diff --git a/token_benchmark_ray.py b/token_benchmark_ray.py
index 63216b1..c0380f4 100644
--- a/token_benchmark_ray.py
+++ b/token_benchmark_ray.py
@@ -24,10 +24,11 @@
 )
 from tqdm import tqdm
 
-from transformers import LlamaTokenizerFast
+from transformers import AutoTokenizer, LlamaTokenizerFast
 
 def get_token_throughput_latencies(
     model: str,
+    tokenizer_name_or_path: str,
     mean_input_tokens: int,
     stddev_input_tokens: int,
     mean_output_tokens: int,
@@ -42,6 +43,7 @@ def get_token_throughput_latencies(
 
     Args:
         model: The name of the model to query.
+        tokenizer_name_or_path: The Hugging Face name or local path of the tokenizer to use for tokenizing prompts and completions.
         mean_input_tokens: The mean number of tokens to send in the prompt for the request.
         stddev_input_tokens: The standard deviation of the number of tokens to send in the prompt for the request.
         mean_output_tokens: The mean number of tokens to generate per request.
@@ -60,9 +62,12 @@ def get_token_throughput_latencies(
     """
     random.seed(11111)
 
-    tokenizer = LlamaTokenizerFast.from_pretrained(
-        "hf-internal-testing/llama-tokenizer"
-    )
+    if tokenizer_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path)
+    else:
+        tokenizer = LlamaTokenizerFast.from_pretrained(
+            "hf-internal-testing/llama-tokenizer"
+        )
     get_token_length = lambda text: len(tokenizer.encode(text))
     
     if not additional_sampling_params:
@@ -282,6 +287,7 @@ def flatten(item):
 def run_token_benchmark(
     llm_api: str,
     model: str,
+    tokenizer_name_or_path: str,
     test_timeout_s: int,
     max_num_completed_requests: int,
     num_concurrent_requests: int,
@@ -297,6 +303,7 @@ def run_token_benchmark(
     Args:
         llm_api: The name of the llm api to use.
         model: The name of the model to query.
+        tokenizer_name_or_path: The Hugging Face name or local path of the tokenizer to use for tokenizing prompts and completions.
         max_num_completed_requests: The number of requests to complete before finishing the test.
         test_timeout_s: The amount of time to run the test for before reporting results.
         num_concurrent_requests: The number of concurrent requests to make. Increase
@@ -318,6 +325,7 @@ def run_token_benchmark(
 
     summary, individual_responses = get_token_throughput_latencies(
         model=model,
+        tokenizer_name_or_path=tokenizer_name_or_path,
         llm_api=llm_api,
         test_timeout_s=test_timeout_s,
         max_num_completed_requests=max_num_completed_requests,
@@ -368,6 +376,9 @@ def run_token_benchmark(
 args.add_argument(
     "--model", type=str, required=True, help="The model to use for this load test."
 )
+args.add_argument(
+    "--tokenizer", type=str, required=False, default="", help="The tokenizer to use for this load test."
+)
 args.add_argument(
     "--mean-input-tokens",
     type=int,
@@ -478,6 +489,7 @@ def run_token_benchmark(
     run_token_benchmark(
         llm_api=args.llm_api,
         model=args.model,
+        tokenizer_name_or_path=args.tokenizer,
         test_timeout_s=args.timeout,
         max_num_completed_requests=args.max_num_completed_requests,
         mean_input_tokens=args.mean_input_tokens,