Apply formatting to source files

llama_stack/providers/inline/inference/vllm/vllm.py

@@ -88,23 +88,25 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         """
         Callback that is called when the server associates an inference endpoint
         with an inference provider.
-        
+
         :param model: Object that encapsulates parameters necessary for identifying
          a specific LLM.
-        
+
         :returns: The input ``Model`` object. It may or may not be permissible
          to change fields before returning this object.
         """
-        log.info(f"Registering model {model.identifier} with vLLM inference provider.") 
-        # The current version of this provided is hard-coded to serve only 
+        log.info(f"Registering model {model.identifier} with vLLM inference provider.")
+        # The current version of this provided is hard-coded to serve only
         # the model specified in the YAML config file.
         configured_model = resolve_model(self.config.model)
         registered_model = resolve_model(model.model_id)
-        
+
         if configured_model.core_model_id != registered_model.core_model_id:
-            raise ValueError(f"Requested model '{model.identifier}' is different from "
-                             f"model '{self.config.model}' that this provider "
-                             f"is configured to serve")
+            raise ValueError(
+                f"Requested model '{model.identifier}' is different from "
+                f"model '{self.config.model}' that this provider "
+                f"is configured to serve"
+            )
         return model
 
     def _sampling_params(self, sampling_params: SamplingParams) -> VLLMSamplingParams:
@@ -169,8 +171,9 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         log.info("Sampling params: %s", sampling_params)
         request_id = _random_uuid()
 
-        prompt = await chat_completion_request_to_prompt(request, self.config.model,
-                                                         self.formatter)
+        prompt = await chat_completion_request_to_prompt(
+            request, self.config.model, self.formatter
+        )
         vllm_sampling_params = self._sampling_params(request.sampling_params)
         results_generator = self.engine.generate(
             prompt, vllm_sampling_params, request_id
@@ -226,7 +229,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
             stream, self.formatter
         ):
             yield chunk
-            
+
     async def embeddings(
         self, model_id: str, contents: List[InterleavedContent]
     ) -> EmbeddingsResponse:

llama_stack/providers/tests/inference/fixtures.py

@@ -292,6 +292,6 @@ async def inference_stack(request, inference_model):
 
     # Pytest yield fixture; see https://docs.pytest.org/en/stable/how-to/fixtures.html#yield-fixtures-recommended
     yield test_stack.impls[Api.inference], test_stack.impls[Api.models]
-    
+
     # Cleanup code that runs after test case completion
     await test_stack.impls[Api.inference].shutdown()