Update README.md

README.md

@@ -23,28 +23,33 @@ The ChessBot model is a transformer-based architecture designed for chess gamepl
 
 ```python
 import torch
-from huggingface_hub import snapshot_download
+from transformers import AutoModel
 
-# Download the model files
-model_path = snapshot_download(repo_id="Maxlegrec/ChessBot")
-
-# Add to path and import
-import sys
-sys.path.append(model_path)
-from modeling_chessbot import ChessBotModel, ChessBotConfig
-
-# Load the model
-config = ChessBotConfig()
-model = ChessBotModel.from_pretrained(model_path)
+model = AutoModel.from_pretrained("Maxlegrec/ChessBot", trust_remote_code=True)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
 
 # Example usage
 fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = model.to(device)
 
 # Get the best move
 move = model.get_move_from_fen_no_thinking(fen, T=0.1, device=device)
-print(f"Predicted move: {move}")
+print(f"Policy-based move: {move}")
+
+# Get the best move using value analysis
+value_move = model.get_best_move_value(fen, T=0, device=device)
+print(f"Value-based move: {value_move}")
+
+# Get position evaluation
+position_value = model.get_position_value(fen, device=device)
+print(f"Position value [black_win, draw, white_win]: {position_value}")
+
+# Get move probabilities
+probs = model.get_move_from_fen_no_thinking(fen, T=1, device=device, return_probs=True)
+top_moves = sorted(probs.items(), key=lambda x: x[1], reverse=True)[:5]
+print("Top 5 moves:")
+for move, prob in top_moves:
+    print(f"  {move}: {prob:.4f}")
 ```
 
 ## Requirements