Upload chess-gpt-eval-contrastive/main.py with huggingface_hub

chess-gpt-eval-contrastive/main.py

@@ -731,8 +731,8 @@ recording_file = "logs/determine.csv" # default recording file. Because we are u
 player_ones = ["50M/anneal/anneal_complete_round3.pt"]
 player_two_recording_name = "lc0_sweep" #"stockfish_sweep"
 move_num_in_gamestate = False
-book_opening = False
-random_opening = True
+book_opening = True
+random_opening = False
 random_opening_moves = 10
 
 activations_path="activations_rdm.pkl"
@@ -749,9 +749,9 @@ if __name__ == "__main__":
         i = 0
         rm = 10
 #        for rm in range(5, 36, 5):
-#        for i in [0]: # [3] #range(11):
-        for wgt in [0.005, 0.01, 0.025, 0.05]:
-            num_games = 250
+        for i in [0]: # [3] #range(11):
+#        for wgt in [0.005, 0.01, 0.025, 0.05]:
+            num_games = 5000
             # player_one = GPTPlayer(model="gpt-3.5-turbo-instruct")
             # player_one = LocalLlamaPlayer(model_name="meta-llama/Llama-2-7b-hf")
             # player_one = LocalLoraLlamaPlayer("meta-llama/Llama-2-7b-hf", "/workspace/axolotl/lora2-out")
@@ -762,7 +762,7 @@ if __name__ == "__main__":
 #            player_one = NanoGptPlayer(model_name=player_one_recording_name, move_num_in_gamestate=move_num_in_gamestate)
             #player_one_recording_name = f"xformer_rdm_{rm}"
             player_one = MambaPlayer(model_name=player_one_recording_name, move_num_in_gamestate=move_num_in_gamestate, update_contrastive=update_activations, update_linear=update_linear, linear_probe_path=linear_path)
-            player_one_recording_name = f'contrastive_weights_rdm/mamba_rdm_wgt_{wgt}' #f'contrastive_rdm/mamba_rdm_{rm}'
+            player_one_recording_name = f'linear_train'
             if apply_activations:
                 player_one.apply_contrastive_activations(path=activations_path, weight=wgt)