diff --git a/examples/models/llama2/eval_llama_lib.py b/examples/models/llama2/eval_llama_lib.py
index c9650531705..8bb653739b3 100644
--- a/examples/models/llama2/eval_llama_lib.py
+++ b/examples/models/llama2/eval_llama_lib.py
@@ -6,16 +6,22 @@
 
 
 import argparse
-from typing import Optional
+
+from typing import Optional, Union
 
 import lm_eval
 import torch
 
+from executorch.examples.models.llama2.tokenizer.tiktoken import Tokenizer as Tiktoken
+from executorch.examples.models.llama2.tokenizer.tokenizer import (
+    Tokenizer as SentencePieceTokenizer,
+)
+
 from lm_eval.api.model import LM
 from lm_eval.evaluator import evaluate
 from lm_eval.models.huggingface import HFLM as eval_wrapper
 from lm_eval.tasks import get_task_dict
-from sentencepiece import SentencePieceProcessor
+
 from torch import nn
 
 from .builder import LlamaEdgeManager
@@ -33,7 +39,7 @@ class GPTFastEvalWrapper(eval_wrapper):
     def __init__(
         self,
         model: nn.Module,
-        tokenizer: SentencePieceProcessor,
+        tokenizer: Union[SentencePieceTokenizer, Tiktoken],
         max_seq_length: Optional[int] = None,
     ):
         super().__init__()
@@ -46,7 +52,7 @@ def __init__(
 
     @property
     def eot_token_id(self):
-        return self._tokenizer.eos_id()
+        return self._tokenizer.eos_id
 
     @property
     def max_length(self):
@@ -65,7 +71,7 @@ def device(self):
         return self._device
 
     def tok_encode(self, string: str, **kwargs):
-        tokens = [self._tokenizer.bos_id()] + self._tokenizer.encode(string)
+        tokens = self._tokenizer.encode(string, bos=True, eos=False)
         encoded = torch.tensor(tokens, dtype=torch.int, device=self.device)
         # encoded is a pytorch tensor, but some internal logic in the
         # eval harness expects it to be a list instead
@@ -93,7 +99,7 @@ class ETEagerEvalWrapper(GPTFastEvalWrapper):
     def __init__(
         self,
         model: str,
-        tokenizer: SentencePieceProcessor,
+        tokenizer: Union[SentencePieceTokenizer, Tiktoken],
         max_seq_length: Optional[int] = None,
     ):
         super().__init__(None, tokenizer, max_seq_length)
@@ -120,7 +126,7 @@ class ETRunnerEvalWrapper(GPTFastEvalWrapper):
     def __init__(
         self,
         model: str,
-        tokenizer: SentencePieceProcessor,
+        tokenizer: Union[SentencePieceTokenizer, Tiktoken],
         tokenizer_bin: str,
         max_seq_length: Optional[int] = None,
     ):
@@ -183,7 +189,11 @@ def gen_eval_wrapper(
     Returns:
         eval_wrapper (LM): A wrapper interface for the lm-evaluation-harness library.
     """
-    tokenizer = SentencePieceProcessor(model_file=str(args.tokenizer_path))
+    try:
+        tokenizer = SentencePieceTokenizer(model_path=str(args.tokenizer_path))
+    except Exception:
+        print("Using Tiktokenizer")
+        tokenizer = Tiktoken(model_path=str(args.tokenizer_path))
 
     # ExecuTorch Binary Evaluation
     if (model := args.pte) is not None: