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README.md

@@ -17,9 +17,11 @@ tags:
 
 Llama 2 is a family of LLMs. The "Chat" at the end indicates that the model is optimized for chatbot-like dialogue. The model is quantized to 4-bit weights and 16-bit activations making it suitable for on-device deployment. For Prompt and output length specified below, the time to first token is Llama-PromptProcessor-Quantized's latency and average time per addition token is Llama-TokenGenerator-KVCache-Quantized's latency.
 
-This is based on the implementation of Llama-v2-7B-Chat found
-[here](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf). More details on model performance
-accross various devices, can be found [here](https://aihub.qualcomm.com/models/llama_v2_7b_chat_quantized).
+This model is an implementation of Llama-v2-7B-Chat found [here](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf).
+This repository provides scripts to run Llama-v2-7B-Chat on Qualcomm® devices.
+More details on model performance across various devices, can be found
+[here](https://aihub.qualcomm.com/models/llama_v2_7b_chat_quantized).
+
 
 ### Model Details
 
@@ -38,12 +40,6 @@ accross various devices, can be found [here](https://aihub.qualcomm.com/models/l
   - Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
   - QNN-SDK: 2.19
 
-
-| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
-| ---|---|---|---|---|---|---|---|
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 104.953 ms | 316 - 4785 MB | UINT16 | NPU | Llama-TokenGenerator-KVCache-Quantized 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1917.811 ms | 0 - 1028 MB | UINT16 | NPU | Llama-PromptProcessor-Quantized 
-
 ## Deploying Llama 2 on-device
 
 Large Language Model (LLM) such as [Llama 2](https://llama.meta.com/llama2/) has the following complexities to deploy on-device:
@@ -68,39 +64,113 @@ In order to export Llama 2, please ensure
 2. If you don't have enough memory, export.py will dump instructions to increase swap space accordingly
 
 
-## Example & Usage
 
-Install the package via pip:
+| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+| ---|---|---|---|---|---|---|---|
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 104.953 ms | 316 - 4785 MB | UINT16 | NPU | Llama-TokenGenerator-KVCache-Quantized 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1917.811 ms | 0 - 1028 MB | UINT16 | NPU | Llama-PromptProcessor-Quantized 
+
+
+
+## Installation
+
+This model can be installed as a Python package via pip.
+
 ```bash
-pip install "qai_hub_models[llama_v2_7b_chat_quantized]"
+pip install "qai-hub-models[llama_v2_7b_chat_quantized]"
 ```
 
 
-Once installed, run the following simple CLI demo:
+
+## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
+
+Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
+Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
+
+With this API token, you can configure your client to run models on the cloud
+hosted devices.
+```bash
+qai-hub configure --api_token API_TOKEN
+```
+Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
+
+
+
+## Demo off target
+
+The package contains a simple end-to-end demo that downloads pre-trained
+weights and runs this model on a sample input.
 
 ```bash
 python -m qai_hub_models.models.llama_v2_7b_chat_quantized.demo
 ```
-More details on the CLI tool can be found with the `--help` option. See
-[demo.py](demo.py) for sample usage of the model including pre/post processing
-scripts. Please refer to our [general instructions on using
-models](../../../#getting-started) for more usage instructions.
 
-## Export for on-device deployment
+The above demo runs a reference implementation of pre-processing, model
+inference, and post processing.
+
+**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
+environment, please add the following to your cell (instead of the above).
+```
+%run -m qai_hub_models.models.llama_v2_7b_chat_quantized.demo
+```
+
+
+### Run model on a cloud-hosted device
 
-This repository contains export scripts that produce a model optimized for
-on-device deployment. This can be run as follows:
+In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
+device. This script does the following:
+* Performance check on-device on a cloud-hosted device
+* Downloads compiled assets that can be deployed on-device for Android.
+* Accuracy check between PyTorch and on-device outputs.
 
 ```bash
 python -m qai_hub_models.models.llama_v2_7b_chat_quantized.export
 ```
-Additional options are documented with the `--help` option. Note that the above
-script requires access to Deployment instructions for Qualcomm® AI Hub.
+
+```
+Profile Job summary of Llama-TokenGenerator-KVCache-Quantized
+--------------------------------------------------
+Device: Snapdragon X Elite CRD (11)
+Estimated Inference Time: 118.14 ms
+Estimated Peak Memory Range: 64.97-64.97 MB
+Compute Units: NPU (34842) | Total (34842)
+
+Profile Job summary of Llama-PromptProcessor-Quantized
+--------------------------------------------------
+Device: Snapdragon X Elite CRD (11)
+Estimated Inference Time: 2302.57 ms
+Estimated Peak Memory Range: 10.29-10.29 MB
+Compute Units: NPU (31766) | Total (31766)
+
+
+```
 
 
+
+
+
+## Deploying compiled model to Android
+
+
+The models can be deployed using multiple runtimes:
+- TensorFlow Lite (`.tflite` export): [This
+  tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
+  guide to deploy the .tflite model in an Android application.
+
+
+- QNN (`.so` export ): This [sample
+  app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
+provides instructions on how to use the `.so` shared library  in an Android application.
+
+
+## View on Qualcomm® AI Hub
+Get more details on Llama-v2-7B-Chat's performance across various devices [here](https://aihub.qualcomm.com/models/llama_v2_7b_chat_quantized).
+Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
+
 ## License
 - The license for the original implementation of Llama-v2-7B-Chat can be found
   [here](https://github.com/facebookresearch/llama/blob/main/LICENSE).
+- The license for the compiled assets for on-device deployment can be found [here](https://github.com/facebookresearch/llama/blob/main/LICENSE)
 
 ## References
 * [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
@@ -110,23 +180,4 @@ script requires access to Deployment instructions for Qualcomm® AI Hub.
 * Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI.
 * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
 
-## Usage and Limitations
-
-Model may not be used for or in connection with any of the following applications:
-
-- Accessing essential private and public services and benefits;
-- Administration of justice and democratic processes;
-- Assessing or recognizing the emotional state of a person;
-- Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics;
-- Education and vocational training;
-- Employment and workers management;
-- Exploitation of the vulnerabilities of persons resulting in harmful behavior;
-- General purpose social scoring;
-- Law enforcement;
-- Management and operation of critical infrastructure;
-- Migration, asylum and border control management;
-- Predictive policing;
-- Real-time remote biometric identification in public spaces;
-- Recommender systems of social media platforms;
-- Scraping of facial images (from the internet or otherwise); and/or
-- Subliminal manipulation
+