Upload README.md with huggingface_hub

README.md

@@ -15,7 +15,7 @@ tags:
 # Llama-v2-7B-Chat: Optimized for Mobile Deployment
 ## State-of-the-art large language model useful on a variety of language understanding and generation tasks
 
-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.
+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.
@@ -28,17 +28,18 @@ More details on model performance across various devices, can be found
 - **Model Type:** Text generation
 - **Model Stats:**
   - Number of parameters: 7B
-  - Model size: 3.6GB
+  - 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
   - Model-2 (Token Generator): Llama-TokenGenerator-KVCache-Quantized
+  - 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.
-  - QNN-SDK: 2.19
 
 ## Deploying Llama 2 on-device
 
@@ -61,14 +62,46 @@ Here, we divide the model into 4 parts in order to
 
 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
+2. If you don't have enough memory, export.py will dump instructions to increase swap space accordingly.
+
+## Sample output prompts generated on-device
+1. --prompt "what is gravity?" --max-output-tokens 30
+~~~
+-------- Response Summary --------
+Prompt: what is gravity?
+Response: Hello! I'm here to help you answer your question. Gravity is a fundamental force of nature that affects the behavior of objects with mass
+~~~
+
+2. --prompt "what is 2+3?" --max-output-tokens 30
+~~~
+-------- Response Summary --------
+Prompt: what is 2+3?
+Response: Of course! I'm happy to help! The answer to 2+3 is 5.
+~~~
+
+3. --prompt "could you please write code for fibonacci series in python?" --max-output-tokens 100
+~~~
+-------- Response Summary --------
+Prompt: could you please write code for fibonacci series in python?
+Response: Of course! Here is an example of how you could implement the Fibonacci sequence in Python:
+```
+def fibonacci(n):
+    if n <= 1:
+        return n
+    else:
+        return fibonacci(n-1) + fibonacci(n-2)
+```
+You can test the function by calling it with different values of `n`, like this:
+```
+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 | 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 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 90.268 ms | 64 - 4351 MB | UINT16 | NPU | Llama2-TokenGenerator-KVCache-Quantized 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1917.811 ms | 0 - 1028 MB | UINT16 | NPU | Llama2-PromptProcessor-Quantized 
 
 
 
@@ -128,14 +161,14 @@ python -m qai_hub_models.models.llama_v2_7b_chat_quantized.export
 ```
 
 ```
-Profile Job summary of Llama-TokenGenerator-KVCache-Quantized
+Profile Job summary of Llama2-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
+Profile Job summary of Llama2-PromptProcessor-Quantized
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
 Estimated Inference Time: 2302.57 ms