        engine.print_board()

    # Game over
    if engine.board.is_checkmate():
        winner = "Black" if engine.board.turn == chess.WHITE else "White"
        print(f"Checkmate! {winner} wins!")
    elif engine.board.is_stalemate():
        print("Stalemate! It's a draw.")
    elif engine.board.is_insufficient_material():
        print("Draw by insufficient material.")
    elif engine.board.is_seventyfive_moves():
        print("Draw by 75-move rule.")
    elif engine.board.is_fivefold_repetition():
        print("Draw by fivefold repetition.")
    else:
        print("Game ended in draw.")

if __name__ == "__main__":
    main()

Features of this Chess Engine:

Complete Chess Implementation:
- Full chess rules including castling, en passant, and pawn promotion
- Check and checkmate detection
- Draw conditions (stalemate, insufficient material, 75-move rule, etc.)
AI Engine:
- Minimax algorithm with alpha-beta pruning for efficient search
- Position evaluation function considering material and mobility
- Adjustable search depth
User Interface:
- Interactive command-line interface
- Move input in algebraic notation (e.g., e2e4)
- Visual board display after each move
Game Management:
- Move history tracking
- Game state management
- Automatic game ending detection

How to Use:

Run the program
You play as White (human player)
Enter moves in algebraic notation (e.g., "e2e4" for moving a piece from e2 to e4)
The engine will automatically play as Black
Type "quit" to exit the game

Requirements:

To run this program, you'll need to install the python-chess library:

pip install python-chess

The engine uses a simple evaluation function that considers:

Material value of pieces
Mobility (number of legal moves)
Checkmate and draw conditions

The AI searches to a depth of 3 moves ahead with alpha-beta pruning for efficiency. You can adjust the search depth by changing the depth parameter in get_best_move().

Note: This implementation is for educational purposes. For a production chess engine, more sophisticated evaluation functions and search algorithms would be needed.

Qwen3-Coder-480B-A35B-Instruct-FP8

Here's a complete chess engine implementation using python-chess with a minimax algorithm and alpha-beta pruning:

import chess
import chess.svg
import random
from typing import Dict, Any

class ChessEngine:
    def __init__(self, depth: int = 3):
        self.depth = depth
        self.piece_values = {
            chess.PAWN: 100,
            chess.KNIGHT: 320,
            chess.BISHOP: 330,
            chess.ROOK: 500,
            chess.QUEEN: 900,
            chess.KING: 20000
        }
        self.position_values = {
            chess.PAWN: [
                0,  0,  0,  0,  0,  0,  0,  0,
                50, 50, 50, 50, 50, 50, 50, 50,
                10, 10, 20, 30, 30, 20, 10, 10,
                5,  5, 10, 25, 25, 10,  5,  5,
                0,  0,  0, 20, 20,  0,  0,  0,
                5, -5,-10,  0,  0,-10, -5,  5,
                5, 10, 10,-20,-20, 10, 10,  5,
                0,  0,  0,  0,  0,  0,  0,  0
            ],
            chess.KNIGHT: [
                -50,-40,-30,-30,-30,-30,-40,-50,
                -40,-20,  0,  0,  0,  0,-20,-40,
                -30,  0, 10, 15, 15, 10,  0,-30,
                -30,  5, 15, 20, 20, 15,  5,-30,
                -30,  0, 15, 20, 20, 15,  0,-30,
                -30,  5, 10, 15, 15, 10,  5,-30,
                -40,-20,  0,  5,  5,  0,-20,-40,
                -50,-40,-30,-30,-30,-30,-40,-50
            ],
            chess.BISHOP: [
                -20,-10,-10,-10,-10,-10,-10,-20,
                -10,  0,  0,  0,  0,  0,  0,-10,
                -10,  0,  5, 10, 10,  5,  0,-10,
                -10,  5,  5, 10, 10,  5,  5,-10,
                -10,  0, 10, 10, 10, 10,  0,-10,
                -10, 10, 10, 10, 10, 10, 10,-10,
                -10,  5,  0,  0,  0,  0,  5,-10,
                -20,-10,-10,-10,-10,-10,-10,-20
            ],
            chess.ROOK: [
                0,  0,  0,  0,  0,  0,  0,  0,
                5, 10, 10, 10, 10, 10, 10,  5,
                -5,  0,  0,  0,  0,  0,  0, -5,
                -5,  0,  0,  0,  0,  0,  0, -5,
                -5,  0,  0,  0,  0,  0,  0, -5,
                -5,  0,  0,  0,  0,  0,  0, -5,
                -5,  0,  0,  0,  0,  0,  0, -5,
                0,  0,  0,  5,  5,  0,  0,  0
            ],
            chess.QUEEN: [
                -20,-10,-10, -5, -5,-10,-10,-20,
                -10,  0,  0,  0,  0,  0,  0,-10,
                -10,  0,  5,  5,  5,  5,  0,-10,
                -5,  0,  5,  5,  5,  5,  0, -5,
                0,  0,  5,  5,  5,  5,  0, -5,
                -10,  5,  5,  5,  5,  5,  0,-10,
                -10,  0,  5,  0,  0,  0,  0,-10,
                -20,-10,-10, -5, -5,-10,-10,-20
            ],
            chess.KING: [
                -30,-40,-40,-50,-50,-40,-40,-30,
                -30,-40,-40,-50,-50,-40,-40,-30,
                -30,-40,-40,-50,-50,-40,-40,-30,
                -30,-40,-40,-50,-50,-40,-40,-30,
                -20,-30,-30,-40,-40,-30,-30,-20,
                -10,-20,-20,-20,-20,-20,-20,-10,
                20, 20,  0,  0,  0,  0, 20, 20,
                20, 30, 10,  0,  0, 10, 30, 20
            ]
        }

    def evaluate_board(self, board: chess.Board) -> float:
        """Evaluate the current board position"""
        if board.is_checkmate():
            return -99999 if board.turn else 99999
        if board.is_stalemate() or board.is_insufficient_material():
            return 0

        score = 0
        for square in chess.SQUARES:
            piece = board.piece_at(square)
            if piece:
                value = self.piece_values[piece.piece_type]
                # Adjust for position (use mirrored values for black)
                if piece.color == chess.WHITE:
                    score += value + self.position_values[piece.piece_type][square]
                else:
                    score -= value + self.position_values[piece.piece_type][chess.square_mirror(square)]
        return score if board.turn else -score

    def minimax(self, board: chess.Board, depth: int, alpha: float, beta: float, maximizing: bool) -> float:
        """Minimax algorithm with alpha-beta pruning"""
        if depth == 0 or board.is_game_over():
            return self.evaluate_board(board)

        if maximizing:
            max_eval = float('-inf')
            for move in board.legal_moves:
                board.push(move)
                eval_score = self.minimax(board, depth - 1, alpha, beta, False)
                board.pop()
                max_eval = max(max_eval, eval_score)
                alpha = max(alpha, eval_score)
                if beta <= alpha:
                    break
            return max_eval
        else:
            min_eval = float('inf')
            for move in board.legal_moves:
                board.push(move)
                eval_score = self.minimax(board, depth - 1, alpha, beta, True)
                board.pop()
                min_eval = min(min_eval, eval_score)
                beta = min(beta, eval_score)
                if beta <= alpha:
                    break
            return

(extra end-of-code added due to cutoff of HF)

Qwen3-Coder-480B-A35B-Instruct

Here's a complete chess engine implementation using python-chess with a simple minimax algorithm and alpha-beta pruning:

import chess
import chess.engine
import random
from typing import Optional

class SimpleChessEngine:
    def __init__(self, depth: int = 3):
        self.depth = depth
        self.piece_values = {
            chess.PAWN: 100,
            chess.KNIGHT: 320,
            chess.BISHOP: 330,
            chess.ROOK: 500,
            chess.QUEEN: 900,
            chess.KING: 20000
        }

    def evaluate_board(self, board: chess.Board) -> float:
        """Evaluate the board position using material counting."""
        if board.is_checkmate():
            return -100000 if board.turn else 100000

        score = 0
        for square in chess.SQUARES:
            piece = board.piece_at(square)
            if piece:
                value = self.piece_values[piece.piece_type]
                if piece.color == chess.WHITE:
                    score += value
                else:
                    score -= value
        return score

    def minimax(self, board: chess.Board, depth: int, alpha: float, beta: float,
                maximizing: bool) -> float:
        """Minimax algorithm with alpha-beta pruning."""
        if depth == 0 or board.is_game_over():
            return self.evaluate_board(board)

        if maximizing:
            max_eval = float('-inf')
            for move in board.legal_moves:
                board.push(move)
                eval_score = self.minimax(board, depth - 1, alpha, beta, False)
                board.pop()
                max_eval = max(max_eval, eval_score)
                alpha = max(alpha, eval_score)
                if beta <= alpha:
                    break
            return max_eval
        else:
            min_eval = float('inf')
            for move in board.legal_moves:
                board.push(move)
                eval_score = self.minimax(board, depth - 1, alpha, beta, True)
                board.pop()
                min_eval = min(min_eval, eval_score)
                beta = min(beta, eval_score)
                if beta <= alpha:
                    break
            return min_eval

    def get_best_move(self, board: chess.Board) -> Optional[chess.Move]:
        """Find the best move using minimax."""
        best_move = None
        best_value = float('-inf') if board.turn else float('inf')

        for move in board.legal_moves:
            board.push(move)
            board_value = self.minimax(
                board,
                self.depth - 1,
                float('-inf'),
                float('inf'),
                not board.turn
            )
            board.pop()

            if board.turn:
                if board_value > best_value:
                    best_value = board_value
                    best_move = move
            else:
                if board_value < best_value:
                    best_value = board_value
                    best_move = move

        return best_move

def play_game():
    """Play a game between the engine and a random player."""
    board = chess.Board()
    engine = SimpleChessEngine(depth=3)

    print("Starting new game!")
    print(board)

    while not board.is_game_over():
        if board.turn == chess.WHITE:
            print("\nEngine's turn (White)")
            move = engine.get_best_move(board)
        else:
            print("\nRandom player's turn (Black)")
            move = random.choice(list(board.legal_moves))

        print(f"Move: {move}")
        board.push(move)
        print(board)

        if board.is_checkmate():
            print("Checkmate!")
        elif board.is_stalemate():
            print("Stalemate!")
        elif board.is_insufficient_material():
            print("Draw by insufficient material!")

    print(f"Game over! Result: {board.result()}")

if __name__ == "__main__":
    play_game()

Features:

Board Evaluation: Simple material counting with standard piece values
Minimax Algorithm: With alpha-beta pruning for efficiency
Configurable Depth: Adjustable search depth (default 3 moves)
Game Demo: Play against a random move generator
Game End Detection: Handles checkmate, stalemate, and draws

How to Use:

Run the script to see a demo game between the engine (white) and a random player (black)

To use the engine in your own code:

engine = SimpleChessEngine(depth=4)  # Adjust depth as needed
best_move = engine.get_best_move(board)  # Pass a chess.Board object

Requirements:

pip install chess

Key Components:

Evaluation Function: Counts material with standard values (pawn=100, knight=320, etc.)
Minimax Search: Explores possible moves up to specified depth
Alpha-Beta Pruning: Reduces search space for better performance
Move Selection: Chooses the move with the best evaluation score

Limitations:

No opening book (plays from scratch)
No endgame tablebases
Simple evaluation (no positional factors)
Fixed search depth (no quiescence search)

Possible Improvements:

Add positional evaluation (piece-square tables)
Implement iterative deepening
Add transposition tables
Include quiescence search
Add opening/endgame specific knowledge

This implementation provides a solid foundation for a chess engine that can be extended with more sophisticated features.

Outputs for Qwen2.5-Coder:

Qwen2.5-Coder-0.5B-Instruct-GGUF

Creating a complete chess engine in Python involves several steps, including defining the game rules, implementing the search algorithm, and handling the game logic. Below is a simple implementation of a chess engine using the python-chess library. This example will provide a basic framework, including a basic chess board representation, a search algorithm, and a simple evaluation function.

First, ensure you have the python-chess library installed. You can install it using pip:
pip install python-chess
Here's a basic implementation of a chess engine:
import chess
import time

class ChessEngine:
    def __init__(self):
        self.board = chess.Board()
        self.white = chess.WHITE
        self.black = chess.BLACK
        self.active_player = self.white
        self.search_depth = 5

    def get_valid_moves(self, color):
        valid_moves = self.board.legal_moves(color)
        return valid_moves

    def is_valid_move(self, move):
        start = move.start
        end = move.end
        return self.board.is_legal((start, end))

    def make_move(self, move):
        self.board.push(move)
        self.active_player = self.other_player()
        return self.board

    def other_player(self):
        if self.active_player == self.white:
            return self.black
        else:
            return self.white

    def evaluate(self):
        # Simple evaluation function using a heuristic
        return self.board.enpassant_score + 2 * self.board.king_square_value + self.board.knight_square_value

    def get_best_move(self):
        return self.board.currently_best_move()

if __name__ == "__main__":
    engine = ChessEngine()

    while True:
        print("Welcome to the chess engine!")
        print("Current board:", engine.board)
        print("Your turn, white.")

        # Get valid moves for white
        valid_moves_white = engine.get_valid_moves(engine.white)
        if not valid_moves_white:
            print("No valid moves for white, you lose.")
            break

        # Get the best move for white
        best_move_white = engine.get_best_move()
        print(f"Best move for white: {best_move_white} (score: {engine.evaluate()})")

        # Make the move
        engine.make_move(best_move_white)

        # Wait for a turn
        time.sleep(1)
Explanation:

Initialization: The ChessEngine class initializes a new chess board and sets up the player as white.

Valid Moves: The get_valid_moves method returns all the valid moves for a given color.

Making a Move: The make_move method updates the board and switches to the opponent's color.

Other Player: The other_player method returns the other player's color.

Evaluation: The evaluate method calculates the score of the current state of the board using a simple heuristic. A higher score means the game is better.

Best Move: The get_best_move method returns the best move from the current state of the board.

Main Loop: The main loop allows the user to play against the opponent, making moves, and evaluating the board.

This is a very basic implementation and can be expanded with additional features such as AI opponents, time management, and a graphical user interface. For a more advanced chess engine, you would need to implement a more sophisticated search algorithm, a more complex evaluation function, and possibly a more sophisticated game logic.

Qwen2.5-Coder-1.5B-Instruct-GGUF

Implementing a whole chess engine in Python using the python-chess library involves several steps. Below is a step-by-step guide to help you get started:

Install the python-chess library: You can install the python-chess library using pip:
```
pip install python-chess
```

Set up the chess engine: You need to create a chess engine class that will handle the game logic. Here is an example of a simple chess engine class using the python-chess library:

import chess
import random

class SimpleChessEngine:
    def __init__(self):
        self.board = chess.Board()

    def make_move(self, move):
        try:
            self.board.push(move)
            return True
        except ValueError:
            return False

    def get_board(self):
        return self.board

    def get_current_player(self):
        return self.board.turn

    def is_game_over(self):
        return self.board.is_game_over()

    def get_winner(self):
        if self.is_game_over():
            return chess.unicode(self.board.result())
        return None

    def get_legal_moves(self):
        return list(self.board.legal_moves)

    def get_random_move(self):
        if self.is_game_over():
            return None
        return random.choice(list(self.board.legal_moves))

Implement the game loop: You need to create a game loop that allows two players to play the game. Here is an example of a simple game loop:

def play_game():
    engine = SimpleChessEngine()
    player1 = 'white'
    player2 = 'black'

    while not engine.is_game_over():
        if engine.get_current_player() == player1:
            print("Player 1's turn")
            move = input("Enter your move (e.g., e4): ")
            if engine.make_move(chess.parse_san(move)):
                print("Move made")
            else:
                print("Invalid move")
        else:
            print("Player 2's turn")
            move = input("Enter your move (e.g., e5): ")
            if engine.make_move(chess.parse_san(move)):
                print("Move made")
            else:
                print("Invalid move")

        print(engine.get_board())

    print("Game over")
    print("Result:", engine.get_winner())

if __name__ == "__main__":
    play_game()

Enhance the game: You can enhance the game by adding features such as:
- Move validation
- Player AI
- Time management
- Multiplayer support
- Database for game history

Here is a more advanced example that includes AI:

import chess
import random
import time

class SimpleChessEngine:
    def __init__(self):
        self.board = chess.Board()

    def make_move(self, move):
        try:
            self.board.push(move)
            return True
        except ValueError:
            return False

    def get_board(self):
        return self.board

    def get_current_player(self):
        return self.board.turn

    def is_game_over(self):
        return self.board.is_game_over()

    def get_winner(self):
        if self.is_game_over():
            return chess.unicode(self.board.result())
        return None

    def get_legal_moves(self):
        return list(self.board.legal_moves)

    def get_random_move(self):
        if self.is_game_over():
            return None
        return random.choice(list(self.board.legal_moves))

class AI:
    def __init__(self):
        self.engine = SimpleChessEngine()
        self.engine.make_move(chess.Move.from_uci('e4'))  # Start with e4

    def get_move(self, board):
        if board.is_game_over():
            return None
        return self.engine.get_random_move()

def play_game():
    engine = SimpleChessEngine()
    ai = AI()

    while not engine.is_game_over():
        if engine.get_current_player() == 'white':
            move = input("Enter your move (e.g., e4): ")
            if engine.make_move(chess.parse_san(move)):
                print("Move made")
            else:
                print("Invalid move")
        else:
            move = ai.get_move(engine.get_board())
            if move:
                engine.make_move(move)
                print("Move made")
            else:
                print("AI cannot make a move")

        print(engine.get_board())

    print("Game over")
    print("Result:", engine.get_winner())

if __name__ == "__main__":
    play_game()

This is a basic implementation of a chess engine in Python using the python-chess library. You can enhance this code to create a more sophisticated chess engine with additional features.

Qwen2.5-Coder-3B

Creating a complete chess engine in Python with python-chess is a complex task, but I can guide you through the steps to get started. python-chess is a powerful library that simplifies the process of working with chess positions and moves.

Here's a step-by-step guide to creating a basic chess engine:
Install python-chess:

First, you need to install the python-chess library. You can do this using pip:
pip install python-chess
Understand the basic concepts of chess:

Before writing any code, it's important to understand the basic rules and concepts of chess:

The board is a 64-square grid.

Players take turns moving their pieces.

Pieces can move to different squares according to their specific rules.

Check, mate, stalemate, and draw are all possible game states.
Set up the chess board:

You can use python-chess to create and manipulate chess positions. Here's a basic example:
import chess

# Create a new chess board
board = chess.Board()

# Print the initial board position
print(board)
Move pieces:

You can make moves on the board using the push() method:
# Make a move
board.push(chess.Move.from_uci('e4'))

# Print the updated board position
print(board)
Check the game state:

You can check various game states using methods like is_game_over(), is_stalemate(), is_checkmate(), etc.
# Check if the game is over
if board.is_game_over():
    result = 'Checkmate' if board.is_checkmate() else 'Stalemate'
    print(result)
else:
    print('Game is ongoing')
Implement the main loop:

A simple main loop to simulate a game of chess would look like this:
while not board.is_game_over():
    # Print the current board position
    print(board)

    # Get user input for the next move
    user_input = input("Enter your move: ")
    move = chess.Move.from_uci(user_input)

    # Check if the move is valid
    if move in board.legal_moves:
        # Make the move
        board.push(move)
    else:
        print("Invalid move. Try again.")

# Print the final game state
print(board)
Expand the engine:

To create a more sophisticated chess engine, you can:

Implement search algorithms (e.g., minimax, alpha-beta pruning) to evaluate positions.

Consider additional features like piece promotion, castling, en passant, and pawn promotion.

Optimize performance by using more efficient data structures and algorithms.

Test and refine:

Test your engine with different positions and scenarios to ensure it works correctly. Refine the engine based on the results and feedback.
Here's a simple example of a basic chess engine using the minimax algorithm:
import chess
from chess.engine import SimpleEngine
import time

# Load a pre-trained chess engine
engine = SimpleEngine.popen_uci("stockfish")

def evaluate_position(board):
    # Simple evaluation function (can be improved)
    score = 0
    for piece in board.pieces():
        if piece.color == chess.WHITE:
            score += 1
        else:
            score -= 1
    return score

def minimax(board, depth, maximizing_player):
    if depth == 0 or board.is_game_over():
        return evaluate_position(board), None

    if maximizing_player:
        max_score = float('-inf')
        best_move = None
        for move in board.legal_moves:
            board.push(move)
            score, _ = minimax(board, depth - 1, False)
            board.pop()
            if score > max_score:
                max_score = score
                best_move = move
        return max_score, best_move
    else:
        min_score = float('inf')
        best_move = None
        for move in board.legal_moves:
            board.push(move)
            score, _ = minimax(board, depth - 1, True)
            board.pop()
            if score < min_score:
                min_score = score
                best_move = move
        return min_score, best_move

def play_game():
    board = chess.Board()
    while not board.is_game_over():
        if board.turn == chess.WHITE:
            # Use the engine to make a move
            start = time.time()
            result, move = engine.play(board, chess.engine.Limit(time=1))
            print("Engine move:", move)
            board.push(move)
        else:
            # Get user input for the next move
            user_input = input("Enter your move: ")
            move = chess.Move.from_uci(user_input)

            # Check if the move is valid
            if move in board.legal_moves:
                # Make the move
                board.push(move)
            else:
                print("Invalid move. Try again.")

        # Print the current board position
        print(board)

    # Print the final game state
    print(board)
    print("Game over")

if __name__ == "__main__":
    play_game()
This example uses the Stockfish engine as a reference, but you can replace it with any other engine if you prefer. The minimax function is a basic evaluation and decision-making algorithm that can be expanded to include more sophisticated strategies.

Remember, creating a full-fledged chess engine is a complex task that requires a good understanding of chess rules and algorithms. This example is a starting point, and you can build upon it to create a more advanced engine.

Qwen2.5-Coder-7B-Instruct-GGUF (Q2_K)

Implementing a whole chess engine is a complex task, but I can guide you through creating a simple one using the python-chess library. The python-chess library is a powerful tool for handling chess board operations and is ideal for developing chess-related applications.

First, you need to install the python-chess library if you haven't already:
pip install python-chess
Here’s a step-by-step guide to building a basic chess engine:

Step 1: Import the Library

Start by importing the necessary modules from python-chess.
import chess
import chess.engine
Step 2: Define the Engine

We will create a simple chess engine using the chess.engine.SimpleEngine class. This engine will use the stockfish command-line engine, which is a strong opensource chess engine.

First, you need to have stockfish installed. You can download it from the official Stockfish website.

Then, you can define the engine:
engine = chess.engine.SimpleEngine.popen_uci("/path/to/stockfish")
Replace "/path/to/stockfish" with the actual path to your stockfish executable.

Step 3: Create a Chess Board

Next, we will create a chess.Board object to represent the current state of the chess board.
board = chess.Board()
Step 4: Make a Move

To make a move, you need to get a chess.engine.Limit object and pass it to the engine.play method. This object specifies the time limits for the move.
def make_move(board, time_limit=0.1):
    result = engine.play(board, chess.engine.Limit(time=time_limit))
    if result is None:
        raise ValueError("No valid move found")
    return result.move
Step 5: Play a Game

Now, let's create a function to play a game. This function will repeatedly make moves until the game is over.
def play_game():
    board = chess.Board()
    while not board.is_game_over():
        move = make_move(board)
        board.push(move)
        print(board)
    print(f"Result: {board.result()}")
Step 6: Run the Engine

Finally, you can call the play_game function to start a new game.
if __name__ == "__main__":
    play_game()
Full Code

Here is the complete code for the simple chess engine:
import chess
import chess.engine

# Replace "/path/to/stockfish" with the actual path to your stockfish executable
engine = chess.engine.SimpleEngine.popen_uci("/path/to/stockfish")

def make_move(board, time_limit=0.1):
    result = engine.play(board, chess.engine.Limit(time=time_limit))
    if result is None:
        raise ValueError("No valid move found")
    return result.move

def play_game():
    board = chess.Board()
    while not board.is_game_over():
        move = make_move(board)
        board.push(move)
        print(board)
    print(f"Result: {board.result()}")

if __name__ == "__main__":
    play_game()
This is a very basic chess engine. It uses stockfish to get the best move and plays until the game is over. You can expand this by adding more sophisticated evaluation, opening book, endgame handling, and more.

Next Steps

Opening Book: You can add opening book functionality to improve the engine's performance.

Evaluation Function: Implement a custom evaluation function to guide the engine in making better decisions.

Alpha-Beta Pruning: Implement alpha-beta pruning to speed up the search process.

Aspiration Windows: Use aspiration windows to handle large value differences in the search.

Transposition Table: Use a transposition table to avoid redundant calculations.

These steps will help you build a more advanced chess engine.

Conclusion: (rating point is from 0-1000) table sorted by appearance time

Model	Model size	Rating point	Rating reason
DeepSeek-R1	1.5B	-500	invalid syntax on `pip` call and chess.js is a NodeJS module
DeepSeek-R1	7B	1	not working by `AttributeError` and invalid syntax and a bit of good basics
DeepSeek-R1	70B	750	pretty basic engine but works
DeepSeek-R1	685B	900	PSQT not completed - but code is fine!
Qwen3-Coder	30B	850	basic evaluation and minimax (that makes code complicated later)
Qwen3-Coder	480B (FP8)	950	full PSQT and evaluation is fine, but the request was cut by HF due to length limit
Qwen3-Coder	480B	850	basic evaluation and minimax (that makes code complicated later)
Qwen2.5-Coder	0.5B	-500	not even giving a way for human to play the move and no logic (edit: also incorrect)
Qwen2.5-Coder	1.5B	500	random moves and easily beatable by humans, but at least it pass the input
Qwen2.5-Coder	3B	400	using SF directly is cheating and the search function is unused, at least it has good logic
Qwen2.5-Coder	7B	125	cheating with SF and no search logic

winapiadmin/llm_test.md

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Explanation:

Enhancements:

Usage:

Key Features:

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Usage Examples:

Chess Engine Implementation with Python-Chess

Features of this Chess Engine:

How to Use:

Requirements:

Features:

How to Use:

Requirements:

Key Components:

Limitations:

Possible Improvements:

Explanation:

Step 1: Import the Library

Step 2: Define the Engine

Step 3: Create a Chess Board

Step 4: Make a Move

Step 5: Play a Game

Step 6: Run the Engine

Full Code

Next Steps