remove anthropic

Author: Ubuntu

test_scripts/images/resize.py

@@ -1,7 +1,7 @@
 from PIL import Image
 
 # Path to the image
-image_path = '/Users/colemiller/cortex.t/test_scripts/images/john2.png'
+image_path = '/example_path/'
 
 # Desired dimensions
 desired_width = 576

validators/text_validator.py

@@ -17,7 +17,7 @@ def __init__(self, dendrite, config, subtensor, wallet: bt.wallet):
         super().__init__(dendrite, config, subtensor, wallet, timeout=75)
         self.streaming = True
         self.query_type = "text"
-        self.model =  "gpt-4-1106-preview"
+        self.model = "gpt-4-1106-preview"
         self.max_tokens = 4096
         self.temperature = 0.0001
         self.weight = 1
@@ -77,7 +77,7 @@ async def start_query(self, available_uids, metagraph) -> tuple[list, dict]:
             query_tasks = []
             uid_to_question = {}
             # Randomly choose the provider based on specified probabilities
-            providers = ["OpenAI"] * 88 + ["Anthropic"] * 2 + ["Gemini"] * 0 + ["Claude"] * 10
+            providers = ["OpenAI"] * 70 + ["Anthropic"] * 0 + ["Gemini"] * 0 + ["Claude"] * 30
             self.provider = random.choice(providers)
 
             if self.provider == "Anthropic":
@@ -86,15 +86,17 @@ async def start_query(self, available_uids, metagraph) -> tuple[list, dict]:
                 # gemini models = ["gemini-pro"]
                 self.model = "anthropic.claude-v2:1"
             elif self.provider == "OpenAI":
-                self.model = "gpt-4-1106-preview"
-                # self.model = "gpt-3.5-turbo"
+                # self.model = "gpt-4-1106-preview"
+                self.model = "gpt-3.5-turbo"
 
             elif self.provider == "Gemini":
                 self.model = "gemini-pro"
 
             elif self.provider == "Claude":
-                self.model = "claude-3-opus-20240229"
+                # self.model = "claude-3-opus-20240229"
                 # self.model = "claude-3-sonnet-20240229"
+                self.model = "claude-instant-1.2"
+
             bt.logging.info(f"provider = {self.provider}\nmodel = {self.model}")
             for uid in available_uids:
                 prompt = await self.get_question(len(available_uids))
@@ -116,7 +118,7 @@ async def start_query(self, available_uids, metagraph) -> tuple[list, dict]:
 
     def should_i_score(self):
         random_number = random.random()
-        will_score_all = random_number < 1 / 12
+        will_score_all = random_number < 1 / 2
         bt.logging.info(f"Random Number: {random_number}, Will score text responses: {will_score_all}")
         return will_score_all
 

validators/weight_setter.py

@@ -243,7 +243,7 @@ async def perform_synthetic_scoring_and_update_weights(self):
                     )
 
                 # if we want to slow down the speed of the validator steps
-                await asyncio.sleep(100)
+                # await asyncio.sleep(100)
 
     def select_validator(self, steps_passed):
         return self.text_vali if steps_passed % 10 in (0, 1, 2, 3, 4, 5, 6, 7, 8) else self.image_vali