Undo None check and temporarily move if model check before builder

llama_stack/providers/inline/inference/meta_reference/config.py

@@ -37,8 +37,8 @@ class MetaReferenceInferenceConfig(BaseModel):
     @classmethod
     def validate_model(cls, model: str) -> str:
         permitted_models = supported_inference_models()
-        descriptors = [m.descriptor() for m in permitted_models if m is not None]
-        repos = [m.huggingface_repo for m in permitted_models if m is not None]
+        descriptors = [m.descriptor() for m in permitted_models]
+        repos = [m.huggingface_repo for m in permitted_models]
         if model not in (descriptors + repos):
             model_list = "\n\t".join(repos)
             raise ValueError(

llama_stack/providers/inline/inference/meta_reference/inference.py

@@ -34,6 +34,8 @@ class MetaReferenceInferenceImpl(Inference, ModelRegistryHelper, ModelsProtocolP
     def __init__(self, config: MetaReferenceInferenceConfig) -> None:
         self.config = config
         model = resolve_model(config.model)
+        if model is None:
+            raise RuntimeError(f"Unknown model: {config.model}, Run `llama model list`")
         ModelRegistryHelper.__init__(
             self,
             [
@@ -43,8 +45,6 @@ class MetaReferenceInferenceImpl(Inference, ModelRegistryHelper, ModelsProtocolP
                 )
             ],
         )
-        if model is None:
-            raise RuntimeError(f"Unknown model: {config.model}, Run `llama model list`")
         self.model = model
         # verify that the checkpoint actually is for this model lol
 

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

@@ -49,8 +49,8 @@ class VLLMConfig(BaseModel):
     def validate_model(cls, model: str) -> str:
         permitted_models = supported_inference_models()
 
-        descriptors = [m.descriptor() for m in permitted_models if m is not None] 
-        repos = [m.huggingface_repo for m in permitted_models if m is not None]
+        descriptors = [m.descriptor() for m in permitted_models]
+        repos = [m.huggingface_repo for m in permitted_models]
         if model not in (descriptors + repos):
             model_list = "\n\t".join(repos)
             raise ValueError(

llama_stack/providers/utils/inference/prompt_adapter.py

@@ -179,7 +179,7 @@ def chat_completion_request_to_messages(
         return request.messages
 
     allowed_models = supported_inference_models()
-    descriptors = [m.descriptor() for m in allowed_models if m is not None]
+    descriptors = [m.descriptor() for m in allowed_models]
     if model.descriptor() not in descriptors:
         cprint(f"Unsupported inference model? {model.descriptor()}", color="red")
         return request.messages