diff --git a/src/raglite/_litellm.py b/src/raglite/_litellm.py
index 31bf279..9f1d0ea 100644
--- a/src/raglite/_litellm.py
+++ b/src/raglite/_litellm.py
@@ -22,6 +22,7 @@
     get_model_info,
 )
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.utils import custom_llm_setup
 from llama_cpp import (  # type: ignore[attr-defined]
     ChatCompletionRequestMessage,
     CreateChatCompletionResponse,
@@ -33,6 +34,7 @@
 from raglite._config import RAGLiteConfig
 
 # Reduce the logging level for LiteLLM, flashrank, and httpx.
+litellm.suppress_debug_info = True
 os.environ["LITELLM_LOG"] = "WARNING"
 logging.getLogger("LiteLLM").setLevel(logging.WARNING)
 logging.getLogger("flashrank").setLevel(logging.WARNING)
@@ -125,24 +127,23 @@ def llm(model: str, **kwargs: Any) -> Llama:
         # Enable caching.
         llm.set_cache(LlamaRAMCache())
         # Register the model info with LiteLLM.
-        litellm.register_model(  # type: ignore[attr-defined]
-            {
-                model: {
-                    "max_tokens": llm.n_ctx(),
-                    "max_input_tokens": llm.n_ctx(),
-                    "max_output_tokens": None,
-                    "input_cost_per_token": 0.0,
-                    "output_cost_per_token": 0.0,
-                    "output_vector_size": llm.n_embd() if kwargs.get("embedding") else None,
-                    "litellm_provider": "llama-cpp-python",
-                    "mode": "embedding" if kwargs.get("embedding") else "completion",
-                    "supported_openai_params": LlamaCppPythonLLM.supported_openai_params,
-                    "supports_function_calling": True,
-                    "supports_parallel_function_calling": True,
-                    "supports_vision": False,
-                }
+        model_info = {
+            model: {
+                "max_tokens": llm.n_ctx(),
+                "max_input_tokens": llm.n_ctx(),
+                "max_output_tokens": None,
+                "input_cost_per_token": 0.0,
+                "output_cost_per_token": 0.0,
+                "output_vector_size": llm.n_embd() if kwargs.get("embedding") else None,
+                "litellm_provider": "llama-cpp-python",
+                "mode": "embedding" if kwargs.get("embedding") else "completion",
+                "supported_openai_params": LlamaCppPythonLLM.supported_openai_params,
+                "supports_function_calling": True,
+                "supports_parallel_function_calling": True,
+                "supports_vision": False,
             }
-        )
+        }
+        litellm.register_model(model_info)  # type: ignore[attr-defined]
         return llm
 
     def _translate_openai_params(self, optional_params: dict[str, Any]) -> dict[str, Any]:
@@ -307,7 +308,7 @@ async def astreaming(  # type: ignore[misc,override]  # noqa: PLR0913
     litellm.custom_provider_map.append(
         {"provider": "llama-cpp-python", "custom_handler": LlamaCppPythonLLM()}
     )
-    litellm.suppress_debug_info = True
+    custom_llm_setup()  # type: ignore[no-untyped-call]
 
 
 @cache