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README.md

@@ -17,9 +17,11 @@ tags:
 
 Llama 3 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 w4a16 (4-bit weights and 16-bit activations) and part of the model is quantized to w8a16 (8-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-Quantized's latency.
 
-This is based on the implementation of Llama-v3-8B-Chat found
-[here]({source_repo}). More details on model performance
-accross various devices, can be found [here](https://aihub.qualcomm.com/models/llama_v3_8b_chat_quantized).
+This model is an implementation of Llama-v3-8B-Chat found [here]({source_repo}).
+This repository provides scripts to run Llama-v3-8B-Chat on Qualcomm® devices.
+More details on model performance across various devices, can be found
+[here](https://aihub.qualcomm.com/models/llama_v3_8b_chat_quantized).
+
 
 ### Model Details
 
@@ -38,14 +40,6 @@ accross various devices, can be found [here](https://aihub.qualcomm.com/models/l
   - Token generator output: 1 output token + KV cache outputs
   - Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
 
-| Model | Device | Chipset | Target Runtime | Response Rate (Tokens/Second) | Time To First Token (TTFT) Range (Seconds) | Evaluation | 
-|---|---|---|---|---|---|---|
-| Llama-v3-8B-Chat | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 66.14 | (0.028, 0.92) | -- | -- |
-| Llama-v3-8B-Chat | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 66.14 | (0.028, 0.92) | -- | -- |
-| Llama-v3-8B-Chat | Samsung Galaxy S23 Ultra | Snapdragon® 8 Gen 2 | QNN | 66.14 | (0.028, 0.92) | -- | -- |
-| Llama-v3-8B-Chat | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 66.14 | (0.028, 0.92) | -- | -- |
-| Llama-v3-8B-Chat | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 66.14 | (0.028, 0.92) | -- | -- |
-
 ## Deploying Llama 3 on-device
   Please follow [this tutorial](https://github.com/quic/ai-hub-apps/tree/main/tutorials/llama)
   to compile QNN binaries and generate bundle assets to run [ChatApp on Windows](https://github.com/quic/ai-hub-apps/tree/main/apps/windows/cpp/ChatApp) and on Android powered by QNN-Genie.
@@ -73,6 +67,90 @@ Response: Superposition is a fundamental concept in quantum mechanics, which is
 
 
 
+| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+| ---|---|---|---|---|---|---|---|
+
+
+
+## Installation
+
+This model can be installed as a Python package via pip.
+
+```bash
+pip install "qai-hub-models[llama_v3_8b_chat_quantized]"
+```
+
+
+
+## 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_v3_8b_chat_quantized.demo
+```
+
+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_v3_8b_chat_quantized.demo
+```
+
+
+### Run model on a cloud-hosted device
+
+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_v3_8b_chat_quantized.export
+```
+
+
+
+
+
+
+## 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-v3-8B-Chat's performance across various devices [here](https://aihub.qualcomm.com/models/llama_v3_8b_chat_quantized).
+Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
+
+
 ## License
 * The license for the original implementation of Llama-v3-8B-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)
@@ -86,26 +164,7 @@ Response: Superposition is a fundamental concept in quantum mechanics, which is
 
 
 ## Community
-* 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.
+* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
 * For questions or feedback please [reach out to us](mailto:[email protected]).
 
-## 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
+