fix typing

litellm/fine_tuning/main.py

@@ -224,7 +224,7 @@ def create_fine_tuning_job(
                 _is_async=_is_async,
             )
         elif custom_llm_provider == "vertex_ai":
-            api_base = optional_params.api_base
+            api_base = optional_params.api_base or ""
             vertex_ai_project = (
                 optional_params.vertex_project
                 or litellm.vertex_project

litellm/llms/fine_tuning_apis/vertex_ai.py

@@ -30,11 +30,13 @@ class VertexFineTuningAPI(VertexLLM):
 
     def convert_response_created_at(self, response: ResponseTuningJob):
         try:
-            create_time = datetime.fromisoformat(
-                response["createTime"].replace("Z", "+00:00")
+
+            create_time_str = response.get("createTime", "") or ""
+            create_time_datetime = datetime.fromisoformat(
+                create_time_str.replace("Z", "+00:00")
             )
             # Convert to Unix timestamp (seconds since epoch)
-            created_at = int(create_time.timestamp())
+            created_at = int(create_time_datetime.timestamp())
 
             return created_at
         except Exception as e:
@@ -59,33 +61,36 @@ class VertexFineTuningAPI(VertexLLM):
 
         created_at = self.convert_response_created_at(response)
 
+        training_uri = ""
+        if "supervisedTuningSpec" in response and response["supervisedTuningSpec"]:
+            training_uri = response["supervisedTuningSpec"]["trainingDatasetUri"] or ""
+
         return FineTuningJob(
-            id=response["name"],
+            id=response["name"] or "",
             created_at=created_at,
             fine_tuned_model=response["tunedModelDisplayName"],
             finished_at=None,
             hyperparameters=Hyperparameters(
-                n_epochs=0, batch_size="", learning_rate_multiplier=""
+                n_epochs=0,
             ),
-            model=response["baseModel"],
+            model=response["baseModel"] or "",
             object="fine_tuning.job",
             organization_id="",
             result_files=[],
             seed=0,
             status=status,
             trained_tokens=None,
-            training_file=response["supervisedTuningSpec"]["trainingDatasetUri"],
+            training_file=training_uri,
             validation_file=None,
             estimated_finish=None,
             integrations=[],
-            user_provided_suffix=None,
         )
 
     async def acreate_fine_tuning_job(
         self,
         fine_tuning_url: str,
         headers: dict,
-        request_data: dict,
+        request_data: FineTuneJobCreate,
     ):
         from litellm.fine_tuning.main import FineTuningJob
 
@@ -95,10 +100,14 @@ class VertexFineTuningAPI(VertexLLM):
                 fine_tuning_url,
                 request_data,
             )
+            if self.async_handler is None:
+                raise ValueError(
+                    "VertexAI Fine Tuning - async_handler is not initialized"
+                )
             response = await self.async_handler.post(
                 headers=headers,
                 url=fine_tuning_url,
-                json=request_data,
+                json=request_data,  # type: ignore
             )
 
             if response.status_code != 200:
@@ -110,7 +119,16 @@ class VertexFineTuningAPI(VertexLLM):
                 "got response from creating fine tuning job: %s", response.json()
             )
 
-            vertex_response = ResponseTuningJob(**response.json())
+            vertex_response = ResponseTuningJob(
+                name=None,
+                tunedModelDisplayName=None,
+                baseModel=None,
+                supervisedTuningSpec=None,
+                state=None,
+                createTime=None,
+                updateTime=None,
+                **response.json(),
+            )
 
             verbose_logger.debug("vertex_response %s", vertex_response)
             open_ai_response = self.convert_vertex_response_to_open_ai_response(
@@ -140,7 +158,7 @@ class VertexFineTuningAPI(VertexLLM):
         )
 
         auth_header, _ = self._get_token_and_url(
-            model=None,
+            model="",
             gemini_api_key=None,
             vertex_credentials=vertex_credentials,
             vertex_project=vertex_project,
@@ -172,16 +190,15 @@ class VertexFineTuningAPI(VertexLLM):
             )
         sync_handler = HTTPHandler(timeout=httpx.Timeout(timeout=600.0, connect=5.0))
 
-        request_data = fine_tune_job
         verbose_logger.debug(
             "about to create fine tuning job: %s, request_data: %s",
             fine_tuning_url,
-            request_data,
+            fine_tune_job,
         )
-        response = self.sync_handler.post(
+        response = sync_handler.post(
             headers=headers,
             url=fine_tuning_url,
-            json=request_data,
+            json=fine_tune_job,  # type: ignore
         )
 
         if response.status_code != 200:
@@ -192,7 +209,16 @@ class VertexFineTuningAPI(VertexLLM):
         verbose_logger.debug(
             "got response from creating fine tuning job: %s", response.json()
         )
-        vertex_response = ResponseTuningJob(**response.json())
+        vertex_response = ResponseTuningJob(
+            name=None,
+            tunedModelDisplayName=None,
+            baseModel=None,
+            supervisedTuningSpec=None,
+            state=None,
+            createTime=None,
+            updateTime=None,
+            **response.json(),
+        )
 
         verbose_logger.debug("vertex_response %s", vertex_response)
         open_ai_response = self.convert_vertex_response_to_open_ai_response(