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README.md

@@ -17,20 +17,19 @@ 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 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-KVCache-Quantized's latency.
 
-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).
-
+This is based on the implementation of Llama-v2-7B-Chat found
+[here]({source_repo}). More details on model performance
+accross various devices, can be found [here](https://aihub.qualcomm.com/models/llama_v2_7b_chat_quantized).
 
 ### Model Details
 
 - **Model Type:** Text generation
 - **Model Stats:**
+  - Input sequence length for Prompt Processor: 1024
+  - Context length: 1024
   - Number of parameters: 7B
   - Precision: w4a16 + w8a16 (few layers)
   - Model-1 (Prompt Processor): Llama-PromptProcessor-Quantized
-  - Max context length: 1024
   - Prompt processor model size: 3.6 GB
   - Prompt processor input: 1024 tokens
   - Prompt processor output: 1024 output tokens + KVCache for token generator
@@ -38,31 +37,22 @@ More details on model performance across various devices, can be found
   - Token generator model size: 3.6 GB
   - Token generator input: 1 input token + past KVCache
   - Token generator output: 1 output token + KVCache for next iteration
-  - Decoding length: 1024 (1 output token + 1023 from KVCache)
   - Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
+  - Minimum QNN SDK version required: 2.27.0
+  - Supported languages: English.
+  - TTFT: Time To First Token is the time it takes to generate the first response token. This is expressed as a range because it varies based on the length of the prompt. For Llama-v2-7B-Chat, both values in the range are the same since prompt length is the full context length (1024 tokens).
+  - Response Rate: Rate of response generation after the first response token.
+
+| Model | Device | Chipset | Target Runtime | Response Rate (tokens per second) | Time To First Token (range, seconds)
+|---|---|---|---|---|---|
+| Llama-v2-7B-Chat | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 12.85 | 1.49583 - 1.49583 | -- | -- |
+| Llama-v2-7B-Chat | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 11.2 | 1.9189999999999998 - 1.9189999999999998 | -- | -- |
+| Llama-v2-7B-Chat | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 11.2 | 1.9189999999999998 - 1.9189999999999998 | -- | -- |
+| Llama-v2-7B-Chat | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 17.94 | 1.44 - 1.44 | -- | -- |
 
 ## 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:
-1. Model size is too large to fit in device memory for inference
-2. Multi-Head Attention (MHA) has large activations leading to fallback from accelerators
-3. High model load and inference time
-
-We can tackle the above constraints with the following steps:
-1. Quantize weights to reduce on-disk model size, e.g., int8 or int4 weights
-2. Quantize activations to reduce inference time memory pressure
-3. Graph transformations to reduce inference time memory pressure, e.g., Multi-Head to Split-Head Attention (MHA -> SHA)
-4. Graph transformations to convert or decompose operations into more accelerator friendly operations e.g. Linear to Conv
-5. For LLM with 7B or more parameters, above steps are still not good enough on mobile,
-  hence we go one step further and split model into sub-parts.
-
-Here, we divide the model into 4 parts in order to
-1. Make model exportable with low memory usage
-2. Avoid inference time out-of-memory errors
-
-In order to export Llama 2, please ensure
-1. Host machine has >40GB memory (RAM+swap-space)
-2. If you don't have enough memory, export.py will dump instructions to increase swap space accordingly.
+Please follow the [LLM on-device deployment](https://github.com/quic/ai-hub-apps/tree/main/tutorials/llm_on_genie) tutorial.
 
 ## Sample output prompts generated on-device
 1. --prompt "what is gravity?" --max-output-tokens 30
@@ -98,119 +88,39 @@ print(fibonacci(5))
 
 
 
-| 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 | 97.732 ms | 71 - 72 MB | UINT16 | NPU | Llama2-TokenGenerator-KVCache-Quantized 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 2020.745 ms | 11 - 12 MB | UINT16 | NPU | Llama2-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]"
-```
-
-
-
-## 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
-```
-
-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
-
-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
-```
-
-```
-Profile Job summary of Llama2-TokenGenerator-KVCache-Quantized
---------------------------------------------------
-Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 95.96 ms
-Estimated Peak Memory Range: 65.07-65.07 MB
-Compute Units: NPU (33818) | Total (33818)
-
-Profile Job summary of Llama2-PromptProcessor-Quantized
---------------------------------------------------
-Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 1889.09 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.
-
+## 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)
 
-## 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)
 * [Source Model Implementation](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
 
+
+
 ## Community
-* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
+* 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:[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