diff --git a/cortext/__init__.py b/cortext/__init__.py index bcc6d01b..094214fd 100644 --- a/cortext/__init__.py +++ b/cortext/__init__.py @@ -19,7 +19,7 @@ # version must stay on line 22 -__version__ = "3.2.8" +__version__ = "3.2.9" version_split = __version__.split(".") __spec_version__ = ( (1000 * int(version_split[0])) diff --git a/test_scripts/t2t/test_claude.py b/test_scripts/t2t/test_claude.py index 36631caa..cfe94290 100644 --- a/test_scripts/t2t/test_claude.py +++ b/test_scripts/t2t/test_claude.py @@ -50,6 +50,7 @@ async def call_claude(messages, max_tokens, model): # Send final message to close the stream print("\n") + # non streaming # async def call_claude(messages, max_tokens, model): # filtered_messages = [] diff --git a/validators/text_validator.py b/validators/text_validator.py index 61612e52..e62dddba 100644 --- a/validators/text_validator.py +++ b/validators/text_validator.py @@ -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"] * 70 + ["Anthropic"] * 0 + ["Gemini"] * 0 + ["Claude"] * 30 + providers = ["OpenAI"] * 100 + ["Anthropic"] * 0 + ["Gemini"] * 0 + ["Claude"] * 0 self.provider = random.choice(providers) if self.provider == "Anthropic": @@ -86,8 +86,8 @@ 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" @@ -118,7 +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 / 2 + will_score_all = random_number < 1 / 12 bt.logging.info(f"Random Number: {random_number}, Will score text responses: {will_score_all}") return will_score_all