diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py
index c698d6ada1..10c5ed7066 100644
--- a/litellm/tests/test_completion.py
+++ b/litellm/tests/test_completion.py
@@ -123,7 +123,7 @@ def test_completion_gpt4_turbo():
         pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
-test_completion_gpt4_turbo()
+# test_completion_gpt4_turbo()
 
 def test_completion_perplexity_api():
     try:
@@ -756,37 +756,19 @@ def test_completion_azure_deployment_id():
 
 # test_completion_anthropic_openai_proxy()
 
-# Replicate API endpoints are unstable -> throw random CUDA errors -> this means our tests can fail even if our tests weren't incorrect.
-
-# def test_completion_replicate_llama_2():
-#     model_name = "replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3"
-#     litellm.replicate_config(max_new_tokens=200)
-#     try:
-#         response = completion(
-#             model=model_name, 
-#             messages=messages, 
-#         )
-#         print(response)
-#         cost = completion_cost(completion_response=response)
-#         print("Cost for completion call with llama-2: ", f"${float(cost):.10f}")
-#         # Add any assertions here to check the response
-#         response_str = response["choices"][0]["message"]["content"]
-#         print(response_str)
-#         if type(response_str) != str:
-#             pytest.fail(f"Error occurred: {e}")
-#     except Exception as e:
-#         pytest.fail(f"Error occurred: {e}")
-# test_completion_replicate_llama_2()
-
 def test_completion_replicate_vicuna():
     print("TESTING REPLICATE")
+    litellm.set_verbose=False
     model_name = "replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b"
     try:
         response = completion(
             model=model_name, 
             messages=messages, 
-            custom_llm_provider="replicate",
-            temperature=0.1,
+            temperature=0.5,
+            top_k=20,
+            repetition_penalty=1,
+            min_tokens=1,
+            seed=-1,
             max_tokens=20,
         )
         print(response)