rebase and fix some small breakage due to model -> model_id fix

llama_stack/distribution/routers/routing_tables.py

@@ -38,15 +38,15 @@ async def register_object_with_provider(obj: RoutableObject, p: Any) -> Routable
     if api == Api.inference:
         return await p.register_model(obj)
     elif api == Api.safety:
-        await p.register_shield(obj)
+        return await p.register_shield(obj)
     elif api == Api.memory:
-        await p.register_memory_bank(obj)
+        return await p.register_memory_bank(obj)
     elif api == Api.datasetio:
-        await p.register_dataset(obj)
+        return await p.register_dataset(obj)
     elif api == Api.scoring:
-        await p.register_scoring_function(obj)
+        return await p.register_scoring_function(obj)
     elif api == Api.eval:
-        await p.register_eval_task(obj)
+        return await p.register_eval_task(obj)
     else:
         raise ValueError(f"Unknown API {api} for registering object with provider")
 

llama_stack/providers/inline/safety/llama_guard/llama_guard.py

@@ -234,7 +234,7 @@ class LlamaGuardShield:
         # TODO: llama-stack inference protocol has issues with non-streaming inference code
         content = ""
         async for chunk in await self.inference_api.chat_completion(
-            model=self.model,
+            model_id=self.model,
             messages=[shield_input_message],
             stream=True,
         ):

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

@@ -164,7 +164,6 @@ class OllamaInferenceAdapter(Inference, ModelRegistryHelper, ModelsProtocolPriva
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
         model = await self.model_store.get_model(model_id)
-        print(f"model={model}")
         request = ChatCompletionRequest(
             model=model.provider_resource_id,
             messages=messages,

llama_stack/providers/tests/agents/conftest.py

@@ -46,6 +46,16 @@ DEFAULT_PROVIDER_COMBINATIONS = [
         id="together",
         marks=pytest.mark.together,
     ),
+    pytest.param(
+        {
+            "inference": "fireworks",
+            "safety": "llama_guard",
+            "memory": "faiss",
+            "agents": "meta_reference",
+        },
+        id="fireworks",
+        marks=pytest.mark.fireworks,
+    ),
     pytest.param(
         {
             "inference": "remote",
@@ -60,7 +70,7 @@ DEFAULT_PROVIDER_COMBINATIONS = [
 
 
 def pytest_configure(config):
-    for mark in ["meta_reference", "ollama", "together", "remote"]:
+    for mark in ["meta_reference", "ollama", "together", "fireworks", "remote"]:
         config.addinivalue_line(
             "markers",
             f"{mark}: marks tests as {mark} specific",

llama_stack/providers/tests/inference/test_text_inference.py

@@ -147,9 +147,9 @@ class TestInference:
 
         user_input = "Michael Jordan was born in 1963. He played basketball for the Chicago Bulls. He retired in 2003."
         response = await inference_impl.completion(
+            model_id=inference_model,
             content=user_input,
             stream=False,
-            model=inference_model,
             sampling_params=SamplingParams(
                 max_tokens=50,
             ),

llama_stack/providers/tests/inference/test_vision_inference.py

@@ -55,7 +55,7 @@ class TestVisionModelInference:
             )
 
         response = await inference_impl.chat_completion(
-            model=inference_model,
+            model_id=inference_model,
             messages=[
                 UserMessage(content="You are a helpful assistant."),
                 UserMessage(content=[image, "Describe this image in two sentences."]),
@@ -102,7 +102,7 @@ class TestVisionModelInference:
             response = [
                 r
                 async for r in await inference_impl.chat_completion(
-                    model=inference_model,
+                    model_id=inference_model,
                     messages=[
                         UserMessage(content="You are a helpful assistant."),
                         UserMessage(