Merge pull request #2473 from BerriAI/litellm_fix_compatible_provider_model_name

fix(openai.py): return model name with custom llm provider for openai-compatible endpoints (e.g. mistral, together ai, etc.)

litellm/llms/openai.py

@@ -239,6 +239,7 @@ class OpenAIChatCompletion(BaseLLM):
                 )
 
             if custom_llm_provider != "openai":
+                model_response.model = f"{custom_llm_provider}/{model}"
                 # process all OpenAI compatible provider logic here
                 if custom_llm_provider == "mistral":
                     # check if message content passed in as list, and not string
@@ -254,6 +255,7 @@ class OpenAIChatCompletion(BaseLLM):
                         messages=messages,
                         custom_llm_provider=custom_llm_provider,
                     )
+
             for _ in range(
                 2
             ):  # if call fails due to alternating messages, retry with reformatted message

litellm/main.py

@@ -945,6 +945,7 @@ def completion(
                     custom_prompt_dict=custom_prompt_dict,
                     client=client,  # pass AsyncOpenAI, OpenAI client
                     organization=organization,
+                    custom_llm_provider=custom_llm_provider,
                 )
             except Exception as e:
                 ## LOGGING - log the original exception returned

litellm/tests/test_completion.py

@@ -289,6 +289,7 @@ def test_completion_mistral_api():
         cost = litellm.completion_cost(completion_response=response)
         print("cost to make mistral completion=", cost)
         assert cost > 0.0
+        assert response.model == "mistral/mistral-tiny"
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 

litellm/utils.py

@@ -6427,7 +6427,7 @@ def convert_to_model_response_object(
                     "system_fingerprint"
                 ]
 
-            if "model" in response_object:
+            if "model" in response_object and model_response_object.model is None:
                 model_response_object.model = response_object["model"]
 
             if start_time is not None and end_time is not None: