diff --git a/llama_stack/providers/inline/inference/meta_reference/inference.py b/llama_stack/providers/inline/inference/meta_reference/inference.py
index 2fdc8f2d5..1e668b183 100644
--- a/llama_stack/providers/inline/inference/meta_reference/inference.py
+++ b/llama_stack/providers/inline/inference/meta_reference/inference.py
@@ -46,7 +46,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
             self.generator = Llama.build(self.config)
 
     async def register_model(self, model: Model) -> None:
-        if model.identifier != self.model.descriptor():
+        if model.provider_resource_id != self.model.descriptor():
             raise ValueError(
                 f"Model mismatch: {model.identifier} != {self.model.descriptor()}"
             )
@@ -68,7 +68,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
 
     async def completion(
         self,
-        model: str,
+        model_id: str,
         content: InterleavedTextMedia,
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         response_format: Optional[ResponseFormat] = None,
@@ -79,7 +79,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
             assert logprobs.top_k == 1, f"Unexpected top_k={logprobs.top_k}"
 
         request = CompletionRequest(
-            model=model,
+            model=model_id,
             content=content,
             sampling_params=sampling_params,
             response_format=response_format,
@@ -186,7 +186,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
 
     async def chat_completion(
         self,
-        model: str,
+        model_id: str,
         messages: List[Message],
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         response_format: Optional[ResponseFormat] = None,
@@ -201,7 +201,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
 
         # wrapper request to make it easier to pass around (internal only, not exposed to API)
         request = ChatCompletionRequest(
-            model=model,
+            model=model_id,
             messages=messages,
             sampling_params=sampling_params,
             tools=tools or [],
@@ -386,7 +386,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
 
     async def embeddings(
         self,
-        model: str,
+        model_id: str,
         contents: List[InterleavedTextMedia],
     ) -> EmbeddingsResponse:
         raise NotImplementedError()
diff --git a/llama_stack/providers/inline/inference/vllm/vllm.py b/llama_stack/providers/inline/inference/vllm/vllm.py
index 3b1a0dd50..8869cc07f 100644
--- a/llama_stack/providers/inline/inference/vllm/vllm.py
+++ b/llama_stack/providers/inline/inference/vllm/vllm.py
@@ -110,7 +110,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
 
     async def completion(
         self,
-        model: str,
+        model_id: str,
         content: InterleavedTextMedia,
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         response_format: Optional[ResponseFormat] = None,
@@ -120,7 +120,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         log.info("vLLM completion")
         messages = [UserMessage(content=content)]
         return self.chat_completion(
-            model=model,
+            model=model_id,
             messages=messages,
             sampling_params=sampling_params,
             stream=stream,
@@ -129,7 +129,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
 
     async def chat_completion(
         self,
-        model: str,
+        model_id: str,
         messages: List[Message],
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         tools: Optional[List[ToolDefinition]] = None,
@@ -144,7 +144,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         assert self.engine is not None
 
         request = ChatCompletionRequest(
-            model=model,
+            model=model_id,
             messages=messages,
             sampling_params=sampling_params,
             tools=tools or [],
@@ -215,7 +215,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
             yield chunk
 
     async def embeddings(
-        self, model: str, contents: list[InterleavedTextMedia]
+        self, model_id: str, contents: list[InterleavedTextMedia]
     ) -> EmbeddingsResponse:
         log.info("vLLM embeddings")
         # TODO
diff --git a/llama_stack/providers/remote/inference/ollama/ollama.py b/llama_stack/providers/remote/inference/ollama/ollama.py
index 938d05c08..f5750e0cf 100644
--- a/llama_stack/providers/remote/inference/ollama/ollama.py
+++ b/llama_stack/providers/remote/inference/ollama/ollama.py
@@ -66,8 +66,10 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
         pass
 
     async def register_model(self, model: Model) -> None:
-        if model.identifier not in OLLAMA_SUPPORTED_MODELS:
-            raise ValueError(f"Model {model.identifier} is not supported by Ollama")
+        if model.provider_resource_id not in OLLAMA_SUPPORTED_MODELS:
+            raise ValueError(
+                f"Model {model.provider_resource_id} is not supported by Ollama"
+            )
 
     async def list_models(self) -> List[Model]:
         ollama_to_llama = {v: k for k, v in OLLAMA_SUPPORTED_MODELS.items()}
@@ -94,7 +96,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
 
     async def completion(
         self,
-        model: str,
+        model_id: str,
         content: InterleavedTextMedia,
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         response_format: Optional[ResponseFormat] = None,
@@ -102,7 +104,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
         request = CompletionRequest(
-            model=model,
+            model=model_id,
             content=content,
             sampling_params=sampling_params,
             stream=stream,
@@ -148,7 +150,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
 
     async def chat_completion(
         self,
-        model: str,
+        model_id: str,
         messages: List[Message],
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         response_format: Optional[ResponseFormat] = None,
@@ -159,7 +161,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
         request = ChatCompletionRequest(
-            model=model,
+            model=model_id,
             messages=messages,
             sampling_params=sampling_params,
             tools=tools or [],
@@ -271,7 +273,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
 
     async def embeddings(
         self,
-        model: str,
+        model_id: str,
         contents: List[InterleavedTextMedia],
     ) -> EmbeddingsResponse:
         raise NotImplementedError()
diff --git a/llama_stack/providers/remote/inference/vllm/vllm.py b/llama_stack/providers/remote/inference/vllm/vllm.py
index bd7f5073c..3a8b8c326 100644
--- a/llama_stack/providers/remote/inference/vllm/vllm.py
+++ b/llama_stack/providers/remote/inference/vllm/vllm.py
@@ -45,8 +45,15 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         self.client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
 
     async def register_model(self, model: Model) -> None:
-        for running_model in self.client.models.list():
-            repo = running_model.id
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def list_models(self) -> List[Model]:
+        models = []
+        for model in self.client.models.list():
+            repo = model.id
             if repo not in self.huggingface_repo_to_llama_model_id:
                 print(f"Unknown model served by vllm: {repo}")
                 continue
@@ -67,7 +74,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
 
     async def completion(
         self,
-        model: str,
+        model_id: str,
         content: InterleavedTextMedia,
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         response_format: Optional[ResponseFormat] = None,
@@ -78,7 +85,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
 
     async def chat_completion(
         self,
-        model: str,
+        model_id: str,
         messages: List[Message],
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         response_format: Optional[ResponseFormat] = None,
@@ -89,7 +96,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
         request = ChatCompletionRequest(
-            model=model,
+            model=model_id,
             messages=messages,
             sampling_params=sampling_params,
             tools=tools or [],
@@ -173,7 +180,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
 
     async def embeddings(
         self,
-        model: str,
+        model_id: str,
         contents: List[InterleavedTextMedia],
     ) -> EmbeddingsResponse:
         raise NotImplementedError()
diff --git a/llama_stack/providers/tests/inference/fixtures.py b/llama_stack/providers/tests/inference/fixtures.py
index 2a4eba3ad..59bd492b9 100644
--- a/llama_stack/providers/tests/inference/fixtures.py
+++ b/llama_stack/providers/tests/inference/fixtures.py
@@ -49,7 +49,7 @@ def inference_meta_reference(inference_model) -> ProviderFixture:
         providers=[
             Provider(
                 provider_id=f"meta-reference-{i}",
-                provider_type="meta-reference",
+                provider_type="inline::meta-reference",
                 config=MetaReferenceInferenceConfig(
                     model=m,
                     max_seq_len=4096,