qwen2

Qwen2

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Qwen2 models. For illustration purposes, we utilize the Qwen/Qwen2-7B-Instruct as a reference Qwen2 model.

0. Requirements

To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to here for more information.

Example: Predict Tokens using generate() API

In the example generate.py, we show a basic use case for a Qwen model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations.

1. Install

We suggest using conda to manage environment:

On Linux:

conda create -n llm python=3.11
conda activate llm

# install ipex-llm with 'all' option
pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
pip install transformers==4.37.0 # install the transformers which support Qwen2

On Windows:

conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[all]
pip install transformers==4.37.0

2. Run

python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT

Arguments info:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the Qwen model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'Qwen/Qwen2-7B-Instruct'.
• --prompt PROMPT: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be 'AI是什么?'.
• --n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be 32.

Note: When loading the model in 4-bit, IPEX-LLM converts linear layers in the model into INT4 format. In theory, a XB model saved in 16-bit will requires approximately 2X GB of memory for loading, and ~0.5X GB memory for further inference.

Please select the appropriate size of the Qwen model based on the capabilities of your machine.

2.1 Client

On client Windows machine, it is recommended to run directly with full utilization of all cores:

python ./generate.py 

2.2 Server

For optimal performance on server, it is recommended to set several environment variables (refer to here for more information), and run the example with all the physical cores of a single socket.

E.g. on Linux,

# set IPEX-LLM env variables
source ipex-llm-init

# e.g. for a server with 48 cores per socket
export OMP_NUM_THREADS=48
numactl -C 0-47 -m 0 python ./generate.py

2.3 Sample Output

Qwen/Qwen2-7B-Instruct

Inference time: xxxx s
-------------------- Prompt --------------------
AI是什么？
-------------------- Output --------------------
AI，即人工智能（Artificial Intelligence），是一门研究、开发用于模拟、延伸和扩展人类智能的理论、方法、技术及应用系统的学科

Inference time: xxxx s
-------------------- Prompt --------------------
What is AI?
-------------------- Output --------------------
AI stands for Artificial Intelligence, which is the simulation of human intelligence in machines that are programmed to think and learn like humans and mimic their actions. The term may