fix meta-reference, test vllm

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

@@ -26,6 +26,7 @@ from llama_stack.apis.inference import (
     ChatCompletionResponseEvent,
     ChatCompletionResponseEventType,
     ChatCompletionResponseStreamChunk,
+    CompletionMessage,
     CompletionRequest,
     CompletionResponse,
     CompletionResponseStreamChunk,
@@ -315,11 +316,15 @@ class MetaReferenceInferenceImpl(
             if stop_reason is None:
                 stop_reason = StopReason.out_of_tokens
 
-            message = self.generator.formatter.decode_assistant_message(
+            raw_message = self.generator.formatter.decode_assistant_message(
                 tokens, stop_reason
             )
             return ChatCompletionResponse(
-                completion_message=message,
+                completion_message=CompletionMessage(
+                    content=raw_message.content,
+                    stop_reason=raw_message.stop_reason,
+                    tool_calls=raw_message.tool_calls,
+                ),
                 logprobs=logprobs if request.logprobs else None,
             )
 
@@ -454,7 +459,9 @@ async def convert_request_to_raw(
         messages = []
         for m in request.messages:
             content = await interleaved_content_convert_to_raw(m.content)
-            messages.append(RawMessage(**m.model_dump(), content=content))
+            d = m.model_dump()
+            d["content"] = content
+            messages.append(RawMessage(**d))
         request.messages = messages
     else:
         request.content = await interleaved_content_convert_to_raw(request.content)

llama_stack/providers/tests/inference/fixtures.py

@@ -113,6 +113,7 @@ def inference_vllm_remote() -> ProviderFixture:
                 provider_type="remote::vllm",
                 config=VLLMInferenceAdapterConfig(
                     url=get_env_or_fail("VLLM_URL"),
+                    max_tokens=int(os.getenv("VLLM_MAX_TOKENS", 2048)),
                 ).model_dump(),
             )
         ],

llama_stack/providers/utils/inference/prompt_adapter.py

@@ -103,6 +103,8 @@ async def interleaved_content_convert_to_raw(
                         data = response.content
                 else:
                     raise ValueError("Unsupported URL type")
+            else:
+                data = c.data
             return RawMediaItem(data=data)
         else:
             raise ValueError(f"Unsupported content type: {type(c)}")