diff --git a/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md b/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md
index 41073848cda..c01d878fd65 100644
--- a/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md
+++ b/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md
@@ -71,8 +71,8 @@ cmake --build cmake-out -j16 --target install --config Release
 
 
 
-### Setup Llama Runner  
-Next we need to build and compile the Llama runner. This is similar to the requirements for running Llama with XNNPack. 
+### Setup Llama Runner
+Next we need to build and compile the Llama runner. This is similar to the requirements for running Llama with XNNPACK.
 ```
 sh examples/models/llama2/install_requirements.sh
 
@@ -130,9 +130,9 @@ You may also wonder what the "--metadata" flag is doing. This flag helps export
 
 Convert tokenizer for Llama 2
 ```
-python -m extension.llm.tokenizer.tokenizer -t <tokenizer.model> -o tokenizer.bin
+python -m extension.llm.tokenizer.tokenizer -t tokenizer.model -o tokenizer.bin
 ```
-Convert tokenizer for Llama 3 - Rename tokenizer.model to tokenizer.bin.
+Rename tokenizer for Llama 3 with command: `mv tokenizer.model tokenizer.bin`. We are updating the demo app to support tokenizer in original format directly.
 
 
 ### Export with Spinquant (Llama 3 8B only)
diff --git a/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md b/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md
index 902f9724841..536e202d334 100644
--- a/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md
+++ b/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md
@@ -1,10 +1,11 @@
-# Building ExecuTorch Android Demo App for Llama running XNNPack
+# Building ExecuTorch Android Demo App for Llama/Llava running XNNPACK
 
-**[UPDATE - 09/25]** We have added support for running [Llama 3.2 models](#for-llama-32-1b-and-3b-models) on the XNNPack backend. We currently support inference on their original data type (BFloat16). We have also added instructions to run [Llama Guard 1B models](#for-llama-guard-1b-models) on-device.
+**[UPDATE - 09/25]** We have added support for running [Llama 3.2 models](#for-llama-32-1b-and-3b-models) on the XNNPACK backend. We currently support inference on their original data type (BFloat16). We have also added instructions to run [Llama Guard 1B models](#for-llama-guard-1b-models) on-device.
+
+This tutorial covers the end to end workflow for building an android demo app using CPU on device via XNNPACK framework.
 
-This tutorial covers the end to end workflow for building an android demo app using CPU on device via XNNPack framework.
 More specifically, it covers:
-1. Export and quantization of Llama and Llava models against the XNNPack backend.
+1. Export and quantization of Llama and Llava models against the XNNPACK backend.
 2. Building and linking libraries that are required to inference on-device for Android platform.
 3. Building the Android demo app itself.
 
@@ -59,10 +60,10 @@ Optional: Use the --pybind flag to install with pybindings.
 In this demo app, we support text-only inference with up-to-date Llama models and image reasoning inference with LLaVA 1.5.
 
 ### For Llama 3.2 1B and 3B models
-We have supported BFloat16 as a data type on the XNNPack backend for Llama 3.2 1B/3B models.
+We have supported BFloat16 as a data type on the XNNPACK backend for Llama 3.2 1B/3B models.
 * You can request and download model weights for Llama through Meta official [website](https://llama.meta.com/).
 * For chat use-cases, download the instruct models instead of pretrained.
-* Run “examples/models/llama2/install_requirements.sh” to install dependencies.
+* Run `examples/models/llama2/install_requirements.sh` to install dependencies.
 * The 1B model in BFloat16 format can run on mobile devices with 8GB RAM. The 3B model will require 12GB+ RAM.
 * Export Llama model and generate .pte file as below:
 
@@ -70,7 +71,7 @@ We have supported BFloat16 as a data type on the XNNPack backend for Llama 3.2 1
 python -m examples.models.llama2.export_llama --checkpoint <checkpoint.pth> --params <params.json> -kv -X -d bf16 --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' --output_name="llama3_2.pte"
 ```
 
-* Convert tokenizer for Llama 3.2 - Rename 'tokenizer.model' to 'tokenizer.bin'.
+* Rename tokenizer for Llama 3.2 with command: `mv tokenizer.model tokenizer.bin`. We are updating the demo app to support tokenizer in original format directly.
 
 For more detail using Llama 3.2 lightweight models including prompt template, please go to our official [website](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_2#-llama-3.2-lightweight-models-(1b/3b)-).
 
@@ -89,7 +90,7 @@ python -m examples.models.llama2.export_llama --checkpoint <pruned llama guard 1
 
 
 ### For Llama 3.1 and Llama 2 models
-* You can download original model weights for Llama through Meta official [website](https://llama.meta.com/), or via Huggingface ([Llama 3.1 8B Instruction](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct))
+* You can download original model weights for Llama through Meta official [website](https://llama.meta.com/).
 * For Llama 2 models, Edit params.json file. Replace "vocab_size": -1 with "vocab_size": 32000. This is a short-term workaround
 * Run `examples/models/llama2/install_requirements.sh` to install dependencies.
 * The Llama 3.1 and Llama 2 models (8B and 7B) can run on devices with 12GB+ RAM.
@@ -103,9 +104,9 @@ You may wonder what the ‘--metadata’ flag is doing. This flag helps export t
 
 * Convert tokenizer for Llama 2
 ```
-python -m extension.llm.tokenizer.tokenizer -t <tokenizer.model> -o tokenizer.bin
+python -m extension.llm.tokenizer.tokenizer -t tokenizer.model -o tokenizer.bin
 ```
-* Convert tokenizer for Llama 3 - Rename `tokenizer.model` to `tokenizer.bin`.
+* Rename tokenizer for Llama 3.1 with command: `mv tokenizer.model tokenizer.bin`. We are updating the demo app to support tokenizer in original format directly.
 
 
 ### For LLaVA model