model registration in ollama and vllm check against the available models in the provider (#446)

tests:
pytest -v -s -m "ollama"
llama_stack/providers/tests/inference/test_text_inference.py

pytest -v -s -m vllm_remote
llama_stack/providers/tests/inference/test_text_inference.py --env
VLLM_URL="http://localhost:9798/v1"

---------

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -71,12 +71,9 @@ model_aliases = [
 ]
 
 
-class OllamaInferenceAdapter(Inference, ModelRegistryHelper, ModelsProtocolPrivate):
+class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
     def __init__(self, url: str) -> None:
-        ModelRegistryHelper.__init__(
-            self,
-            model_aliases=model_aliases,
-        )
+        self.register_helper = ModelRegistryHelper(model_aliases)
         self.url = url
         self.formatter = ChatFormat(Tokenizer.get_instance())
 
@@ -203,7 +200,9 @@ class OllamaInferenceAdapter(Inference, ModelRegistryHelper, ModelsProtocolPriva
             else:
                 input_dict["raw"] = True
                 input_dict["prompt"] = chat_completion_request_to_prompt(
-                    request, self.get_llama_model(request.model), self.formatter
+                    request,
+                    self.register_helper.get_llama_model(request.model),
+                    self.formatter,
                 )
         else:
             assert (
@@ -282,6 +281,18 @@ class OllamaInferenceAdapter(Inference, ModelRegistryHelper, ModelsProtocolPriva
     ) -> EmbeddingsResponse:
         raise NotImplementedError()
 
+    async def register_model(self, model: Model) -> Model:
+        model = await self.register_helper.register_model(model)
+        models = await self.client.ps()
+        available_models = [m["model"] for m in models["models"]]
+        if model.provider_resource_id not in available_models:
+            raise ValueError(
+                f"Model '{model.provider_resource_id}' is not available in Ollama. "
+                f"Available models: {', '.join(available_models)}"
+            )
+
+        return model
+
 
 async def convert_message_to_dict_for_ollama(message: Message) -> List[dict]:
     async def _convert_content(content) -> dict: