Update README.md

README.md

@@ -71,25 +71,7 @@ o1 now features a hybrid neural architecture:
 You can load the model and play against o1 directly from the Hugging Face Hub, or use the Streamlit web app:
 
 ```python
-import torch
-from huggingface_hub import hf_hub_download
-from o1.agent import Agent
-
-# Download the model weights from Hugging Face Hub
-model_path = hf_hub_download(repo_id="FlameF0X/o1", filename="o1_agent.pth")
-
-# Initialize the agent and load weights
-agent = Agent()
-agent.model.load_state_dict(torch.load(model_path, map_location='cpu'))
-agent.model.eval()
-
-# Now agent is ready to use
-# Example: get policy and value for the starting position
-import chess
-board = chess.Board()
-policy_logits, value = agent.predict(board)
-print("Policy logits shape:", policy_logits.shape)
-print("Value:", value.item())
+
 ```
 
 Or launch the web UI: