diff --git a/examples/models/llama2/README.md b/examples/models/llama2/README.md
index b996e89cce6..7284acfcec6 100644
--- a/examples/models/llama2/README.md
+++ b/examples/models/llama2/README.md
@@ -111,9 +111,11 @@ You can export and run the original Llama3 8B model.
 
 2. Export model and generate `.pte` file
     ```
-    python -m examples.models.llama2.export_llama --checkpoint <consolidated.00.pth> -p <params.json> -d=fp32 -X -qmode 8da4w -kv --use_sdpa_with_kv_cache --output_name="llama3_kv_sdpa_xnn_qe_4_32.pte" group_size 128 --metadata '{"get_bos_id":128000, "get_eos_id":128001}' --embedding-quantize 4,32
+    python -m examples.models.llama2.export_llama --checkpoint <consolidated.00.pth> -p <params.json> -kv --use_sdpa_with_kv_cache -X -qmode 8da4w  --group_size 128 -d fp32 --metadata '{"get_bos_id":128000, "get_eos_id":128001}' --embedding-quantize 4,32 --output_name="llama3_kv_sdpa_xnn_qe_4_32.pte"
     ```
 
+    Due to the larger vocabulary size of Llama3, we recommend quantizing the embeddings with `--embedding-quantize 4,32` to further reduce the model size.
+
 ## (Optional) Finetuning
 
 If you want to finetune your model based on a specific dataset, PyTorch provides [TorchTune](https://github.com/pytorch/torchtune) - a native-Pytorch library for easily authoring, fine-tuning and experimenting with LLMs.