diff --git a/engines/python/setup/djl_python/lmi_vllm/request_response_utils.py b/engines/python/setup/djl_python/lmi_vllm/request_response_utils.py
index bb050cc6d..0d0168845 100644
--- a/engines/python/setup/djl_python/lmi_vllm/request_response_utils.py
+++ b/engines/python/setup/djl_python/lmi_vllm/request_response_utils.py
@@ -55,6 +55,7 @@ def convert_lmi_schema_to_completion_request(
     parameters = payload.get("parameters", {})
 
     completion_dict = {
+        "model": payload.pop("model"),
         "prompt": payload.pop("inputs"),
         "max_tokens": parameters.pop("max_new_tokens", 30),
         "echo": parameters.pop("return_full_text", False),
diff --git a/engines/python/setup/djl_python/lmi_vllm/vllm_async_service.py b/engines/python/setup/djl_python/lmi_vllm/vllm_async_service.py
index 792e462ab..f3d216561 100644
--- a/engines/python/setup/djl_python/lmi_vllm/vllm_async_service.py
+++ b/engines/python/setup/djl_python/lmi_vllm/vllm_async_service.py
@@ -165,6 +165,8 @@ def preprocess_request(self, inputs: Input) -> ProcessedRequest:
             logging.info(
                 f"Using LoRA request: {lora_request.lora_name} (ID: {lora_request.lora_int_id})"
             )
+            # Set the model field to the adapter name so vLLM's _maybe_get_adapters() can extract it
+            decoded_payload["model"] = adapter_name
 
         # completions request
         if "prompt" in decoded_payload:
@@ -238,22 +240,9 @@ async def inference(
                 "", error=f"Input parsing failed: {str(e)}", code=424)
             return output
 
-        if processed_request.lora_request:
-            original_add_request = self.vllm_engine.add_request
-
-            async def add_request_with_lora(*args, **kwargs):
-                kwargs['lora_request'] = processed_request.lora_request
-                return await original_add_request(*args, **kwargs)
-
-            self.vllm_engine.add_request = add_request_with_lora
-            try:
-                response = await processed_request.inference_invoker(
-                    processed_request.vllm_request)
-            finally:
-                self.vllm_engine.add_request = original_add_request
-        else:
-            response = await processed_request.inference_invoker(
-                processed_request.vllm_request)
+        # vLLM will extract the adapter from the request object via _maybe_get_adapters()
+        response = await processed_request.inference_invoker(
+            processed_request.vllm_request)
 
         if isinstance(response, types.AsyncGeneratorType):
             # Apply custom formatter to streaming response