ipex-llm/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models

Run HuggingFace transformers Models with Pipeline Optimization on Intel NPU

In this directory, you will find examples on how to directly run HuggingFace transformers models with pipeline optimization on Intel NPUs. See the table blow for verified models.

Verified Models

Model	Model Link
Llama2	meta-llama/Llama-2-7b-chat-hf
Llama3	meta-llama/Meta-Llama-3-8B-Instruct
Baichuan2	baichuan-inc/Baichuan2-7B-Chat

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 -> Browse my computer for drivers. And then manually select the unzipped driver folder to install.

1. Install

1.1 Installation on Windows

We suggest using conda to manage environment:

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

Following envrionment variables are required:

set BIGDL_USE_NPU=1

3. Run Models

In the example 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.

:: to run Llama-2-7b-chat-hf
python llama2.py

:: to run Meta-Llama-3-8B-Instruct
python llama3.py

:: to run Baichuan2-7B-Chat
python baichuan2.py

Arguments info:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the model (e.g. meta-llama/Llama-2-7b-chat-hf) to be downloaded, or the path to the huggingface checkpoint folder.
• --prompt PROMPT: argument defining the prompt to be infered. 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.

Sample Output

meta-llama/Llama-2-7b-chat-hf

 Number of input tokens: 28
 Generated tokens: 32
 First token generation time: xxxx s
 Generation average latency: xxxx ms, (xxxx token/s)
 Generation time: xxxx s

Inference time: xxxx s
-------------------- Input --------------------
<s><s> [INST] <<SYS>>

<</SYS>>

What is AI? [/INST]
-------------------- Output --------------------
<s><s> [INST] <<SYS>>

<</SYS>>

What is AI? [/INST]  AI (Artificial Intelligence) is a field of computer science and technology that focuses on the development of intelligent machines that can perform