diff --git a/evaluation/inference/human_eval/run_human_eval.py b/evaluation/inference/human_eval/run_human_eval.py
index 3a9c2f0fa..5ca29dcf6 100644
--- a/evaluation/inference/human_eval/run_human_eval.py
+++ b/evaluation/inference/human_eval/run_human_eval.py
@@ -9,9 +9,9 @@
 from human_eval.evaluation import evaluate_functional_correctness
 
 parser = argparse.ArgumentParser()
-parser.add_argument("--model", "-m", type=str, help="evaluation model name")
-parser.add_argument("--max-tokens", type=int, default=256, help="max new tokens")
-parser.add_argument("--num-samples-per-task", type=int, default=10, help="number of samples to gen/eval per task")
+parser.add_argument("--model", "-m", type=str, default="codellama/CodeLlama-7b-Python-hf", help="evaluation model name")
+parser.add_argument("--max-tokens", type=int, default=512, help="max new tokens")
+parser.add_argument("--num-samples-per-task", type=int, default=20, help="number of samples to gen/eval per task")
 parser.add_argument("--local_rank", type=int, default=int(os.getenv("LOCAL_RANK", "0")), help="local rank")
 args = parser.parse_args()
 
@@ -27,7 +27,9 @@ def generate_samples(generation_function):
         for _ in range(args.num_samples_per_task)
     ]
     return samples
-# TODO (lekurile): Move these functions to utils in DSE repo (TBD)
+
+print("Loading Problems")
+problems = read_problems("human-eval/data/HumanEval.jsonl.gz")
 
 print("Initializing HuggingFace Pipeline")
 device = torch.device(get_accelerator().device_name(args.local_rank))
@@ -36,18 +38,22 @@ def generate_samples(generation_function):
                         max_length=args.max_tokens,
                         return_full_text=False)
 
-print("Initializing DeepSpeed-MII Pipeline")
-mii_pipe = mii.pipeline(args.model)
-
-print("Loading Problems")
-problems = read_problems("human-eval/data/HumanEval.jsonl.gz")
-
 print("Generating Base Samples")
 base_samples = generate_samples(generate_base_completion)
 
+print("Base Pipeline Teardown")
+del base_pipe
+torch.cuda.empty_cache()
+
+print("Initializing DeepSpeed-MII Pipeline")
+mii_pipe = mii.pipeline(args.model)
+
 print("Generating MII Samples")
 mii_samples = generate_samples(generate_mii_completion)
 
+print("MII Pipeline Teardown")
+mii_pipe.destroy()
+
 print("Writing Samples")
 write_jsonl("base_samples.jsonl", base_samples)
 write_jsonl("mii_samples.jsonl", mii_samples)
@@ -59,7 +65,5 @@ def generate_samples(generation_function):
 print(f"Base Results = {base_results}")
 print(f"MII Results = {mii_results}")
 
-print("Executing Assertions")
 for key in base_results.keys():
-    assert numpy.allclose(base_results[key], mii_results[key], rtol=0.2), \
-        f"Base result: {base_results[key]}, MII result: {mii_results[key]}, outside of rtol."
+    print(f"{key} - Base Result: {base_results[key]}, MII result: {mii_results[key]}")