Update NPU example readme (#11931)

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md

@@ -9,7 +9,7 @@ In this directory, you will find examples on how you could apply IPEX-LLM INT4 o
 | Llama3 | [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) |
 | Chatglm3 | [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b) |
 | Chatglm2 | [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b) |
-| Qwen2 | [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) |
+| Qwen2 | [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct), [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) |
 | MiniCPM | [openbmb/MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16) |
 | Phi-3 | [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) |
 | Stablelm | [stabilityai/stablelm-zephyr-3b](https://huggingface.co/stabilityai/stablelm-zephyr-3b) |
@@ -23,10 +23,8 @@ Go to https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-w
 Then go to **Device Manager**, find **Neural Processors** -> **Intel(R) AI Boost**.
 Right click and select **Update Driver**. And then manually select the folder unzipped from the driver.
 
-## Example 1: Predict Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a Llama2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel NPUs.
-### 1. Install
-#### 1.1 Installation on Windows
+## 1. Install
+### 1.1 Installation on Windows
 We suggest using conda to manage environment:
 ```bash
 conda create -n llm python=3.10
@@ -36,9 +34,9 @@ conda activate llm
 pip install --pre --upgrade ipex-llm[npu]
 ```
 
-### 2. Runtime Configurations
+## 2. Runtime Configurations
 For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 2.1 Configurations for Windows
+### 2.1 Configurations for Windows
 
 > [!NOTE]
 > For optimal performance, we recommend running code in `conhost` rather than Windows Terminal:
@@ -54,19 +52,20 @@ For optimal performance, it is recommended to set several environment variables.
 set BIGDL_USE_NPU=1
 ```
 
-### 3. Running examples
+## 3. Run models
+In the example [generate.py](./generate.py), we show a basic use case for a Llama2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel NPUs.
 
 ```
 python ./generate.py
 ```
 
 Arguments info:
-- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2 model (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`, and more verified models please see the list in [Verified Models](#verified-models).
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2 model (e.g. `meta-llama/Llama-2-7b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`, and more verified models please see the list in [Verified Models](#verified-models).
 - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun'`.
 - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
 - `--load_in_low_bit`: argument defining the `load_in_low_bit` format used. It is default to be `sym_int8`, `sym_int4` can also be used.
 
-#### Sample Output
+### Sample Output
 #### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
 
 ```log
@@ -77,48 +76,14 @@ Inference time: xxxx s
 done
 ```
 
-## Example 2: Predict Tokens using `generate()` API using multi processes
-In the example [llama2.py](./llama2.py) and [qwen2.py](./qwen2.py), we show an experimental support for a Llama2 / Qwen2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimization and fused decoderlayer optimization on Intel NPUs.
-
-> [!IMPORTANT]
-> To run Qwen2 and Llama2 with IPEX-LLM on Intel NPUs, we recommend using version **32.0.100.2540** for the Intel NPU.
-> 
-> Go to https://www.intel.com/content/www/us/en/download/794734/825735/intel-npu-driver-windows.html to download and unzip the driver. Then follow the same steps on [Requirements](#0-requirements).
-
-### 1. Install
-#### 1.1 Installation on Windows
-We suggest using conda to manage environment:
-```bash
-conda create -n llm python=3.10
-conda activate llm
-
-# install ipex-llm with 'npu' option
-pip install --pre --upgrade ipex-llm[npu]
-```
-
-### 2. Runtime Configurations
-For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
-#### 2.1 Configurations for Windows
-
-> [!NOTE]
-> For optimal performance, we recommend running code in `conhost` rather than Windows Terminal:
-> - Press <kbd>Win</kbd>+<kbd>R</kbd> and input `conhost`, then press Enter to launch `conhost`.
-> - Run following command to use conda in `conhost`. Replace `<your conda install location>` with your conda install location.
-> ```
-> call <your conda install location>\Scripts\activate
-> ```
-
-**Following envrionment variables are required**:
-
-```cmd
-set BIGDL_USE_NPU=1
-```
-
-### 3. Running examples
+## 4. Run Optimized Models (Experimental)
+The example below shows how to run the **_optimized model implementations_** on Intel NPU, including
+- [Llama2-7B](./llama2.py)
+- [Qwen2-1.5B](./qwen2.py)
 
 ```
 # to run Llama-2-7b-chat-hf
-python  llama2.py
+python llama2.py
 
 # to run Qwen2-1.5B-Instruct
 python qwen2.py
@@ -132,7 +97,7 @@ Arguments info:
 - `--max-prompt-len MAX_PROMPT_LEN`: Defines the maximum number of tokens that the input prompt can contain. It is default to be `512`.
 - `--disable-transpose-value-cache`: Disable the optimization of transposing value cache.
 
-### 4. Troubleshooting
+### Troubleshooting
 
 If you encounter output problem, please try to disable the optimization of transposing value cache with following command:
 ```bash
@@ -144,7 +109,7 @@ python qwen2.py --disable-transpose-value-cache
 ```
 
 
-#### Sample Output
+### Sample Output
 #### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
 
 ```log