# Run Large Language Model on Intel NPU In this directory, you will find examples on how you could apply IPEX-LLM INT4 or INT8 optimizations on LLM models on [Intel NPUs](../../../README.md). See the table blow for verified models. ## Verified Models | Model | Model Link | |------------|----------------------------------------------------------------| | Llama2 | [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) | | 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) | | 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) | | Baichuan2 | [baichuan-inc/Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan-7B-Chat) | | Deepseek | [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) | | Mistral | [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | ## 0. Requirements To run these examples with IPEX-LLM on Intel NPUs, make sure to install the newest driver version of Intel NPU. Go to https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html to download and unzip the driver. 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 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 Win+R and input `conhost`, then press Enter to launch `conhost`. > - Run following command to use conda in `conhost`. Replace `` with your conda install location. > ``` > call \Scripts\activate > ``` **Following envrionment variables are required**: ```cmd set BIGDL_USE_NPU=1 ``` ### 3. Running examples ``` 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). - `--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 #### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) ```log Inference time: xxxx s -------------------- Output -------------------- 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. But her parents were always telling her to stay at home and be careful. They were worried about her safety, and they didn't want her to -------------------------------------------------------------------------------- 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 Win+R and input `conhost`, then press Enter to launch `conhost`. > - Run following command to use conda in `conhost`. Replace `` with your conda install location. > ``` > call \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 # to run Qwen2-1.5B-Instruct python qwen2.py ``` Arguments info: - `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2 model (i.e. `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'`. - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `What is AI?`. - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`. - `--max-output-len MAX_OUTPUT_LEN`: Defines the maximum sequence length for both input and output tokens. It is default to be `1024`. - `--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 If you encounter output problem, please try to disable the optimization of transposing value cache with following command: ```bash # to run Llama-2-7b-chat-hf python  llama2.py --disable-transpose-value-cache # to run Qwen2-1.5B-Instruct python qwen2.py --disable-transpose-value-cache ``` #### Sample Output #### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) ```log Inference time: xxxx s -------------------- Input -------------------- [INST] <> <> What is AI? [/INST] -------------------- Output -------------------- [INST] <> <> What is AI? [/INST] AI (Artificial Intelligence) is a field of computer science and engineering that focuses on the development of intelligent machines that can perform tasks ```