distil

instructor/distil.py

@@ -92,9 +92,6 @@ class Instructions:
         log_handlers: List[logging.Handler] = None,
         finetune_format: FinetuneFormat = FinetuneFormat.MESSAGES,
         indent: int = 2,
-<<<<<<< HEAD
-    ):
-=======
         include_code_body: bool = False,
     ):
         """
@@ -107,16 +104,12 @@ class Instructions:
         :param indent: Indentation to use for finetuning.
         :param include_code_body: Whether to include the code body in the finetuning.
         """
->>>>>>> distil
         self.name = name
         self.id = id or str(uuid.uuid4())
         self.unique_id = str(uuid.uuid4())
         self.finetune_format = finetune_format
         self.indent = indent
-<<<<<<< HEAD
-=======
         self.include_code_body = include_code_body
->>>>>>> distil
 
         self.logger = logging.getLogger(self.name)
         for handler in log_handlers or []:
@@ -127,10 +120,7 @@ class Instructions:
         *args,
         name: str = None,
         mode: str = "distil",
-<<<<<<< HEAD
-=======
         model: str = "gpt-3.5-turbo",
->>>>>>> distil
         fine_tune_format: FinetuneFormat = None,
     ):
         """
@@ -154,10 +144,6 @@ class Instructions:
         """
         allowed_modes = {"distil", "dispatch"}
         assert mode in allowed_modes, f"Must be in {allowed_modes}"
-<<<<<<< HEAD
-        assert mode == "distil", "Only distil mode is supported at the moment."
-=======
->>>>>>> distil
 
         if fine_tune_format is None:
             fine_tune_format = self.finetune_format
@@ -165,8 +151,6 @@ class Instructions:
         def _wrap_distil(fn):
             msg = f"Return type hint for {fn} must subclass `pydantic.BaseModel'"
             assert is_return_type_base_model_or_instance(fn), msg
-<<<<<<< HEAD
-=======
             return_base_model = inspect.signature(fn).return_annotation
 
             @functools.wraps(fn)
@@ -181,7 +165,6 @@ class Instructions:
                 return openai.ChatCompletion.create(
                     **openai_kwargs, model=model, response_model=return_base_model
                 )
->>>>>>> distil
 
             @functools.wraps(fn)
             def _distil(*args, **kwargs):
@@ -192,15 +175,11 @@ class Instructions:
 
                 return resp
 
-<<<<<<< HEAD
-            return _distil
-=======
             if mode == "dispatch":
                 return _dispatch
 
             if mode == "distil":
                 return _distil
->>>>>>> distil
 
         if len(args) == 1 and callable(args[0]):
             return _wrap_distil(args[0])