use corrext wandb entity

evoprompt/models.py

-from collections.abc import Iterable
 import hashlib
 import inspect
-from itertools import zip_longest
 import json
 import logging
 import random
@@ -10,14 +8,16 @@ import time
 import warnings
 from abc import ABC, abstractmethod
 from argparse import ArgumentParser
+from collections.abc import Iterable
+from itertools import zip_longest
 from pathlib import Path
 from typing import Any, Callable, ClassVar
 
 import llama_cpp
-from llama_cpp.llama_chat_format import format_llama3
 import openai
 import weave
 from diskcache import Cache
+from llama_cpp.llama_chat_format import format_llama3
 
 from evoprompt.cli import argument_parser
 from evoprompt.opt_types import ModelUsage

main.py

@@ -2,11 +2,11 @@ import logging
 import os
 from typing import Any
 
-from dotenv import load_dotenv
-import wandb
 import weave
+from dotenv import load_dotenv
 from weave.trace.settings import UserSettings
 
+import wandb
 from evoprompt.cli import argument_parser
 from evoprompt.evolution import get_optimizer_class
 from evoprompt.models import HfChat, Llama, LlamaChat, LLMModel
@@ -62,7 +62,11 @@ if __name__ == "__main__":
         # TODO weave recently had 500 errors quite often, so we disable it for now
         # weave_settings = UserSettings(disabled=False, print_call_link=False)
         # weave.init(project_name=options.wandb_project, settings=weave_settings)
-        wandb.init(project=options.wandb_project, config=options.__dict__)
+        wandb.init(
+            entity="prompt-optimization",
+            project=options.wandb_project,
+            config=options.__dict__,
+        )
 
     # set up console logging and rnd
     setup_console_logger(verbosity_level=options.verbose)