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Update NPU example readme #11931

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Update NPU example readme #11931

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61 changes: 12 additions & 49 deletions python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md
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Expand Up @@ -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) |
Expand All @@ -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
Expand All @@ -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:
Expand All @@ -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
Expand All @@ -77,45 +76,9 @@ Inference time: xxxx s
done
```

## Example 2: Predict Tokens using `generate()` API using multi processes
## 4. Run Optimized Models (Experimental)
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.
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@jason-dai jason-dai Aug 27, 2024 •
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The example below shows how to run the optimized model implementations on Intel NPU, including

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The example below shows how to run the optimized model implementations on Intel NPU, including

Have updated.


> [!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).
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Do we still need this notice?

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Do we still need this notice?

Our troubleshooting (https://github.com/intel-analytics/ipex-llm/blob/4fcffc40503bd92aad164bb2d4a9cbb69c66342d/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md#troubleshooting) involves transpose value setting workaround. And 32.0.100.2540 is mainly verified on MTL.


### 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

```
# to run Llama-2-7b-chat-hf
python  llama2.py
Expand All @@ -132,7 +95,7 @@ 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
Expand All @@ -144,7 +107,7 @@ 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
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