diff --git a/recipes/inference/local_inference/inference.py b/recipes/inference/local_inference/inference.py
index 4f83c8f2caec1fbac1558a2182671fe5de67f24e..194adcf0e888d0c397bef93c7dbe7389b395fdd1 100644
--- a/recipes/inference/local_inference/inference.py
+++ b/recipes/inference/local_inference/inference.py
@@ -10,7 +10,7 @@ import time
 import gradio as gr
 
 import torch
-from transformers import LlamaTokenizer
+from transformers import AutoTokenizer
 
 from llama_recipes.inference.safety_utils import get_safety_checker, AgentType
 from llama_recipes.inference.model_utils import load_model, load_peft_model
@@ -69,17 +69,17 @@ def main(
     else:
         torch.cuda.manual_seed(seed)
     torch.manual_seed(seed)
-    
+
     model = load_model(model_name, quantization, use_fast_kernels)
     if peft_model:
         model = load_peft_model(model, peft_model)
 
     model.eval()
-    
 
-    tokenizer = LlamaTokenizer.from_pretrained(model_name)
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
     tokenizer.pad_token = tokenizer.eos_token
-    
+
     batch = tokenizer(user_prompt, padding='max_length', truncation=True, max_length=max_padding_length, return_tensors="pt")
     if is_xpu_available():
         batch = {k: v.to("xpu") for k, v in batch.items()}
diff --git a/src/llama_recipes/finetuning.py b/src/llama_recipes/finetuning.py
index d276857773152e4c5a577dcbd83c68cd7348d936..f7b3a2caddd19ebee274ec483b3e865695da834d 100644
--- a/src/llama_recipes/finetuning.py
+++ b/src/llama_recipes/finetuning.py
@@ -18,8 +18,8 @@ from torch.distributed.fsdp import (
 from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload
 from torch.optim.lr_scheduler import StepLR
 from transformers import (
+    AutoTokenizer,
     LlamaForCausalLM,
-    LlamaTokenizer,
     LlamaConfig,
 )
 from transformers.models.llama.modeling_llama import LlamaDecoderLayer
@@ -137,7 +137,7 @@ def main(**kwargs):
         )
 
     # Load the tokenizer and add special tokens
-    tokenizer = LlamaTokenizer.from_pretrained(train_config.model_name)
+    tokenizer = AutoTokenizer.from_pretrained(train_config.model_name)
     tokenizer.pad_token_id = tokenizer.eos_token_id
 
     print_model_size(model, train_config, rank if train_config.enable_fsdp else 0)