diff --git a/examples/README.md b/examples/README.md
index 3f6b5f0..9422813 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -11,6 +11,8 @@ To Execute `basic_usage.py`, using command like this:
 python basic_usage.py
 ```
 
+This script also showcases how to download/upload quantized model from/to 🤗 Hub, to enable those features, you can uncomment the commented codes.
+
 To Execute `basic_usage_with_wikitext2.py`, using command like this:
 ```shell
 python basic_usage_with_wikitext2.py
diff --git a/examples/quantization/basic_usage.py b/examples/quantization/basic_usage.py
index 99fc8e7..b8c459e 100644
--- a/examples/quantization/basic_usage.py
+++ b/examples/quantization/basic_usage.py
@@ -31,11 +31,28 @@ def main():
     # save quantized model
     model.save_quantized(quantized_model_dir)
 
+    # push quantized model to Hugging Face Hub.
+    # to use use_auth_token=True, Login first via huggingface-cli login.
+    # or pass explcit token with: use_auth_token="hf_xxxxxxx"
+    # (uncomment the following three lines to enable this feature)
+    # repo_id = f"YourUserName/{quantized_model_dir}"
+    # commit_message = f"AutoGPTQ model for {pretrained_model_dir}: {quantize_config.bits}bits, gr{quantize_config.group_size}, desc_act={quantize_config.desc_act}"
+    # model.push_to_hub(repo_id, commit_message=commit_message, use_auth_token=True)
+
+    # alternatively you can save and push at the same time
+    # (uncomment the following three lines to enable this feature)
+    # repo_id = f"YourUserName/{quantized_model_dir}"
+    # commit_message = f"AutoGPTQ model for {pretrained_model_dir}: {quantize_config.bits}bits, gr{quantize_config.group_size}, desc_act={quantize_config.desc_act}"
+    # model.push_to_hub(repo_id, save_dir=quantized_model_dir, use_safetensors=True, commit_message=commit_message, use_auth_token=True)
+
     # save quantized model using safetensors
     model.save_quantized(quantized_model_dir, use_safetensors=True)
 
     # load quantized model to the first GPU
-    model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir)
+    model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir, device="cuda:0")
+
+    # download quantized model from Hugging Face Hub and load to the first GPU
+    # model = AutoGPTQForCausalLM.from_quantized(repo_id, device="cuda:0", use_safetensors=True, use_triton=False)
 
     # inference with model.generate
     print(tokenizer.decode(model.generate(**tokenizer("auto_gptq is", return_tensors="pt").to(model.device))[0]))