diff --git a/inference/huggingface/zero_inference/README.md b/inference/huggingface/zero_inference/README.md
index d76de89cd..ef78e3da1 100644
--- a/inference/huggingface/zero_inference/README.md
+++ b/inference/huggingface/zero_inference/README.md
@@ -172,4 +172,5 @@ In running example above, only two fully connected layers (fc1 and fc2) and the
 ## References
 
 - DeepSpeed [ZeRO-Inference](https://www.deepspeed.ai/2022/09/09/zero-inference.html)
+- Sheng, Ying et al. [FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU](https://arxiv.org/abs/2303.06865)
 - Shen, Sheng, et al. "Q-bert: Hessian based ultra low precision quantization of bert." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 05. 2020.
diff --git a/inference/huggingface/zero_inference/run_model.py b/inference/huggingface/zero_inference/run_model.py
index bc03e7499..5aa28fd7f 100644
--- a/inference/huggingface/zero_inference/run_model.py
+++ b/inference/huggingface/zero_inference/run_model.py
@@ -26,7 +26,7 @@
 from packaging import version
 
 
-assert version.parse(deepspeed.__version__) >= version.parse("0.10.2"), "ZeRO-Inference with weight quantization and kv cache offloading is available only in DeepSpeed 0.10.3+, please upgrade DeepSpeed"
+assert version.parse(deepspeed.__version__) >= version.parse("0.10.3"), "ZeRO-Inference with weight quantization and kv cache offloading is available only in DeepSpeed 0.10.3+, please upgrade DeepSpeed"
 
 def get_model_config(model_name):
     if "175b" in model_name:
@@ -161,11 +161,19 @@ def run_generation(
     return_token_type_ids = True 
     padding_side = "left" if config.model_type in ["opt"] else "right"
 
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_name, 
-        return_token_type_ids=return_token_type_ids,
-        padding_side=padding_side
-    )
+    if config.model_type == "opt":
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_name.replace("175b", "66b"), 
+            return_token_type_ids=return_token_type_ids,
+            padding_side=padding_side
+        )
+    else:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_name,
+            return_token_type_ids=return_token_type_ids,
+            padding_side=padding_side
+        )
+
 
     tokenizer.pad_token = tokenizer.eos_token